Search

To enhance the performance of ultra-high voltage power fittings in severe weather conditions without altering their current structure, the high-strength and toughness aluminum alloys were rationally selected to study the optimization of the die-casting process. This approach aims to improve the overall longevity and function of the power fittings in extreme climates. First of all, the propose of this study is to use the material's strength–toughness product (STP) concept to evaluate the material stability of the power fitting impact resistance and fatigue toughness in order to determine the appropriate material selection. Secondly, the location of the mold's sprue and gate was optimized through finite element simulation to prevent gas volume and flow defects during the casting process. This improves the material's toughness and anti-fatigue failure characteristics of the product. Then, vacuum equipment and a vacuum valve auxiliary system were added based on the existing die-casting machine, and the mold structure was optimized to enable the vacuum die-casting process. Finally, a water-based boron nitride environmentally friendly mold release agent was used to solve demolding difficulties with an A356 aluminum alloy and improve mold lubrication and surface quality. The production of quad-bundled spacers using A356 and vacuum die casting has resulted in parts with a tensile strength of at least 250 MPa and an elongation of no less than 7%. This improvement has laid a foundation for enhancing the operational reliability of existing overhead transmission line fittings. [ABSTRACT FROM AUTHOR]

To ensure the high reliability of the pipeline system, it is necessary to take group maintenance actions. This work proposes an optimal group maintenance approach based on a heuristically genetic algorithm to attain the system-performing goal while minimizing overall costs with assured system reliability. Multi-stage corrosion defects and multi-level risk areas are considered in this novel approach. The proposed method can sufficiently avoid pipeline failures by considering the multi-level probability of failure constraints. The methodology is illustrated by taking the 60-segment pipelines with multiple corrosion defects as an example. Numerical studies show that the proposed methodology can find an optimized maintenance strategy with a lower cost compared with the unified batch maintenance strategy. In addition, an optimal dynamic maintenance model is proposed and compared with the static model when the environment of the pipelines varies. Sensitivity analyses of the physical parameters of the phenomenon and the tuning parameters of the data-driven model show that the method has good effectiveness, compatibility, and universality. [ABSTRACT FROM AUTHOR]

The accurate detection of insulators is an important prerequisite for insulator fault diagnosis. To solve the problem of background interference and overlap caused by the axis-aligned bounding boxes in the tilting insulator detection tasks, we construct an improved detection architecture according to the scale and tilt features of the insulators from several perspectives, such as bounding box representation, loss function, and anchor box construction. A new orientation detection method for tilting insulators based on angle regression and priori constraints is put forward in this paper. Ablation tests and comparative validation tests were conducted on a self-built aerial insulator image dataset. The results show that the detection accuracy of our model was increased by 7.98% compared with that of the baseline, and the overall detection accuracy reached 82.33%. Moreover, the detection effect of our method was better than that of the YOLOv5 detection model and other orientation detection models. Our model provides a new idea for the accurate orientation detection of insulators. [ABSTRACT FROM AUTHOR]

In this paper, we consider the computational problem of the quaternion equality constrained least squares problem. First, applying the quaternion QR decomposition and an equivalence problem of the quaternion equality constrained least squares problem, we obtain the expressions of the general solution and the minimal norm solution of the quaternion equality constrained least squares problem. And then by using the real representation matrices of quaternion matrices, the special structure of the real representation matrices and the real structure-preserving algorithm of the quaternion QR decomposition, we purpose a real structure-preserving algorithm for the minimal norm solution of the quaternion equality constrained least squares problem. The purposed algorithm only involves real algebraic operations, and the number of real floating-point operations and assignments has been minimized. Finally, we give two examples, which show that our purposed algorithm is more efficient and time-saving than direct computation in the quaternion Toolbox for Matlab. [ABSTRACT FROM AUTHOR]

Bolts are important components on transmission lines, and the timely detection and exclusion of their abnormal conditions are imperative to ensure the stable operation of transmission lines. To accurately identify bolt defects, we propose a bolt defect identification method incorporating an attention mechanism and wide residual networks. Firstly, the spatial dimension of the feature map is compressed by the spatial compression network to obtain the global features of the channel dimension and enhance the attention of the network to the vital information in a weighted way. After that, the enhanced feature map is decomposed into two one-dimensional feature vectors by embedding a cooperative attention mechanism to establish long-term dependencies in one spatial direction and preserve precise location information in the other direction. During this process, the prior knowledge of the bolts is utilized to help the network extract critical feature information more accurately, thus improving the accuracy of recognition. The test results show that the bolt recognition accuracy of this method is improved to 94.57% compared with that before embedding the attention mechanism, which verifies the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, attention mechanisms have gained popularity in sequential recommender systems (SRSs) due to obtaining dynamic user preferences efficiently. However, over-parameterization of these models often increases the risk of overfitting. To address this challenge, we propose a Transformer model based on tensor train networks. Initially, we propose a tensor train layer (TTL) to accommodate the original weight matrix, thus reducing the space complexity of the mapping layer. Based on the TTL, we reconfigure the multi-head attention module and the position-wise feed-forward network. Finally, a tensor train layer replaces the output layer to complete the overall compression. According to the experimental results, the proposed model compresses SRSs parameters effectively, achieving compression rates of 76. 2 % − 85. 0 % , while maintaining or enhancing sequence recommendation performance. To our knowledge, the Tensor Train Transformer is the first model compression approach for Transformer-based SRSs, and the model is broadly applicable. • A novel model as a remedy for parameter redundancy in SRSs is proposed. • A tensor train layer has lower space complexity than the original linear layer. • The proposed model compresses SRSs parameters effectively. • Tensor Train Transformer model is broadly applicable. [ABSTRACT FROM AUTHOR]

Ring-finger protein 126 (RNF126), an E3 ubiquitin ligase, plays crucial roles in various biological processes, including cell proliferation, DNA damage repair, and intracellular vesicle trafficking. Whether RNF126 is modulated by posttranslational modifications is poorly understood. Here, we show that PARP1 interacts with and poly(ADP)ribosylates RNF126, which then recruits the PAR-binding E3 ubiquitin ligase CHFR to promote ubiquitination and degradation of RNF126. Moreover, RNF126 is required for the activation of ATR-Chk1 signaling induced by either irradiation (IR) or a PARP inhibitor (PARPi), and depletion of RNF126 increases the sensitivity of triple-negative breast cancer (TNBC) cells to PARPi treatment. Our findings suggest that PARPi-mediated upregulation of RNF126 protein stability contributes to TNBC cell resistance to PARPi. Therefore, targeting the E3 ubiquitin ligase RNF126 may be a novel treatment for overcoming the resistance of TNBC cells to PARPi in clinical trials. • RNF126 physically interacts with PARP1 in vivo. • PARP1 promotes RNF126 polyubiquitylation and enhance its ubiquitination-proteasome-dependent degradation. • E3 ligase CHFR interacts with PARylated RNF126 and promotes its ubiquitination and degradation. • Depletion of RNF126 inhibited ATR-Chk1 pathway activation and conferred sensitivity of TNBC cells to PARP inhibitors. [ABSTRACT FROM AUTHOR]

Reasonable maintenance of pipeline systems is essential for ensuring energy transportation and achieving a trade-off between safe operation and maintenance costs. However, previous studies have barely considered the system structure, system availability, and multi-stage corrosion. To fill this gap, a multi-objective optimization framework was established. This framework can balance the cost and downtime caused by maintenance to achieve the desired trade-off between the maintenance cost and transportation continuity of a system with multi-stage corrosion defects and multi-level reliability constraints. By combining the NSGA-II algorithm with the general structure of pipeline systems, chromosome coding was designed to improve the efficiency of the evolutionary algorithm, and a series of preventive maintenance strategies were proposed, including decisions regarding repaired segments, time point for maintenance, and the type of maintenance mode. The effectiveness and accuracy of the methodology were illustrated by considering the pipeline systems with different structures and parameters as examples. A comparison of multi-objective programming with empirical decision and single-objective decisions demonstrates the rationality and universality of the methodology. The proposed methodology can help adjust the corresponding maintenance strategies for diverse requirements and provide reasonable maintenance schemes for corroded pipeline systems. • Multiple states and diversified maintenance modes are considered. • Cost and availability are optimized under multi-level PoF constraints. • NSGA-II is improved to complete multi-objective management framework. • The methodology can be applied to the general structure of pipeline systems. • The methodology is demonstrated by comparing with single-objective optimization. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of extended dissipative analysis based non-fragile controller for networked stochastic system under an event-triggered sampling scheme. First, we introduce an event-triggered scheme and the proposed system is modelled as a stochastic time-delay system. By multi-delay partitioning method, some delay-dependent criteria in terms of linear matrix inequality (L M I s) are proposed to ensure the mean-square stable with extended dissipative performance, where using the notion of the extended dissipative, we can solve (Q, S, R)-dissipativity, H ∞ , passivity and L 2 − L ∞ performance in a unified framework by regulating some weight matrices. In addition, two non-fragile state feedback controllers are designed to ensure that the closed-loop system satisfies the extended dissipative performance. Finally, some simulation examples are given to substantiate the validity of the addressed method. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) significantly increases myocardial ischemia/reperfusion (MI/R) injury. During DM, cardioprotection induced by conventional pre-conditioning (PreCon) is decreased due to impaired AMP-activated protein kinase (AMPK) signaling. The current study investigated whether PreCon with inhaled anesthetic sevoflurane (SF-PreCon) remains cardioprotective during DM, and identified the involved mechanisms. Normal diet (ND) and high-fat diet (HFD)-induced DM mice were randomized into control and SF-PreCon (3 cycles of 15-minute period exposures to 2% sevoflurane) groups before MI/R. SF-PreCon markedly reduced MI/R injury in DM mice, as evidenced by improved cardiac function (increased LVEF and ±Dp/dt), decreased infarct size, and decreased apoptosis. To determine the relevant role of AMPK, the effect of SF-PreCon was determined in cardiac-specific AMPKα2 dominant negative expressing mice (AMPK-DN). SF-PreCon decreased MI/R injury in AMPK-DN mice. To explore the molecular mechanisms responsible for SF-PreCon mediated cardioprotection in DM mice, cell survival molecules were screened. Interestingly, in ND mice, SF-PreCon significantly reduced MI/R-induced activation of p38, a pro-death MAPK, without altering ERK and JNK. In DM and AMPK-DN mice, the inhibitory effect of SF-PreCon upon p38 activation was significantly blunted. However, SF-PreCon significantly increased phosphorylation of ERK1/2, a pro-survival MAPK in DM and AMPK-DN mice. We demonstrate that SF-PreCon protects the heart via AMPK-dependent inhibition of pro-death MAPK in ND mice. However, SF-PreCon exerts cardioprotective action via AMPK-independent activation of a pro-survival MAPK member in DM mice. SF-PreCon may be beneficial compared to conventional PreCon in diabetes or clinical scenarios in which AMPK signaling is impaired. [ABSTRACT FROM AUTHOR]

Abstract The recommendation algorithm is attracting increasing attention in analyzing big data. Matrix factorization (MF) is one of the recommendation methods and Singular Value Decomposition (SVD) is the most popular matrix factorization method. However, the existing SVD methods usually initialize user and item feature randomly, not fully utilize the information of the data, so require plenty of experiments to determine feature matrix dimension, with low convergence efficiency and low accuracy. This paper presents a hybrid initialization method based on attribute mapping and autoencoder neural network to solve these problems, which consists of three parts: (1) use the number of item attribute types to determine feature matrix dimension in order to avoid multiple experiments to select the optimal dimension value; (2) use items' attributes to initialize the item feature matrix in SVD＋＋, and use an attribute mapping mechanism to get an item feature vector by fitting the rating matrix to accelerate the convergence; (3) adopt the autoencoder neural network to reduce feature dimension and obtain item latent features for initializing SVD＋＋. The experimental results show that our methods achieve better performance than SVD＋＋ random initialization and also be adopted to other matrix factorization methods. [ABSTRACT FROM AUTHOR]

This paper considers the problem of extended dissipativity analysis and synthesis for network control systems under an event-triggered scheme, where the extended dissipativity analysis unifies the H ∞ , passivity, dissipativity, and L 2 − L ∞ performance in one framework. Firstly, the proposed system is modeled as a network control system with an suitable event-triggered scheme. Then, by using the improved integral inequality and reciprocally convex inequality technique, sufficient condition to guarantee the extended dissipativity of the network control systems is given in terms of linear matrix inequalities ( LMIs ). Furthermore, the design of the state feedback controller is also proposed. Finally, numerical examples are given to illustrate the efficiency of the addressed method. [ABSTRACT FROM AUTHOR]

Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy. [ABSTRACT FROM AUTHOR]

To provide preferable operating conditions for biomass carbonization, the carbonization properties of maize straw, cotton stalk and poplar wood were investigated in nitrogen atmosphere by segmented heating process at different final temperatures of 300–800 °C for 1 h. Segmented heating carbonization contributes to higher ratio of mass and energy yields than a constant temperature/linear heating process with final temperature of around 300 °C. The fuel properties and characteristics of raw and carbonized biomass were analysed systematically. Increasing carbonization temperature, the fuel ratios of the obtained chars rise exponentially, and the profile of atomic H/C ratio versus atomic O/C ratio approximately decreases linearly. There are small changes of sulphur and nitrogen content in the chars. To provide an estimation method for higher heating value for torrefied/carbonized biomass, a new correlation is proposed based on ultimate analysis. The correlation is H H V = 19.9579 + 0.1284 C + 0.3355 H − 0.1209 O − 1.3836 N − 5.4680 S , which is validated through experimental data. The correlation has a low level of errors, that the correlation efficient, mean absolute error, average absolute error and average bias error are 0.971, − 6.75 × 10 − 6 MJ, 1.2% and 0.02% respectively. This work will make a contribution to the biomass pretreatment process. [ABSTRACT FROM AUTHOR]

In this work, a new robust nonlinear feedback control method with dynamic active compensation is proposed; the active control method has been applied to an integral series of finite-time single-input single-output nonlinear system with uncertainty. In further tracking the closed-loop stability and nonlinear uncertainty, an extended state observer has been employed to solve the immeasurability and nonlinear uncertainty within a nonlinear system. A singular perturbation theory has been used to solve for the finite-time stability of the closed-loop system; furthermore, numerical simulations showed that the robust nonlinear feedback controller tracked the uncertainty in a nonlinear Duffing-type oscillator and has proven the effectiveness of the approximate finite-time control strategy proposed. By using an approximate finite-time control approach with active compensation, the uncertainty in a nonlinear system could be accurately estimated and controlled. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study aimed to develop a nomogram to predict the overall survival (OS) of stage IV breast cancer patients. We searched the National Cancer Database (NCDB) for stage IV breast cancer patients diagnosed between 2010 and 2013. Predictors of OS were identified and a nomogram was developed and validated using concordance index (C-index), calibration plots, and risk group stratifications. A total of 7199 patients from the NCDB were included in the study. With a median follow-up of 25.7 months, the 1-year and 3-year OS rates were 80.6% and 52.5%, respectively. Race, age, comorbidity status, T-stage, grade, ER/PR/Her2 status, the presence of lung/liver/brain metastasis, surgery, radiotherapy, and chemotherapy were significantly associated with OS. The developed nomogram had a C-index of 0.722 (95% CI 0.710-0.734) and 0.725 (95% CI 0.713-0.736) in the training and the validation cohorts, respectively. The predicted survival using the nomogram is well correlated with actual OS. The nomogram was able to stratify patients into different risk groups, among which the survival benefit of local therapy varied. We developed a nomogram to predict the overall survival of stage IV breast cancer patients. Prospectively designed studies with international collaborations are needed to further validate our nomogram. [ABSTRACT FROM AUTHOR]

Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. The simulation results are provided to illustrate the reliability and efficiency of the proposed filter. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the quaternion equality constrained weighted least squares problem. First, according to the properties of the Moore-Penrose generalized inverse matrix, the singular value decomposition, the CS decomposition, and the real representation matrices of quaternion matrices, we obtain a real unconstrained equivalent problem of the quaternion equality constrained weighted least squares problem. And then we give the expressions of its general solution and minimal norm solution. Finally, according to the properties of the real representation matrices of quaternion matrices, the special structure of real representation matrices and the real structure-preserving singular value decomposition of quaternion matrix, we propose a real structure-preserving algorithm for the minimal norm solution of the quaternion equality constrained weighted least squares problem, and illustrate the effectiveness of proposed algorithm by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recommenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine. [ABSTRACT FROM AUTHOR]

Objectives: Diabetic retinopathy (DR) is a severe complication of chronic diabetes. The C1q/TNF-related protein family (CTRPs) has been demonstrated to exert protective effects against obesity and atherosclerosis in animal studies. Heretofore, the association between circulating CTRPs and DR patients has been unexplored. In the current study, we attempt to define this association, as well as the effect of CTRPs upon DR pathophysiology. Design: The present investigation is a case control study that enrolled control subjects and type 2 diabetes mellitus (T2DM) patients diagnosed with DR. Serum CTRPs and sVACM-1 were determined by ELISA. Results: Serum CTRP3 and CTRP5 levels were markedly decreased in patients with T2DM compared to controls (p<0.05) and inversely associated with T2DM. Furthermore, mutivariate regression and ROC analysis revealed CTRP3 deficiency, not CTRP5, was associated with proliferative diabetic retinopathy (PDR). Spearman’s rank correlation assay demonstrated an inverse association between CTRP3 and sVCAM-1. Finally, exogenous CTRP3 administration attenuated high glucose high lipid (HGHL)-induced VCAM-1 production in an AMPK-dependent manner in cultured human retinal microvascular endothelial cells (HRMECs). Conclusion: CTRP3 may serve as a novel biomarker for DR severity. CTRP3 may represent a future novel therapeutic against DR, a common ocular complication of diabetes. [ABSTRACT FROM AUTHOR]

• A property in the iterative process of FPC is discovered. • A novel initialization method of FPC is proposed. • A strategy to improve the efficiency of FPC is given. • The method makes the recommendation algorithm have a high convergence speed. In recent years, the problem of matrix completion based on rank minimization has received widespread attention in machine learning. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Fixed point continuation (FPC), as a representative nuclear norm relaxation matrix completion algorithm, has been proven to perform well in theories and experiments. However, the traditional FPC algorithm initializes the matrix to be completed by zero, which does not make full use of the shrinkage characteristics of the singular value shrinkage operator on the matrix elements and the known field data information, then will lead to slow convergence and poor accuracy. Aiming at this problem, this paper analyzes the shrinkage properties of matrix elements in the iterative process of the FPC algorithm. Combined with the known rating information in the recommendation systems, a new initialization method of overestimation based on FPC is proposed, and it is applied to the rating prediction in the recommendation systems. The experimental results show that the initialization method proposed in this paper greatly improves the algorithm efficiency and prediction accuracy. [ABSTRACT FROM AUTHOR]

• A fast weighted CP decomposition model for context-aware recommendation. • Unifying explicit and implicit feedback by assigning non-uniform weights. • Reducing computation time by element-wise alternating least squares. • A simple and effective multi-threaded computing scheme for above model. As an effective method for high-dimensional information processing, tensor factorization has been widely used in context-aware recommendation. However, the multi-dimensional processing of tensor factorization usually leads to more serious data sparsity. Making full use of the data in recommendation systems, including a small amount of explicit rating data and a large amount of implicit feedback data such as clicks, plays, and purchases, can effectively alleviate this problem. Meanwhile, the processing of sparse explicit data and huge implicit feedback data makes tensor factorization face a severe challenge of computational efficiency. Therefore, this study proposes a fast tensor factorization model named FWCP that unifies explicit and implicit feedback information, which mainly studies as follows: (1) Assigning non-uniform weights to observed and unobserved values to unify explicit and implicit feedback information; (2) Memorizing intermediate data which generated by element-wise alternating least squares(ALS), and implementing parallel optimization of FWCP, which can reduce the training time of tensor factorization based on whole-data. The evaluation results on two real datasets showed that FWCP proposed in this study is superior to other context-aware recommendation algorithms. [ABSTRACT FROM AUTHOR]

• A novel GCN-based traffic prediction model is proposed. • The model enables dynamic spatial–temporal modeling of traffic data. • This model has superior long-term forecasting capability. • Hidden spatial relationships in traffic data were captured. In recent years, spatial–temporal graph modeling based on graph convolutional neural networks (GCN) has become an effective method for mining spatial–temporal dependencies in traffic forecasting research. However, existing studies lack the capability of dynamic spatial–temporal modeling of traffic speeds. Furthermore, long-term forecasting is difficult because of the diversity of traffic conditions. In addition, traditional studies capture only the features of fixed graph structures, which do not reflect real spatial dependence. To address these challenges, this study proposes a novel attention-based dynamic spatial–temporal graph convolutional network (ADSTGCN) model. ADSTGCN mainly consists of multiple dynamic spatial–temporal blocks, each of which contains three modules: 1) a dynamic adjustment module to model the dynamic spatial–temporal correlations of traffic speed, 2) a gated dilated convolution module to mine long-term dependencies, and 3) a spatial convolution module to capture hidden spatial dependencies. Experiments on three public traffic datasets demonstrated the good performance of the model. [ABSTRACT FROM AUTHOR]

Background: Myocardial insulin resistance is a hallmark of diabetic cardiac injury. However, the underlying molecular mechanisms remain unclear. Recent studies demonstrate that the diabetic heart is resistant to other cardioprotective interventions, including adiponectin and preconditioning. The "universal" resistance to multiple therapeutic interventions suggests impairment of the requisite molecule(s) involved in broad prosurvival signaling cascades. Cav (Caveolin) is a scaffolding protein coordinating transmembrane signaling transduction. However, the role of Cav3 in diabetic impairment of cardiac protective signaling and diabetic ischemic heart failure is unknown. Methods: Wild-type and gene-manipulated mice were fed a normal diet or high-fat diet for 2 to 12 weeks and subjected to myocardial ischemia and reperfusion. Insulin cardioprotection was determined. Results: Compared with the normal diet group, the cardioprotective effect of insulin was significantly blunted as early as 4 weeks of high-fat diet feeding (prediabetes), a time point where expression levels of insulin-signaling molecules remained unchanged. However, Cav3/insulin receptor-β complex formation was significantly reduced. Among multiple posttranslational modifications altering protein/protein interaction, Cav3 (not insulin receptor-β) tyrosine nitration is prominent in the prediabetic heart. Treatment of cardiomyocytes with 5-amino-3-(4-morpholinyl)-1,2,3-oxadiazolium chloride reduced the signalsome complex and blocked insulin transmembrane signaling. Mass spectrometry identified Tyr73 as the Cav3 nitration site. Phenylalanine substitution of Tyr73 (Cav3Y73F) abolished 5-amino-3-(4-morpholinyl)-1,2,3-oxadiazolium chloride–induced Cav3 nitration, restored Cav3/insulin receptor-β complex, and rescued insulin transmembrane signaling. It is most important that adeno-associated virus 9–mediated cardiomyocyte-specific Cav3Y73F reexpression blocked high-fat diet–induced Cav3 nitration, preserved Cav3 signalsome integrity, restored transmembrane signaling, and rescued insulin-protective action against ischemic heart failure. Last, diabetic nitrative modification of Cav3 at Tyr73 also reduced Cav3/AdipoR1 complex formation and blocked adiponectin cardioprotective signaling. Conclusions: Nitration of Cav3 at Tyr73 and resultant signal complex dissociation results in cardiac insulin/adiponectin resistance in the prediabetic heart, contributing to ischemic heart failure progression. Early interventions preserving Cav3-centered signalsome integrity is an effective novel strategy against diabetic exacerbation of ischemic heart failure. [ABSTRACT FROM AUTHOR]

In this paper, we provide a systematic characterization of finite BZMVdM-algebra by using semi-tensor product of matrices. The abstract operation law about logic of the finite algebra is transformed into the simple operation of concrete logical matrices. In addition, we study some properties of BZMVdM-algebra, such as homomorphism, isomorphism, and the product of the BZMVdM-algebra. Through logical matrix operation, the direct verifiable conditions for detecting the above properties are given. [ABSTRACT FROM AUTHOR]

SiC/Cu/Gr composites with a boundary of SiO 2 –Cu 2 O glass phase (GP) were manufactured through the hot pressing sintering process at a temperature of 950 °C. The influence of graphite (Gr) on the mechanical properties and tribological behavior under high-temperature were purposefully investigated. The microhardness of 1.37 ± 0.02 GPa is achieved by SiC/Cu/Gr composites with a graphite content of 1 vol%. The friction coefficient decreases significantly to 0.64 ± 0.02, while the average wear rate is as low as 2.2 × 10−3±0.02 mm3/(N·m) at a friction temperature of 500 °C. The results indicate that the excellent lubrication effect of graphite is utilized in the SiC/Cu/Gr composites, leading to a significant improvement in its high-temperature wear resistance performance. This behavior is attributed to the molten state of GP at high temperatures, which acts as a "binder" promoting high interface bonding strength between particles, coupled with the solid-phase lubrication provided by graphite. This research work provides a direction for developing and applying materials conducive to high-temperature contact conductivity. [ABSTRACT FROM AUTHOR]

In this paper, we consider the forward rounding errors of the partial pivoting quaternion LU decomposition. To carry out error analysis of algorithms in quaternion arithmetic, we first propose the accuracy model for the basic quaternion arithmetic operations by the real structure-preserving method. And then, we study the forward rounding errors of the partial pivoting quaternion LU decomposition. Finally, by using the results of the forward rounding errors of the partial pivoting quaternion LU decomposition, we analyze the forward rounding errors of the nonsingular quaternion linear system. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In recent years, context information has been widely used in recommender systems. Tensor factorization is an effective method to process high-dimensional information. However, data sparsity is more serious in tensor factorization, and it is difficult to build a more accurate recommender system only based on user–item–context interaction information. Making full use of user's social information and implicit feedback can alleviate this problem. In this paper, we propose a new tensor factorization model named TrustTF, which mainly works as follows: (1) Using user's social trust information and implicit feedback to extend the bias tensor factorization (BiasTF), effectively alleviate data sparsity problem and improve the recommendation accuracy; (2) Dividing user's trust relationship into unilateral trust and mutual trust, which makes better use of user's social information. To our knowledge, this is the first work to consider the effects of both user trust and implicit feedback on the basis of the BiasTF model. The experimental results in two real-world data sets demonstrate that the TrustTF proposed in this paper can achieve higher accuracy than BiasTF and other social recommendation methods. [ABSTRACT FROM AUTHOR]

• Conservative discontinuous Galerkin schemes for the nonlinear Serre equations. • The well-balanced property of the scheme for the equations with non-flat bottoms. • Hamiltonian conservative scheme based on the equations in non-conservative form. • Arbitrary high order accuracy and capability of the schemes are verified numerically. In this paper, we develop three conservative discontinuous Galerkin (DG) schemes for the one-dimensional nonlinear dispersive Serre equations, including two conserved schemes for the equations in conservative form and a Hamiltonian conserved scheme for the equations in non-conservative form. One of the schemes owns the well-balanced property via constructing a high order approximation to the source term for the Serre equations with a non-flat bottom topography. By virtue of the Hamiltonian structure of the Serre equations, we introduce an Hamiltonian invariant and then develop a DG scheme which can preserve the discrete version of such an invariant. Numerical experiments in different cases are performed to verify the accuracy and capability of these DG schemes for solving the Serre equations. [ABSTRACT FROM AUTHOR]

Recently, the artificial intelligence technologies have been widely used in the field of pipeline integrity management. When crossing mountains, pipelines would inevitably encounter rockfall impact, which will potentially affect the growth of crack. However, previous research barely investigated the effect of sudden rockfall impact on health management of pipelines with fatigue cracks. To overcome this limitation, a novel crack propagation prediction algorithm based is proposed for pipelines subjected to rockfall impact. The stress intensity factor (SIF) rockfall impact ratio is introduced to describe the interaction effect of rockfall on the fatigue crack of pipelines. And the dynamic SIF values are acquired by finite element modeling (FEM) where 354 models with different parameters are analyzed. To more accurately forecast the crack growth under the rockfall impact, a method integrates multilayer perceptron (MLP) with Paris' law is proposed based on the above reliable database. Two parameters impacting the performance of the network including the number of neurons in the hidden layer and the hidden layer's activation function are evaluated and network with the most precise prediction results is selected. Quantitative analyses are performed for key factors including rockfall mass, impact velocity, impact position and crack size. The prediction results using dynamic SIF values are compared with the static ones to indicate the effect of rockfall on the crack propagation. The proposed method is valuable to support decision-making in pipeline reliability assessment and integrity management. [ABSTRACT FROM AUTHOR]

Background: The era of high throughput sequencing offers new paths to identifying species boundaries that are complementary to traditional morphology-based delimitations. De novo species delimitation using traditional or DNA super-barcodes serve as efficient approaches to recognizing putative species (molecular operational taxonomic units, MOTUs). Tea plants (Camellia sect. Thea) form a group of morphologically similar species with significant economic value, providing the raw material for tea, which is the most popular nonalcoholic caffeine-containing beverage in the world. Taxonomic challenges have arisen from vague species boundaries in this group. Results: Based on the most comprehensive sampling of C. sect. Thea by far (165 individuals of 39 morphospecies), we applied three de novo species delimitation methods (ASAP, PTP, and mPTP) using plastome data to provide an independent evaluation of morphology-based species boundaries in tea plants. Comparing MOTU partitions with morphospecies, we particularly tested the congruence of MOTUs resulting from different methods. We recognized 28 consensus MOTUs within C. sect. Thea, while tentatively suggesting that 11 morphospecies be discarded. Ten of the 28 consensus MOTUs were uncovered as morphospecies complexes in need of further study integrating other evidence. Our results also showed a strong imbalance among the analyzed MOTUs in terms of the number of molecular diagnostic characters. Conclusion: This study serves as a solid step forward for recognizing the underlying species boundaries of tea plants, providing a needed evidence-based framework for the utilization and conservation of this economically important plant group. [ABSTRACT FROM AUTHOR]

Passiflora is a plant genus known for its extremely distinctive and colorful flowers and a wide range of genome size variation. However, how genome characteristics are related to flower traits among Passiflora species remains poorly understood. Here, we assembled a chromosome-scale genome of P. foetida, which belongs to the same subgenus as the commercial passionfruit P. edulis. The genome of P. foetida is smaller (424.16 Mb) and contains fewer copies of long terminal repeat retrotransposons (LTR-RTs). The disparity in LTR-RTs is one of the main contributors to the differences in genome sizes between these two species and possibly in floral traits. Additionally, we observed variation in insertion times and copy numbers of LTR-RTs across different transposable element (TE) lineages. Then, by integrating transcriptomic data from 33 samples (eight floral organs and flower buds at three developmental stages) with phylogenomic and metabolomic data, we conducted an in-depth analysis of the expression, phylogeny, and copy number of MIKC-type MADS-box genes and identified essential biosynthetic genes responsible for flower color and scent from glandular bracts and other floral organs. Our study pinpoints LRT-RTs as an important player in genome size variation in Passiflora species and provides insights into future genetic improvement. [ABSTRACT FROM AUTHOR]

Two types of controllability of dynamic-algebraic Boolean control networks are first presented. The necessary and sufficient conditions of such controllability are derived. A similar concept of coordinate transformation called special restricted coordinate transformation is posed. It is sufficient to transform the coordinate frame of a dynamic-algebraic Boolean control network and obtain its corresponding equivalent form. The relevant results about special restricted coordinate transformation are proposed. Accordingly, two kinds of controllability decomposition are discussed and the corresponding algorithms are provided, which differs from that of the conventional Boolean networks. A method is provided to guarantee that all the dynamic equations in controllable subsystem are affected by control. A numerical example and a practical example are raised to illustrate the results of this paper, and the latter one is about the programmable logical circuit. [ABSTRACT FROM AUTHOR]

Diabetes enhances myocardial ischemic/reperfusion (MI/R) injury via an incompletely understood mechanism. Adiponectin (APN) is a cardioprotective adipokine suppressed by diabetes. However, how hypoadiponectinemia exacerbates cardiac injury remains incompletely understood. Dysregulation of miRNAs plays a significant role in disease development. However, whether hypoadiponectinemia alters cardiac miRNA profile, contributing to diabetic heart injury, remains unclear. Methods and Results: Wild-type (WT) and APN knockout (APN-KO) mice were subjected to MI/R. A cardiac microRNA profile was determined. Among 23 miRNAs increased in APN-KO mice following MI/R, miR-449b was most significantly upregulated (3.98-fold over WT mice). Administrating miR-449b mimic increased apoptosis, enlarged infarct size, and impaired cardiac function in WT mice. In contrast, anti-miR-449b decreased apoptosis, reduced infarct size, and improved cardiac function in APN-KO mice. Bioinformatic analysis predicted 73 miR-449b targeting genes, and GO analysis revealed oxidative stress as the top pathway regulated by these genes. Venn analysis followed by luciferase assay identified Nrf-1 and Ucp3 as the two most important miR-449b targets. In vivo administration of anti-miR-449b in APN-KO mice attenuated MI/R-stimulated superoxide overproduction. In vitro experiments demonstrated that high glucose/high lipid and simulated ischemia/reperfusion upregulated miR-449b and inhibited Nrf-1 and Ucp3 expression. These pathological effects were attenuated by anti-miR-449b or Nrf-1 overexpression. In a final attempt to validate our finding in a clinically relevant model, high-fat diet (HFD)-induced diabetic mice were subjected to MI/R and treated with anti-miR-449b or APN. Diabetes significantly increased miR-449b expression and downregulated Nrf-1 and Ucp3 expression. Administration of anti-miR-449b or APN preserved cardiac Nrf-1 expression, reduced cardiac oxidative stress, decreased apoptosis and infarct size, and improved cardiac function. Conclusion: We demonstrated for the first time that hypoadiponectinemia upregulates miR-449b and suppresses Nrf-1/Ucp3 expression, promoting oxidative stress and exacerbating MI/R injury in this population. Dysregulated APN/miR-449b/oxidative stress pathway is a potential therapeutic target against diabetic MI/R injury. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper investigates the controllability and set controllability of periodically switched Boolean control networks (PSBCNs) via the semi-tensor product. Firstly, the algebraic form of switching signal is given and a parallel system is formed by combining the switching signal and the state. Then the controllability problem of PSBCNs is transformed into the set controllability problem of the new system, further, the criterion for controllability is obtained. Secondly, the set controllability matrix of PSBCNs is constructed, and the necessary and sufficient condition of set controllability for PSBCNs is presented. In addition, a more general PSBCN is considered and the criteria for its controllability and set controllability are proposed. Finally, a practical example is given to illustrate the obtained results, which shows that our new results are correct and effective. [ABSTRACT FROM AUTHOR]

Unlike real or complex numbers, quaternion multiplication does not satisfy the commutative law. This makes it impossible to generalize some conclusions on semi‐tensor product to quaternion directly, which is valid on real or complex number fields. In this paper, we define semi‐tensor product of quaternion matrices and study the properties of semi‐tensor product of quaternion matrices, and the conclusion is applied to the color digital image restoration model. [ABSTRACT FROM AUTHOR]

The combustion patterns, characteristics, and kinetics were investigated by thermogravimetric analysis for raw maize straw, cotton stalk, and chars obtained from segmented heating carbonization at 300-800 °C. With increasing carbonization temperature, combustion patterns of biomass chars transform from the sequential reaction steps corresponding to pyrolysis and heterogeneous oxidation of volatiles and char to the in situ heterogeneous oxidation of fixed carbon and volatiles, the ignition temperature of biomass chars gradually increases, the ignition index does not monotonically increase, and the burnout index and combustion characteristic index decrease to different degrees. Judging from the combustion characteristic index, chars obtained from 300 to 500 °C of carbonization show better combustibility. The kinetic parameters of raw and carbonized biomass were determined by Coats-Redfern method. Different reaction mechanisms exist in oxidation processes of different chars, which attribute to the synergistic effects of homogenous oxidation of volatiles and heterogeneous oxidation of char. The kinetic parameters obtained from the variation of species and model functions exhibit kinetic compensation effect. © 2016 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• The low-cycle fatigue (LCF) design rule of pipelines in ASME B31 code is studied. • The structural strain method generalizes ASME B31 design rule for welded pipelines. • The structural strain method can analyze LCF behavior of irregular pipe tee joint. • Different aspects of the structural strain method are discussed in detail. The welded pipe components are prone to low-cycle fatigue (LCF) at welds, especially under extreme loadings. ASME B31.8 specifies the fatigue design rules of the welded pipelines. In this work, the LCF fatigue analysis approach stipulated in the ASME B31 code is revisited to figure out its underlying mechanism, limitations, and scope, by analyzing two sets of LCF fatigue data of piping structures using different strain information. A structural strain method is proposed to generalize the pseudo-stress idea implied in the ASME code, which correlates large amounts of high- and low-cycle fatigue data of weldments into the master E-N curve. [ABSTRACT FROM AUTHOR]

• Circ_0128846 expression was increased in cartilage tissues of OA patients. • IL-1β treatment increased circ_0128846 expression in chondrocytes. • Circ_0128846 depletion assuaged IL-1β-caused chondrocyte dysfunction. • Circ_0128846 targeted miR-940. • MiR-940 combined with PTPN12. Previous evidence has suggested that circular RNA (circRNA) is abnormally expressed in osteoarthritis (OA). However, the underlying mechanism of circRNA in OA progression remains unclear. The study aims to reveal the mechanism of circ_0128846 regulating OA. Human chondrocytes (C28/I2 cells) were treated with interleukin-1β (IL-1β) to mimic an OA cell model. The expression levels of circ_0128846, miR-940 and protein tyrosine phosphatase 12 (PTPN12) were detected by qRT-PCR. Protein expression was checked by Western blotting. Cell viability, proliferation, and apoptosis were analyzed by a cell counting kit-8 assay, 5-Ethynyl-2′-deoxyuridine (EdU) assay and flow cytometry analysis, respectively. The production of tumor necrosis factor-α (TNF-α) and IL-6 was determined by an enzyme-linked immunosorbent assay (ELISA). The binding relationship between miR-940 and circ_0128846 or PTPN12 was identified by dual-luciferase reporter assay and RNA immunoprecipitation assay. Circ_0128846 and PTPN12 expression were significantly upregulated, whereas miR-940 was downregulated in the cartilage tissues of OA patients and IL-1β-treated C28/I2 cells compared with controls. IL-1β treatment inhibited C28/I2 cell proliferation and induced cell apoptosis and the production of inflammatory factors, TNF-α and IL-6; however, these effects were partly reversed after circ_0128846 depletion. In terms of mechanism, circ_0128846 acted as a miR-940 sponge, and miR-940 combined with PTPN12. Also, circ_0128846 depletion partly ameliorated IL-1β-induced C28/I2 cell disorders through miR-940. PTPN12 overexpression also partly relieved miR-940-mediated effects in IL-1β-treated C28/I2 cells. Further, circ_0128846 induced PTPN12 expression by interacting with miR-940. Circ_0128846 regulated human chondrocyte proliferation, apoptosis and inflammation through the miR-940/PTPN12 pathway in OA. [ABSTRACT FROM AUTHOR]

In this paper, we consider the quaternion matrix equation X − A X ˆ B = C , and study its minimal norm least squares solution, j-self-conjugate least-squares solution and anti-j-self-conjugate least-squares solution. By the real representation matrices of quaternion matrices, their particular structure and the properties of Frobenius norm, we convert above least-squares problems into corresponding problems of real matrix equations. The final results of the expressions only involve real matrices, and thus, the corresponding algorithms only involve real operations. Compared with the existing results, they are more convenient and efficient, which are also illustrated by the last two numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we consider the strategy set optimization and payoff optimization for a class of networked evolutionary games (NEGs) with "myopic best response adjustment" by using the semi-tensor product (STP) method, and present a number of new results. Firstly, the dominated strategies are defined, based on which an algorithm for removing dominated strategies is formulated for a given NEG. Secondly, using STP method, the dynamics of the NEG after removing the dominated strategies is converted into an algebraic form. Finally, the payoff optimization problem is considered by adding control players to the game and state feedback controls are designed to steer the game from an initial profile to the optimal profile which can maximize the overall payoff of the whole game. An illustrative example is studied to support our new results. [ABSTRACT FROM AUTHOR]

R2063 --> 861.2 --> Objectives: There is a paradox of nicotine and diabetes in patients with cardiomyopathy. Although great progress has been made in understanding the pathogenesis, it is urgent to clarify the in‐depth mechanism and identify new therapeutic targets for preventing diabetic cardiomyopathy (DCM) in smoker. Methods: Human gene expression profiles in DCM (GSE4745 and GSE99203) corrected by SVA algorithm were analyzed and then "limma" packet gene screening was performed. By constructing the related co‐expression WGCNA network of DCM, combined with the analysis of Metascape pathway, the correlation of module and genes with DCM was determined. Furthermore, the Drug bank and STRING databases were used to screen nicotine targets and expand the gene network. The hub gene was identified by crossing the targets with DCM, which was obtained by WGCNA analysis. r‐packet "clusterprofiler" was used for GO analysis and KEGG pathway analysis. Then, the network of hub genes affecting DCM and the interaction diagram of core target related pathways that influence DCM is constructed by using the Cytoscape software. The relationship between hub genes was analyzed by using "corrplot", "vioplot" packages and cibersort algorithm, and the potential mechanism of hub genes affecting DCM progress was further explored. To explore the relationship between the targets in the process of DCM, ROC curve of diagnostic efficacy was drawn with "pROC" package. Finally, a miRNA‐mRNA regulatory network including hub genes was further constructed by Targetscan. The regulatory network of transcription factors of hub genes was constructed from TRRUST database. Results: 3 gene modules were detected by WGCNA analysis, blue (202), grey (31) and turquoise (397), among which turquoise module had the highest correlation with DCM (cor =0.87, P = (3e −10)). The genes rich in monocarboxylic acid and sulfur compound metabolic processes in response to extracellular stimulus were identified. The top 13 nicotine‐related are targeted. The intersection of 113 target genes and turquoise module gene were screened out. There are the two hub genes involved Iron (III) ion response and retinoic acid biosynthetic process, involving Tryptophan Metabolism and Ovarian steroidogenesis pathways. The AUC values of the two hub genes are Cyp1a1(AUC: 0.769 (0.580 ‐ 0.958)) and Chrnb4(AUC: 0.707 (0.511 ‐ 0.902)) and both genes are strongly correlated with immune cell response. A total of 40 miRNA‐mRNA pairs, including 35 miRNAs and 2 mRNAs, were predicted by Targetscan. Three Cyp1a1 transcription factors were predicted by TRRUST database. Conclusion: We demonstrated that two genes, Chrnb4 and Ccyp1a11, are the potential genes involved in nicotine modulating DCM, and these two genes may regulate DCM's immune response through miRNA‐mRNA mode. It provides new potential targets for preventing DCM in smoking population. [ABSTRACT FROM AUTHOR]

After standard seawalls have been built successfully, fishery ports become the structures most easily damaged during a typhoon. Estimating the risk of fishery ports against typhoons would be useful for identifying weaknesses and implementing corrective measures to protect fishing boats from a typhoon. This study describes a versatile methodology for conducting this type of quantitative assessment at fishery ports. The Dongsha fishery port in Zhejiang Province was selected as a case study to test the results derived from a high-precision Hydrodynamic Flexible Mesh model coupled with the Spectral Wave model. First, typhoon characteristics were assessed based on historical typhoons in the study area, and then, the wind, tide, storm surge, and waves were modeled and tide-surge interactions were investigated. Through comparisons of the destructive parameters from the typhoon assessment with the design and structural parameters of the fishery port, the level of the Dongsha fishery port against typhoons was determined to be 12, and the main weaknesses of the port's defenses were found to be located near feature points T2, T3, T8, and T15. The results obtained demonstrate that the proposed methodology can be used to acquire valuable information on the risk of fishery ports against typhoons. [ABSTRACT FROM AUTHOR]

Coronary microvascular dysfunction (CMD) refers to a subset of structural and/or functional disorders of coronary microcirculation that lead to impaired coronary blood flow and eventually myocardial ischemia. Amid the growing knowledge of the pathophysiological mechanisms and the development of advanced tools for assessment, CMD has emerged as a prevalent cause of a broad spectrum of cardiovascular diseases (CVDs), including obstructive and nonobstructive coronary artery disease, diabetic cardiomyopathy, and heart failure with preserved ejection fraction. Of note, the endothelium exerts vital functions in regulating coronary microvascular and cardiac function. Importantly, insufficient or uncontrolled activation of endothelial autophagy facilitates the pathogenesis of CMD in diverse CVDs. Here, we review the progress in understanding the pathophysiological mechanisms of autophagy in coronary endothelial cells and discuss their potential role in CMD and CVDs. [ABSTRACT FROM AUTHOR]

In this paper, we use semi-tensor product of quaternion matrices, L -representation of quaternion matrices, and GH -representation of special quaternion matrices such as quaternion (anti)-centrosymmetric matrices to solve the special solutions of quaternion matrix equation. Based on semi-tensor product of quaternion matrices and the structure matrix of the multiplication of quaternion, we propose the vector representation operation conclusion of quaternion matrices, and study the different matrix representations of quaternion matrices. Then the problem of the quaternion matrix equation is transformed into the corresponding problem in the real number fields by using vector representation and L -representation of quaternion matrices, combined with the special structure of (anti)-centrosymmetric matrices, the independent elements are extracted by GH -representation method, so as to reduce the number of variables to be calculated and improve the calculation accuracy. Finally, the effectiveness of the method is verified by numerical examples, and the time comparison with the two existing algorithms is carried out. The algorithm in this paper is also applied in a centrosymmetric color digital image restoration model. [ABSTRACT FROM AUTHOR]