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Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.

IntroductionBioactive peptides based on foodstuffs are of particular interest as carriers for calcium delivery due to their safety and high activity. The phosphorylated peptide has been shown to enhance calcium absorption and bone formation.MethodA novel complex of peptide phosphorylation modification derived from soybean protein was introduced, and the mechanism, stability, and osteogenic differentiation bioactivity of the peptide with or without calcium were studied.ResultThe calcium-binding capacity of phosphorylated soy peptide (SPP) reached 50.24 ± 0.20 mg/g. The result of computer stimulation and vibration spectrum showed that SPP could chelate with calcium by the phosphoric acid group, carboxyl oxygen of C-terminal Glu, Asp, and Arg, and phosphoric acid group of Ser on the SPP at a stoichiometric ratio of 1:1, resulting in the formation of the complex of ligand and peptide. Thermal stability showed that chelation enhanced peptide stability compared with SPP alone. Additionally, in vitro results showed that SPP-Ca could facilitate osteogenic proliferation and differentiation ability.DiscussionSPP may function as a promising alternative to current therapeutic agents for bone loss.

Deep learning for prediction of clear cell renal cell carcinoma outcome. The model was built around multiple instance learning and comprised a MobileNet V3 representation network, a Noisy-AND pooling function, and a fully connected classification network similar to the one used by Skrede et al.

Terahertz (THz) band owning the abundant multi-ten-GHz bandwidth is capable to support Terabit-per-second wireless communications, which is a pillar technology for 6G and beyond systems. With sub-millimeter-long antennas, ultra-massive (UM) MIMO and intelligent surface (IS) systems with thousands of array elements are exploited to effectively combat the distance limitation and blockage problems, which compose a promising THz ultra-large antenna array (ULAA) system. As a combined effect of wavelength and array aperture, the resulting coverage of THz systems ranges from near-field to far-field, leading to a new paradigm of cross-field communications. Although channel models, communications theories, and networking strategies have been studied for far-field and near-field separately, the unified design of cross-field communications that achieve high spectral efficiency and low complexity is still missing. In this article, the challenges and features of THz ULAA cross-field communications are investigated. Furthermore, cross-field solutions in three perspectives are presented, including a hybrid spherical- and planar-wave channel model, cross-field channel estimation, and widely-spaced multi-subarray hybrid beamforming, where a subarray as a basic unit in THz ULAA systems is exploited. The approximation error of channel modeling accuracy, spectral efficiency, and estimation error of these designs are numerically evaluated. Finally, as a roadmap of THz ULAA cross-field communications, multiple open problems and potential research directions are elaborated.

In this paper we study equivariant moduli spaces of sheaves on a $ K3 $ surface $ X $ under a symplectic action of a finite group. We prove that under some mild conditions, equivariant moduli spaces of sheaves on $ X $ are irreducible symplectic manifolds deformation equivalent to Hilbert schemes of points on $ X $ via a connection between Gieseker and Bridgeland moduli spaces, as well as the derived McKay correspondence., Comment: This paper is part of the author's PhD thesis [arXiv:2204.09824]

Additional file 2. Original images of Western blotting results.

Due to the complicated background and noise of infrared images, infrared small target detection is one of the most difficult problems in the field of computer vision. In most existing studies, semantic segmentation methods are typically used to achieve better results. The centroid of each target is calculated from the segmentation map as the detection result. In contrast, we propose a novel end-to-end framework for infrared small target detection and segmentation in this paper. First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head. Then, a pyramid pool module is used to further extract features and improve the precision of target segmentation. Next, we use semantic segmentation tasks that pay more attention to pixel-level features to assist in the training process of object detection, which increases the average precision and allows the model to detect some targets that were previously not detectable. Furthermore, we develop a multi-task framework for infrared small target detection and segmentation. Our multi-task learning model reduces complexity by nearly half and speeds up inference by nearly twice compared to the composite single-task model, while maintaining accuracy. The code and models are publicly available at https://github.com/Chenastron/MTUNet.

The resilient cooperative source seeking problem of double-integrator multi-robot systems under $\mathbf {d}$ -local deception attacks is investigated. In this article, we first propose a resilient cooperative control algorithm and present a necessary and sufficient condition for its achievement of resilient consensus. Then, through applying the particle swarm optimization algorithm to the resilient cooperative control algorithm, a resilient cooperative source seeking algorithm is proposed. We show that this algorithm can solve the source seeking problems under $\mathbf {d}$ -local deception attacks with an additional potential of addressing the multiple local extrema case. Finally, the feasibility of the proposed algorithms is illustrated by simulation and experimental results.

With the development of the cloud platform and power Internet of Things, how to effectively connect the physical world and the digital world, realize the interconnection of all things in power terminals, deeply integrate data, and tap the economic value of data is an important issue. Based on the company’s business and technology development status, this paper proposes the power Internet of Things data fusion based on cloud and NFV, discusses data storage, circulation and management methods in the power Internet of Things cloud platform environment, and completes accurate and efficient data analysis. Responding to the access of a large number of distributed energy sources, multiple business concurrency and joint data analysis needs, and improve data analysis and sharing capabilities.

Purpose Autologous bone grafts are the gold standard for treating bone defects. However, limited bone supply and morbidity at the donor site restrict its extensive use. Therefore, developing bone graft materials as an alternative to autologous grafts has gained considerable attention. Injectable hydrogels endowed with osteogenic potential have the ability to fill irregular bone defects using minimally invasive procedures and have thus been attracting researchers’ attention. However, from a clinical perspective, most fabrication methods employed for the current injectable osteogenic hydrogels are difficult and inconvenient. In the current study, we fabricated an injectable osteogenic hydrogel using a simple and convenient strategy. Materials and Methods Gelatin-methacryloyl (GelMA) pre-polymer was synthetized. Nano silicate (SN) and stromal cell-derived factor-1 alpha (SDF-1α) were introduced into the pre-polymer to achieve injectability, controlled release property, excellent osteogenic ability, and efficient stem cell homing. Results The GelMA-SN-SDF-1α demonstrated excellent injectability via a 17-G needle at room temperature. The loaded SDF-1α exhibited a long-term controlled release pattern and efficiently stimulated MSC migration and homing. The GelMA-SN-SDF-1α hydrogel amplified cell spreading, migration, osteogenic-related biomarker expression, and matrix mineralization. The GelMA-SN-SDF-1α hydrogel filled critical-sized calvaria defects in rats and demonstrated excellent bone regeneration ability, as assessed using micro-CT scanning and histomorphometric staining. Conclusion The GelMA-SN-SDF-1α hydrogel provides a simple and convenient strategy for the fabrication of injectable osteogenic graft materials., Video abstract Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://youtu.be/FhyefSKUa34

Oblique water entry of a rigid paraboloid is investigated by fully nonlinear boundary element method. The deadrise angle of the paraboloid increases gradually from zero at the bottom tip and a slenderness coefficient is introduced to symbolize the shape of the paraboloid. Convergence study with respect to time step and element size is carried on to assure the numerical procedure. The free surface elevation and pressure distribution are depicted in time domain. The present solution is compared with the linearized Wagner method and the composite solution with spray jets to investigate the influence of exact fully nonlinear boundary conditions. As there is no sharp edge or tip around the paraboloid, the water flows over the body both from lateral side and down side in oblique entry, the stagnation point relative to the body moves towards the flow-coming direction, and the negative pressure can be observed on both lateral side and down side of the paraboloid. The horizontal speed has great influence on horizontal total force and little influence on vertical total force, while the effect of the slenderness coefficient just has the opposite effect.

Salivary amylase, encoded by the AMY1 gene, is responsible for the digestion of carbohydrates. We investigated associations of AMY1 genetic variations with general and central adiposity changes considering dietary carbohydrate intake among 32,054 adults from four prospective cohort studies. A genetic risk score (GRS) was calculated based on nine AMY1 single-nucleotide polymorphisms, with higher AMY1-GRS indicating higher activity of salivary amylase. We meta-analyzed interactions between AMY1-GRS and dietary intake for changes in general and central adiposity over 5.5–10 years. We found that carbohydrate food intake significantly altered associations of AMY1-GRS with changes in BMI (Pinteraction = 0.001) and waist circumference (Pinteraction < 0.001). Results were consistent and significant in female cohorts rather than in male cohorts. Among women, higher AMY1-GRS was associated with more increases in adiposity if dietary carbohydrate food intake was high, while higher AMY1-GRS was associated with less gains in adiposity when the dietary intake was low. Also, in a 2-year randomized dietary intervention trial, associations of AMY1-GRS with changes in weight (Pinteraction = 0.023) and waist circumference (Pinteraction = 0.037) were significantly modified by carbohydrate intake. Our results suggest the importance of precision nutrition strategies considering participants’ genetic adaptation to carbohydrate-rich diets in regulating general and central adiposity.

We study the orbifold Hirzebruch-Riemann-Roch (HRR) theorem for quotient Deligne-Mumford stacks, explore its relation with representation theory of finite groups, and derive a new orbifold HRR formula via an orbifold Mukai pairing. As a first application, we use this formula to compute the dimensions of $ G $-equivariant moduli spaces of stable sheaves on a $ K3 $ surface $ X $ under the action of a finite subgroup $ G $ of its symplectic automorphism group. We then apply the orbifold HRR formula to reproduce the number of fixed points on $ X $ when $ G $ is cyclic without using the Lefschetz fixed point formula. We prove that under some mild conditions, equivariant moduli spaces of stable sheaves on $ X $ are irreducible symplectic manifolds deformation equivalent to Hilbert schemes of points on $ X $ via a connection between Gieseker and Bridgeland moduli spaces, as well as the derived McKay correspondence. As a corollary, these moduli spaces are also deformation equivalent to equivariant Hilbert schemes of points on $ X. $, Comment: Changed the title and corrected some typos

The soils of four citrus orchards in western Zhejiang were studied in this research. In order to explore the effects of lime on soil chemical properties and bacterial communities, the incubation experiment was conducted with six different dosages of lime addition, 0, 0.6, 1.2, 2.4, 4.8, 7.2 g/kg, respectively. The results showed that soil pH increased significantly (p < 0.05) and the contents of exchangeable acid decreased significantly (p < 0.05) at the early stage of lime application. As time went by, the reacidification existed in the soil of each treatment, mainly reflected in a significant (p < 0.05) decrease in soil pH. Liming increased the nitrate nitrogen content and decreased the ammonium nitrogen content in the four soils. However, the responses of other nutrient indexes to lime varied in different soils, which might be due to the different degrees of soil acidification. In general, the addition of lime increased the soil integrated fertility index (IFI) and improved the soil nutrient status. The application of lime under 2.4 g/kg significantly improved the structure of bacterial community and increased the relative abundance of soil bacterial community species, while the application of lime above 4.8 g/kg might inhibit the growth and activity of microorganisms, resulting in the reduction of soil microbial biomass and diversity. Redundancy analysis (RDA) showed that lime affected the bacterial community mainly by reducing the content of soil active acid and exchangeable acid. At the same time, network analysis showed that the bacterial community had a stronger buffer capacity against external disturbances after lime application. In conclusion, the addition of lime with appropriate amount (2.4~4.8 g/kg, corresponding to a field application rate of 5.8~11.5 t/ha) in acid orchard soil could improve soil properties, soil integrated fertility, and the diversity and stability of the bacterial community.

In order to solve the problem of low accuracy of fault diagnosis algorithms caused by increased network dynamics, this paper proposes a dynamic network fault diagnosis algorithm under 5G network slices. By calculating the failure credibility of each link, a clustering algorithm is used to cluster the symptoms to correct the false symptoms. When constructing the fault propagation model, the time slice t is added to the fault propagation model, thereby depicting different network models in different time slices. Finally, locate the fault by solving the original state and current state of the faulty node. The experimental part compares the algorithm of this paper with the existing algorithm, and verifies that the algorithm of this paper improves the performance of the fault diagnosis algorithm.

The Terahertz band is envisioned to meet the demanding 100 Gbps data rates for 6G wireless communications. Aiming at combating the distance limitation problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is promising. However, relationships among wavelength, array size and antenna spacing give rise to the inaccuracy of planar-wave channel model (PWM), while an enlarged channel matrix dimension leads to excessive parameters of applying spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid beamforming, channel estimation (CE) needs to recover high-dimensional channels from severely compressed channel observation. In this paper, a hybrid spherical- and planar-wave channel model (HSPM) is investigated and proved to be accurate and efficient by adopting PWM within subarray and SWM among subarray. Furthermore, a two-phase HSPM CE mechanism is developed. A deep convolutional-neural-network (DCNN) is designed in the first phase for parameter estimation of reference subarrays, while geometric relationships of the remaining channel parameters between reference subarrays are leveraged to complete CE in the second phase. Extensive numerical results demonstrate the HSPM is accurate at various communication distances, array sizes and carrier frequencies.The DCNN converges fast and achieves high accuracy with 5.2 dB improved normalized-mean-square-error compared to literature methods, and owns substantially low complexity.

The upper Paleozoic tight sandstone in the Linxing area on the eastern margin of the Ordos Basin is characterized by a wide planar distribution and multiple vertical layers that form a region of multi-layered tight sandstone natural gas reservoirs with strong heterogeneity. In this study, physical properties deciphered by using numerous core sample imaging techniques were used to determine the characteristics and formation mechanisms of the tight sandstone reservoirs. We conclude that there are four types of tight sandstone reservoirs in the upper Paleozoic strata of the study area and that the diagenetic evolution of the sandstone can be subdivided into three stages: an early compaction stage, characterized by severe porosity reduction; a middle stage, involving quasi-continuous dissolution–cementation via migrating organic acids generated by coal-measure source rocks; and a late stage, involving oil and gas charging and clay mineral conversion. These processes caused the reservoirs to assume the densification characteristics of multi-stage calcareous, siliceous, and clay mineral cementation and infilling. Our comprehensive analysis shows that compaction and quasi-continuous dissolution and cementation are the main reasons for the formation of tight sandstone. Although organic acids promote the development of dissolution, it is also the fuse of sandstone densification. The matching relationship between sandstone densification and hydrocarbon accumulation is characterized by densification followed by accumulation.

With the development of the advanced precision manufacturing field, the curve profile is becoming more and more complex, and the profile curve evaluation is no longer limited to the two-dimensional plane. Aiming at the difficulty and low efficiency of complex contour evaluation of high-precision parts, this paper proposes a global evaluation method of three-dimensional curve contour. After the curve contour is extracted, dimensioned and calibrated, according to the definition of GB/T1182-2008 plane curve contour, an envelope circle equation is established to evaluate the contour of the complex line. The curve contour theoretical points are used to establish the envelope detection area function, and the position relationship of the measuring points relative to the theoretical contour is evaluated according to the circle function of the envelope group, and the line contour error is determined. Experiments show that the evaluation result is intuitive and can be traced back to the specific area of the curve contour. This method realizes the evaluation of the whole area of the spatially complex curve contour. The method is accurate and efficient, and has overall and unitary characteristics.

Additional file 1. The diameter and score of wheals and flush, and pathological damage grades caused by allergy.

ComOpT is an open-source research tool for coverage-driven testing of autonomous driving systems, focusing on planning and control. Starting with (i) a meta-model characterizing discrete conditions to be considered and (ii) constraints specifying the impossibility of certain combinations, ComOpT first generates constraint-feasible abstract scenarios while maximally increasing the coverage of k-way combinatorial testing. Each abstract scenario can be viewed as a conceptual equivalence class, which is then instantiated into multiple concrete scenarios by (1) randomly picking one local map that fulfills the specified geographical condition, and (2) assigning all actors accordingly with parameters within the range. Finally, ComOpT evaluates each concrete scenario against a set of KPIs and performs local scenario variation via spawning a new agent that might lead to a collision at designated points. We use ComOpT to test the Apollo~6 autonomous driving software stack. ComOpT can generate highly diversified scenarios with limited test budgets while uncovering problematic situations such as inabilities to make simple right turns, uncomfortable accelerations, and dangerous driving patterns. ComOpT participated in the 2021 IEEE AI Autonomous Vehicle Testing Challenge and won first place among more than 110 contending teams.

As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient in-memory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic step-centric programming model named Gather-Move-Update to abstract different RW algorithms. Based on the programming model, we develop the step interleaving technique to hide memory access latency by switching the executions of different random walk queries. In our experiments, we use four representative RW algorithms including PPR, DeepWalk, Node2Vec and MetaPath to demonstrate the efficiency and programming flexibility of ThunderRW. Experimental results show that ThunderRW outperforms state-of-the-art approaches by an order of magnitude, and the step interleaving technique significantly reduces the CPU pipeline stall from 73.1% to 15.0%.

Zhe Shi,1,* Yichuan Xu,1,* Ruzha Mulatibieke,2 Qiang Zhong,1 Xin Pan,1 Yuhang Chen,1 Qiang Lian,1 Xin Luo,3 Zhanjun Shi,1 Qingan Zhu1 1Department of Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Plastic Surgery, The Third Affiliated Hospital of Sun Yet-Sen University, Guangzhou, People’s Republic of China; 3Rehabilitation Medical School, Guangzhou International Economics College, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhanjun Shi; Qingan Zhu Tel +86 2062787924; +86 20622787195Email nfgk@sohu.com; qinganzhu@gmail.comPurpose: Autologous bone grafts are the gold standard for treating bone defects. However, limited bone supply and morbidity at the donor site restrict its extensive use. Therefore, developing bone graft materials as an alternative to autologous grafts has gained considerable attention. Injectable hydrogels endowed with osteogenic potential have the ability to fill irregular bone defects using minimally invasive procedures and have thus been attracting researchers’ attention. However, from a clinical perspective, most fabrication methods employed for the current injectable osteogenic hydrogels are difficult and inconvenient. In the current study, we fabricated an injectable osteogenic hydrogel using a simple and convenient strategy.Materials and Methods: Gelatin-methacryloyl (GelMA) pre-polymer was synthetized. Nano silicate (SN) and stromal cell-derived factor-1 alpha (SDF-1α) were introduced into the pre-polymer to achieve injectability, controlled release property, excellent osteogenic ability, and efficient stem cell homing.Results: The GelMA-SN-SDF-1α demonstrated excellent injectability via a 17-G needle at room temperature. The loaded SDF-1α exhibited a long-term controlled release pattern and efficiently stimulated MSC migration and homing. The GelMA-SN-SDF-1α hydrogel amplified cell spreading, migration, osteogenic-related biomarker expression, and matrix mineralization. The GelMA-SN-SDF-1α hydrogel filled critical-sized calvaria defects in rats and demonstrated excellent bone regeneration ability, as assessed using micro-CT scanning and histomorphometric staining.Conclusion: The GelMA-SN-SDF-1α hydrogel provides a simple and convenient strategy for the fabrication of injectable osteogenic graft materials.Keywords: bone defects, gelatin methacryloyl hydrogels, injectable, nano-silicate, SDF-1α, osteogenic

Conventional non-negative algorithms restrict the weight coefficient vector under non-negativity constraints to satisfy several inherent characteristics of a specific system. However, the presence of impulsive noise causes conventional non-negative algorithms to exhibit inferior performance. Under this background, a robust non-negative least mean square (R-NNLMS) algorithm based on a step-size scaler is proposed. The proposed algorithm uses a step-size scaler to avoid the influence of impulsive noise. For various outliers, the step-size scaler can adjust the step size of the algorithm, thereby eliminating the large error caused by impulsive noise. Furthermore, to improve the performance of the proposed algorithm in sparse system identification, the inversely-proportional R-NNLMS (IP-RNNLMS) algorithm is proposed. The simulation result demonstrates that the R-NNLMS algorithm can eliminate the influence of impulsive noise while showing fast convergence rate and low steady-state error under other noises. In addition, the IP-RNNLMS algorithm has faster convergence rate compared with the R-NNLMS algorithm under sparse system.

The prediction of bearing degradation trend is important for remaining useful life (RUL) estimation. However, there are no clear indicators due to the presence of noise, and long step prediction is not effective enough. This paper proposed a predict method using LSTM in which the time domain and spectral kurtosis related features was selected by monotonicity. Then PCA was employed to fuse features. Health indicator was generated which was increasing as the bearing approaches to failure. A sequence-to-one regression LSTM network was employed to predict the trend of health indicator. Intelligent Maintenance Systems (IMS) bearing run to failure dataset was employed to verify the validity of the method. The result shows that proposed method can effectively predict the degradation trend. This method can be used to real-time estimate the RUL.

This paper addresses a consensus-based approach for visual servo control of multiple mobile robots. The basic idea is to utilize single-integrator consensus techniques to design a image-based visual servo control algorithm for each robot under a certain network. The aim of this work is to extract common objects between different images captured by different robots and adjust the positions and orientations of robots to concentrate on common objects. We show that if the union of communication graphs has a directed spanning tree, the states of robots would achieve consensus based on the relative states. At the end of this paper we present experiments to verify the feasibility and effectiveness of proposed algorithms.

For the special needs of attitude adjustment and vibration isolation of precise instruments, parallel mechanisms with sub-closed-loops (PMWS) are proposed to eliminate the interferences that vary in frequency. First, the multi-drive units composed by sub-closed-loops are synthesized by two derivative methods, and the PMWS with diffident DOF are synthesized based on screw theory. Furthermore, proposing the concept of the sub generalized coordinate, and the first and the second order influence coefficients of each link of PMWS are established under the generalized coordinates and the sub generalized coordinates. Besides, based on the principle of virtual power and the Newton-Euler formula, meanwhile combining the kinematics analysis of PMWS, the dynamics formulas of PMWS are established separately in joint space and operational space. Finally, taking 6-(PRRR)US PMWS as numerical example, by comparing the results of theoretical calculation and simulation, the kinematics and dynamics formulas of PMWS are verified.

The metal bellows used for flexible connection of oxygen lance in metallurgical converter compensate the displacement of pipeline by its bending deformation. Due to the waveform structure, the adjacent waves at the fixed end of the metal bellows are squeezed together, leading to premature fracture. In order to explore the influence of waveform structure parameters on the failure mechanism of the metal bellows, a U-shaped metal bellows with different waveform structure parameters was designed in this paper. The ultimate repeated bending process of the metal bellows was numerically simulated by ABAQUS finite element software. The influence law of waveform structure parameters on equivalent stress, equivalent strain and ultimate bending angular displacement of the metal bellows were obtained. Then, the repeated bending fatigue life of the bellows was tested on the fatigue testing machine, and the influence law of the waveform structure parameters on the fatigue life of the bellows under ultimate bending angular displacement amplitudes was obtained. The experimental results verify the accuracy of the finite element simulation. Finally, X-ray diffraction peaks confirmed the proved that the plasticity accumulation at the wave trough of the equal parameters metal bellows (EPMB) is large, so the hardness value is large. The micro morphology of the fracture and the surrounding area was analyzed by scanning electron microscope (SEM), and the crack propagation and the ultimate repeated bending failure mechanism of the bellows with different waveform structures was revealed. The initial cracks appear in the inner and outer surfaces of the wave trough, distribute along the circumferential direction, and propagate from the inner and outer surfaces to the central layer. Among them, the fracture morphology of the EPMB is mainly composed of fatigue fracture and plastic fracture. However, the fracture morphology of the unequal parameters metal bellows (Un-EPMB) is dominated by fatigue fracture. The results show that the fatigue life and bending fracture resistance of metal bellows can be improved by designing waveform structure with unequal parameters.

Single image inverse problem is a notoriously challenging ill-posed problem that aims to restore the original image from one of its corrupted versions. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. Deep Image Prior (DIP) offers a new approach that forces the recovered image to be synthesized from a given deep architecture. While DIP is quite an effective unsupervised approach, it is deprecated in real-world applications because of the requirement of human assistance. In this work, we aim to find the best-recovered image without the assistance of humans by adding a stopping criterion, which will reach maximum when the iteration no longer improves the image quality. More specifically, we propose to add a pseudo noise to the corrupted image and measure the pseudo-noise component in the recovered image by the orthogonality between signal and noise. The accuracy of the orthogonal stopping criterion has been demonstrated for several tested problems such as denoising, super-resolution, and inpainting, in which 38 out of 40 experiments are higher than 95%.

Electric vehicles have gained more and more attention because of the serious oil crisis and environmental problems. However, the disadvantages of the electric vehicle, such as short driving range, high battery cost, and inconvenient charging, are hindering its market development and expansion. The realization of on-road wireless power transfer technology can effectively solve the problems of short driving range, prevent the battery from being completely discharged to prolong its service life, and reduce requirement of on-board battery. In this paper, the charging mode and the compensation topology of wireless power transfer technology are discussed and then the equivalent circuit model of segmental wireless power transfer system is built. We carried out some magnetic field simulation to analyze how the track shape and length influence coupling coefficient, which is later verified by experiments.

The article describes a non-invasive physiological signal acquisition system based on Ballistocardiography technology, which is applied to the ward monitoring scene to assist medical staff and reduce work consumption. The overall structure of the signal acquisition system and the key signal processing circuit are described in detail. Since the design system uses an array sensing front end, signal crosstalk between multiple channels becomes an obvious problem. In this work, the signal noise analysis and design of the sampling part of the acquisition system is carried out, and an effective measures are taken to reduce the signal crosstalk between the channels. Considering the application requirements in the ward, the electrostatic discharge (ESD) immunity design of the acquisition system was designed and passed the test of relevant standards (IEC60601-1-2).

A digital painting uses a graphics program, created on a computer with a brush, colours, and other virtual paint box supplies. This definition applies to its main digital painting (as a computer file) on the carrier. The carrier is transferred to the secondary physical process in a non-manual (printed on paper, acrylic glass, aluminum, canvas, etc.). Digital technology, seeking computer (tools, techniques, and digitally encoded information content) as tools and materials for creating, can be defined as existing technology. Even though many web applications are used in the digital painting community, some technical demand is still there, such as the need to cope with the efficient painting visualization of browser plug-ins. As the latest Hypertext markup Language 5 (HTML5) became more popular with virtual imaging technology, it gradually uses in digital painting design. High-tech devices create virtual imaging technology based on HTML 5 artificial environments and are proposed to enhance the digital painting system and its performance with interactive virtualized technologies. Virtual reality technology based on HTML 5 technology can improve virtual aesthetics, faces problems and virtual reality digital painting art and design.

With the development of artificial intelligence, machine learning methods have been widely applied to predictive modelling in various fields. Support vector machine is a linear classifier with the largest interval defined on the feature space. In order to improve the current status of server operation and maintenance, and improve the accuracy and efficiency of server health prediction, this paper studies the feature quantity selection based on grey relational analysis, studies the support vector machine applied to data generalization and classification, and establishes a health prediction model. The simulation training of historical data shows that the method has fast learning, high prediction speed, high efficiency, high accuracy and wide application prospects.

With the planning and construction of the ubiquitous power Internet of Things(UPIOT), how to achieve secure connection and interaction between the platform and the terminal becomes a crucial issue. Based on cloud computing and edge computing, this paper constructs the “edge-cloud collaboration” architecture of UPIOT to meet the functional requirements of grid-side intelligent services and core-side data analysis and low-latency, high-bandwidth, and large-access performance. According to the actual deployment situation of Jiangsu electric power, the firewall and VPN application scenario security protection scheme is proposed to provide a theoretical basis for the UPIOT security protection.

In this work, we discuss the problem of approximating a multivariate function via $\ell_1$ minimization method, using a random chosen sub-grid of the corresponding tensor grid of Gaussian points. The independent variables of the function are assumed to be random variables, and thus, the framework provides a non-intrusive way to construct the generalized polynomial chaos expansions, stemming from the motivating application of Uncertainty Quantification (UQ). We provide theoretical analysis on the validity of the approach. The framework includes both the bounded measures such as the uniform and the Chebyshev measure, and the unbounded measures which include the Gaussian measure. Several numerical examples are given to confirm the theoretical results., 25 pages, 8 figures

Summary Computational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient‐specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient‐specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.

The effects of UV-B radiation on the content of bioactive components and the antioxidant activity of Prunella vulgaris L. spica during development were studied. The experimental design involved two levels of UV-B radiation intensity (0 and 120 μW cm−2 nm−1). The results showed that the contents of total flavonoids, rosmarinic acid, caffeic acid and hyperoside, as well as the antioxidant capacities (DPPH● and ABTS•+ scavenging activities), in the spicas significantly decreased during spica development. The content of salviaflaside in the spicas significantly increased during development. The highest contents of total flavonoids, rosmarinic acid, and caffeic acid and the highest antioxidant activities were found in spicas in the full-flowering stage, while the highest content of hyperoside was found in spicas in the bud stage. In addition, the highest content of salviaflaside was found in spicas in the mature-fruiting stage. UV-B radiation significantly promoted the synthesis of secondary metabolites, increased the contents of the main bioactive components in the three developmental stages of isolated dried spicas, and significantly increased the DPPH● and ABTS•+ scavenging activities of P. vulgaris spicas in the mature-fruiting stage. Moreover, the total flavonoids content was positively correlated with the DPPH● and ABTS•+ scavenging activities, and the correlation with the DPPH● scavenging activity was very strong. This result shows that the highest contents of the main bioactive components in the spicas were not all found in the same developmental stages of P. vulgaris. Our research revealed that the best stage for harvesting P. vulgaris spica was between the bud stage and the full-flowering stage since harvesting at this point provides a higher content of bioactive components and a higher antioxidant capacity, which is relevant for medicinal applications.

This paper derives mathematics model of 6-pulse diode rectifiers. Its periodic orbit model and Poincare mapping are established and its steady state performance index and stability margin index can be calculated by the model. dc bus voltage sag can be assessed by the periodic orbit model and the mathematics model. The model is validated by comparison of the resultant steady state and dynamic state behaviors with those obtained from extensive simulations of detailed 6-pulse diode rectifiers model. It is shown that the model can capture the key features of the dynamic response of the rectifiers and dc bus voltage sag, which can be used for performance assessment of dc power systems with the diode rectifiers.

Concerning the difficulties of parameter tuning and poor anti-interference performance in the traditional proportional plus derivative virtual inertia control (PDVIC), this paper presents an improved frequency response control scheme of directly-driven wind turbine with permanent magnet synchronous generator (D-PMSG) based on the active disturbance rejection control (ADRC). The algorithm realizes the power grid disturbance estimation and compensation by using the extended state observer (ESO) to reject the disturbance. With the introduction of the capacity factor of wind turbines, the D-PMSG could provide frequency support for the power system according to their abilities. Compared with PDVIC, this method has better adaptability and robustness. Simulation results confirmed the validity and feasibility of the virtual inertia control based on the ADRC.

Photodynamic therapy (PDT) is a novel and promising antitumor treatment. Our previous study showed that hydrophilic/lipophilic tetra-α-(4-carboxyphenoxy) phthalocyanine zinc- (TαPcZn-) mediated PDT (TαPcZn-PDT) inhibits the proliferation of human hepatocellular carcinoma Bel-7402 cells by triggering apoptosis and arresting cell cycle. However, mechanisms of TαPcZn-PDT-induced apoptosis of Bel-7402 cells have not been fully clarified. In the present study, therefore, effect of TαPcZn-PDT on apoptosis, P38MAPK, p-P38MAPK, Caspase-8, Caspase-3, Bcl-2, Bid, Cytochrome c, and mitochondria membrane potential in Bel-7402 cells without or with P38MAPK inhibitor SB203580 or Caspase-8 inhibitor Ac-IEFD-CHO was investigated by haematoxylin and eosin (HE) staining assay, flow cytometry analysis of annexin V-FITC/propidium iodide (PI) double staining cells and 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethylbenzimidazolylcarbocyanine iodide (JC-1), and immunoblot assay. We found that TαPcZn-PDT resulted in apoptosis induction, activation of P38MAPK, Caspase-8, Caspase-3, and Bid, downregulation of Bcl-2, release of Cytochrome c from mitochondria, and disruption of mitochondrial membrane potential in TαPcZn-PDT-treated Bel-7402 cells. In contrast, SB203580 or Ac-IEFD-CHO attenuated induction of apoptosis, activation of P38MAPK, Caspase-8, Caspase-3, and Bid, downregulation of Bcl-2, release of Cytochrome c from mitochondria, and disruption of mitochondrial membrane potential in TαPcZn-PDT-treated Bel-7402 cells. Taken together, we conclude that Caspase-3, Bcl-2, Bid, and mitochondria are involved in autoregulatory feedback of P38MAPK/Caspase-8 during TαPcZn-PDT-induced apoptosis of Bel-7402 cells.

In order to obtain phase information for the X-ray diffraction of tabtoxin resistance protein (TTR) crystal using the MAD phasing method, a selenomethionine (Se-Met) derivative of TTR was overexpressed inE. coli strain M15, with pQE-30 plasmid, through IPTG induction in M9 medium containing Se-Met. The product was purified to an estimated homogeneity of greater than 95% according to SDS-PAGE, by a Ni-NTA metal affinity followed by a Mono Q anion exchange column chromatography. The successful substitution of Se-Met for methionine (Met) was confirmed by MALDI-TOF and ESI-Quadrupole Mass Spectrometry analysis. The derivative crystal was obtained using similar conditions as those for the native.

The characteristics of two-actuator controlled fuel injection system which is established by the Electronic Unit Pump and Common Rail injector to realize independent controlling the pumping and injection processes are investigated from the point of experimental analysis. The experimental results indicate that high pressure up to 130Mpa can be generated on the advance two-actuator system at low camshaft speed. Meanwhile, synchronous variations of the pumping and injection timing have little influence on the fuel pressure and injection rates, the reason for this phenomenon is that fuel injection characteristics here are mainly determined by the cam lift and velocity. Two-actuator system enables to generate the desired Nozzle Opening Pressure timely and produce a very flexible control of injection rate through changing control parameters of both Spill Control Valve and Nozzle Control Valve, which have great capabilities on fuel atomization and air-fuel mixture formation.

The multiple injection characteristics of two-actuator controlled fuel injection system(two-actuator system) were investigated in this paper, which was established by the Electronic Unit Pump and Common Rail injector to realize the way to independently control the pumping and injection processes. The two-actuator system simulation model was validated and the results indicated consistency between the calculative and the experimental one. Consequently, the model is capable of predicting the characteristics of two-actuator system and plays an extension and complementarity role for experiments, The simulation results of multiple injections illustrated that two-actuator system enables to generate different nozzle opening pressure efficiently and produce a very flexible control of injection rate through changing control parameters of both SCV and NCV.

Crystal growth has been promoted in the fluid cell of an Atomic Force Microscope (AFM) by passing Ba–Sr–SO4 aqueous solutions over barite (001) cleavage surfaces. Steps advance in structural continuity with the original barite (001) surfaces and two-dimensional nucleation occurs preferentially on the newly-formed terraces. The terraces are, on average, 7.5% lower than pure barite terraces. Since the ionic radius of Sr2+ is smaller that the ionic radius of Ba2+, the reduction of terrace height is consistent with an extensive incorporation of Sr2+ into the barite structure. Therefore, it can be considered that the newly-formed terraces have compositions corresponding to terms of the BaxSr1xSO4 solid solution. A non-linear dependence of step rate on [SrSO4] concentration in the solution (and therefore on supersaturation) has been found. The growth behaviour has been discussed by considering both the physicochemical properties of the BaxSr1xSO4 solid solution–aqueous solution (SS–AS) system and a kinetic-based step growth model.

Computational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient-specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient-specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.