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Herein, in order to increase the purity and improve the performance of HMX crude products (Raw HMX) isolated from decommissioned propellants, a new method to obtain submicron levels using the solvent-anti-solvent process was proposed. Two different morphologies of HMX, regular prismatic vs. spherical-like, were obtained by means of two solvents (DMSO, DMF). Polyethylene glycol-200 (PEG-200) was used to improve the morphology and dispersion of crystals at low saturation and low crystallization temperatures. Various rates of addition of anti-solvent (water) were studied to prevent HMX crystalline transition and effectively increase purity. Furthermore, the aging process was applied to further modify the crystals with controlled particle size distribution The purity of the samples was tested by High Performance Liquid Chromatography (HPLC) spectroscopy, and the purity of the crystallized samples was improved from 95.53% in Raw HMX to over 99%. The thermal decomposition properties were investigated by differential scanning calorimetry (DSC) and the kinetic parameters of the thermal decomposition reaction of the materials were calculated using the Kissinger and Ozawa method. The samples were also tested for impact and friction sensitivity, which was reduced compared to both Raw HMX and industrial HMX, demonstrating that this type of modified HMX recovered from decommissioned propellants is fully reusable. [ABSTRACT FROM AUTHOR]

This work focuses on developing a forecasting model for the nonlinear system with herding. Firstly, for clear presentation of the mechanism of herding behavior, a concept called intermittent positive feedback is defined. The mathematical expression of intermittent positive feedback is formulated in terms of dead-zone functions with the beginning and ending thresholds in the positive and negative directions. Then a dynamic surrogate based on the intermittent positive feedback multi-dimensional Taylor network is established, and a method for identifying its parameters is proposed. Multi-dimensional Taylor network module simulates the long-term trends of time series. Subsequently, an optimization model based on the alternating iteration of the parameters is developed to enhance the forecast accuracy of the intermittent positive feedback part. Simulation results demonstrate that the forecasting effect of the proposed modeling method is superior to that of conventional non-mechanism data-based surrogates for herding behavior. [ABSTRACT FROM AUTHOR]

In this paper, a recursive d -step-ahead predictive control scheme based on multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of multiple-input multiple-output (MIMO) nonlinear systems with input time-delay. The MTN predictive model is designed using a recursive approach to compensate the influence of time-delay, and an extended Kalman filter (EKF) is applied as its learning algorithm. An MTN controller is developed based on a proportional–integral–derivative (PID) controller where the closed-loop errors between the reference input and the system output are set as the MTN controller's inputs. Then, a back propagation (BP) algorithm, designed to update its weights according to errors caused by system uncertainty, is used as a learning algorithm for the MTN controller. Meanwhile, the convergence of the MTN predictive model and the stability of the closed-loop system are evaluated. Two numerical examples and a practical example – continuous stirred tank reactor (CSTR) process are presented to verify the superiority of the proposed scheme. The experimental results and the computational complexity analysis show that the proposed scheme is effective, promising its desirable robustness, anti-disturbance, tracking and real-time performance. [ABSTRACT FROM AUTHOR]

Control of stochastic nonlinear systems turns out to be notoriously difficult when stochastic uncertainties and time‐varying delays occur simultaneously. This article presents a tractable adaptive control scheme for the stochastic nonlinear system with time‐varying delays. To mitigate the effects of stochastic uncertainties, an adaptive embedded cubature Kalman filter is developed to realize the robust estimation of the state. Unlike the conventional cubature Kalman filter with fixed construction, a semi‐definite programming is designed to adjust the weights of cubature points dynamically. Such programming guarantees the positive definiteness of the error covariance matrix, which enhances the reliability of the filtering procedure. Based on more accurate state estimations, the multidimensional Taylor network (MTN) is utilized to evaluate the dynamic performance under time‐varying delays and approximate the optimal policy in the deterministic policy gradient framework. Adaptive tracking control with high computational efficiency is achieved due to the concise topological structure of MTN. The exponential convergence of the state estimation error and the semi‐globally uniform ultimate boundness of the tracking error are verified theoretically. The effectiveness of the proposed method is confirmed by a numerical simulation based on a practical electric industrial system. [ABSTRACT FROM AUTHOR]

Urolithiasis, referred to as the formation of stones in the urinary tract, is a common disease with growing prevalence and high recurrence rate worldwide. Although researchers have endeavoured to explore the mechanism of urinary stone formation for novel effective therapeutic and preventative measures, the exact aetiology and pathogenesis remain unclear. Propelled by sequencing technologies and culturomics, great advances have been made in understanding the pivotal contribution of the human microbiome to urolithiasis. Indeed, there are diverse and abundant microbes interacting with the host in the urinary tract, overturning the dogma that urinary system, and urine are sterile. The urinary microbiome of stone formers was clearly distinct from healthy individuals. Besides, dysbiosis of the intestinal microbiome appears to be involved in stone formation through the gut-kidney axis. Thus, the human microbiome has potential significant implications for the aetiology of urolithiasis, providing a novel insight into diagnostic, therapeutic, and prognostic strategies. Herein, we review and summarize the landmark microbiome studies in urolithiasis and identify therapeutic implications, challenges, and future perspectives in this rapidly evolving field. To conclude, a new front has opened with the evidence for a microbial role in stone formation, offering potential applications in the prevention, and treatment of urolithiasis. [ABSTRACT FROM AUTHOR]

Previously, 36 TEAM benchmark problems have been established, used to verify the numerical calculation methods of electromagnetic fields. Among them, there is only one problem related to the magnetic field considering magnetic hysteresis, which is problem 32. The results of it are obtained by experimental measurement. In this paper, a same kind of benchmark problem is proposed, in which the field results are obtained by using analytical method. This benchmark problem can be used to verify both 2D and 3D numerical calculation methods. The analytical solving process of the problem is that H is obtained by using Ampere's law, then B is calculated based on the hysteresis model of the material, so that the field distribution results are formed. The required hysteresis model could be any kind, such as Preisach model, J–A model, etc. As long as the same kind of model is used both in the benchmark problem and in the numerical method, the numerical method can be verified through the proposed benchmark problem. Considering the form of J–A hysteresis model required by the benchmark problem does not exist, which is the field separation-based forward dynamic J–A model, it is derived based on the forward static J–A model. [ABSTRACT FROM AUTHOR]

Background: The pathogenesis of kidney stone disease (KSD) is not fully understood, and potential contributing factors remain to be explored. Several studies have revealed that the urinary microbiome (urobiome) of stone formers was distinct from that of healthy individuals using 16S rRNA gene sequencing, most of which only provided microbial identification at the genus level. 2bRAD sequencing for Microbiome (2bRAD-M) is a novel sequencing technique that enables accurate characterization of the low-biomass microbiome at the species resolution. We aimed to apply 2bRAD-M to profile the renal pelvis urobiome of unilateral kidney stone patients and compared the urobiome with and without stone(s).Method: A total of 30 patients with unilateral stones were recruited, and their renal pelvis urine from both sides was collected. A ureteroscope was inserted into the renal pelvis with stone(s) and a ureteral catheter was placed into the ureteroscope to collect renal pelvis urine. This procedure was repeated again with new devices to collect the urine of the other side. 2bRAD-M was performed to characterize the renal pelvis urobiome of unilateral stone formers to explore whether microbial differences existed between the stone side and the non-stone side.Results: The microbial community composition of the stone side was similar to that of the non-stone side. Paired comparison showed that Corynebacterium was increased and Prevotella and Lactobacillus were decreased in the stone side. Four species (Prevotella bivia, Lactobacillus iners, Corynebacterium aurimucosum, and Pseudomonas sp_286) were overrepresented in the non-stone side. 24 differential taxa were also identified between two groups by linear discriminant analysis effect size (LEfSe). Extensive and close connections among genera and species were observed in the correlation analysis. Moreover, a random forest classifier was constructed using specific enriched species, which can distinguish the stone side from the non-stone side with an accuracy of 71.2%.Conclusion: This first 2bRAD-M microbiome survey gave an important hint towards the potential role of urinary dysbiosis in KSD and provided a better understanding of mechanism of stone formation. [ABSTRACT FROM AUTHOR]

Background. Bladder cancer is one of the most common malignancies of the urinary system with an unfavorable prognosis. More and more studies have suggested that lipid metabolism could influence the progression and treatment of tumors. However, there are few studies exploring the relationship between lipid metabolism and bladder cancer. This study aimed to explore the roles that lipid metabolism-related genes play in patients with bladder cancer. Methods. TCGA_BLCA cohort and GSE13507 cohort were included in this study, and transcriptional and somatic mutation profiles of 309 lipid metabolism-related genes were analyzed to discover the critical lipid metabolism-related genes in the incurrence and progression of bladder cancer. Furthermore, the TCGA_BLCA cohort was randomly divided into training set and validation set, and the GSE13507 cohort was served as an external independent validation set. We performed the LASSO regression and multivariate Cox regression in training set to develop a prognostic signature and further verified this signature in TCGA_BLCA validation set and GSE13507 external validation set. Finally, we systematically investigated the association between this signature and tumor microenvironment, drug response, and potential functions and then verified the differential expression status of signature genes in the protein level by immunohistochemistry. Results. A novel 6-lipidmetabolism-related gene signature was identified and validated, and this risk score model could predict the prognosis of patients with bladder cancer. In addition, the prognostic model was tightly related to immune cell infiltration and tumor mutation burden. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) showed that mTOR signaling pathway, G2M checkpoint, fatty acid metabolism, and hypoxia were enriched in patients in the high-risk score groups. Furthermore, 3 therapies specific for bladder cancer patients in different risk scores were identified. Conclusions. In conclusion, we investigated the lipid metabolism-related genes in bladder cancer through comprehensive bioinformatic analysis. A novel 6-gene signature associated with lipid metabolism for predicting the outcomes of patients with bladder cancer was conducted and validated. Furthermore, the risk score model could be utilized to indicate the choice of therapy in bladder cancer. [ABSTRACT FROM AUTHOR]

Control input acting on the system in the form of nonlinear implicit tends to exert great impacts on the dynamic behavior of the multiple-input–multiple-output (MIMO) nonaffine nonlinear system with actuator saturation, posing a great challenge to its tracking control and dynamic regulation. Performance optimization and design simplicity are hardly taken simultaneously into account by existing controllers. For this sake, the multidimensional Taylor network (MTN) controller, which involves both transient improvement and design simplicity, is proposed here. The MTN controller, as a novel one with a fixed structure, is capable of making the proposed system asymptotically track the reference with no need of system information except for the control direction. A performance index related to the transient improvement by dynamic regulation is established. Parameters of the MTN controller are adapted for the optimization problem in line with this performance index of the arbitrary approximation by the polynomial function. The existence and the realizability of the solution to the optimization problem are demonstrated. Specific steps of the controller design are given. The effectiveness of the proposed controller is confirmed by its theoretical analysis as well as its application to a two-link manipulator system. [ABSTRACT FROM AUTHOR]

• We numerically show that an ultra-thin Weyl semimetal film can achieve an excellent performance of a high polarization extinction ratio (more than 40 dB) and a low insertion loss (less than 0.07 dB). • Monolayer polarizer shows that the optimum insertion loss is not achieved in the band with the highest PER. An isotropic substrate has little effect on the TM mode, while effectively adjusts the position of TE mode transmission peak. Thus, the proposed sandwich structure can further reduce insertion loss (from 0.06 dB to 0.03 dB) and obtain wider acceptances range of angle (from 49. 5 ∘ to 71 ∘). • Due to the high reflectivity of the two interfaces inside the WSM layer, the incident light is reflected several times in the single-layer structure, forming a structure similar to the Fabry-Perot cavity, which seriously affects the polarizer's performance. To overcome the challenges above, the proposed a gradient Fermi level structure eliminates the interference peak, thus resulting a wider bandwidth (approximately 60 % improvement). This method can be generalized to larger thickness cases. • With the increasing demand for on-chip integrated devices, our work can be helpful for the integration of a range of potential optical applications in optical communication and omnidirectional polarization detectors. Recently, Weyl semimetals have been extensively studied due to their unique optical responses, especially in the mid-infrared region. However, the possibility of developing optical polarizers based on Weyl semimetals remains unexplored. In this paper, under the Voigt geometry, we numerically show that an ultra-thin Weyl semimetal film with thickness L = 100 nm can achieve an impressive polarization extinction ratio (more than 40 dB) with a wide acceptance angle. Further, the proposed sandwich structure can effectively adjust the position of the TE mode transmission peak alone, thus obtaining lower insertion loss (from 0.06 dB to 0.03 dB) and wider acceptance range of angle (from 49. 5 ∘ to 71 ∘). The proposed gradient Fermi level structure eliminates the Fabry-Perot cavity resonance, thus resulting a wider bandwidth (approximately 60 % improvement). Our proposed ultra-thin and wide-angle polarizer may have potential applications in optical communication and omnidirectional polarization detectors. [ABSTRACT FROM AUTHOR]

To improve enterprise financial early warning, we propose an algorithm based on a decision tree. According to the shortcomings and defects of the classical algorithm and the traditional decision tree algorithm, in the ordinary decision tree improved algorithm based on PCA, there is a problem that the representativeness of the data after dimensionality reduction processing are not high, resulting in the fact that the accuracy of the algorithm can be improved slightly after multiple data runs. Based on the classical algorithm, attribute eigenvalues before classification are extracted twice, and the amount of data to be classified is calculated. That is, the most important attributes of the original data are selected. After the subtree is established, the dimension reduction and merging selection of the data are performed, and the improved algorithm is verified by using three data sets in the UCI database. The results show that the average accuracy in the three datasets is 94.6%, which is improved by 1.6% and 0.6% for the traditional classical algorithm and the ordinary PCA decision tree optimization algorithm, respectively. PCA-based decision tree algorithms can improve the accuracy of the results to some extent, which is of practical importance. In the future, a classic algorithm improved for secondary modeling will be used to obtain a more efficient decision tree model. The decision tree algorithm has been proven to recognize an early warning of an enterprise's financial risks, which enhances the effectiveness of an enterprise's early financial warning. [ABSTRACT FROM AUTHOR]

An adaptive optimization scheduling AQ (Assembly Q-learning) algorithm of knowledgeable manufacture is proposed for an aero-engine assembly line to address the scheduling of an aero-engine assembly workshop in an uncertain manufacture system, which combines the real-time feature of Q-learning with the self-adaptation feature of knowledgeable manufacture system. An adaptive scheduling model of aero-engine assembly based on Q-learning is developed for the purpose of minimizing earliness penalty and completion time cost. New production scheduling rules are proposed to deal with the aeroengine assembly scheduling problem. Addressing the characteristics of aero-engine assembly, four system-state features are defined, and a proper reward is designed as a reward function. Coherence of the reward function and the scheduling objective is proved by theorem. Simulation tests show that the developed algorithm is superior to other scheduling rules in various situations. Particularly, in the ever-changing assembling environment, better results are guaranteed by the desirable adaptivity of the proposed algorithm. An adaptive optimization scheduling algorithm of an aero-engine assembly workshop in a knowledgeable environment is designed, whereby a problem-solving method based on Q-learning and process simulation is activated. The coherence of the reward function and the scheduling objective is proven by theorem. The developed algorithm is superior to the other scheduling rules in the vast majority of cases. Thus, through self-learning, the aero-engine assembly workshop has much higher efficiency and adaptability than the others. [ABSTRACT FROM AUTHOR]

For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this study, an adaptive predictive control approach based on the multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of single-input single-output nonlinear systems with input time-delay. Two MTNs are used to implement the accurate tracking control. First, to compensate for the influence of time-delay, MTN is taken as a predictor and the damped recursive least squares algorithm is used as its online learning algorithm. Second, a feed-forward MTN controller is developed on the basis of the proportional–integral–derivative controller, and the closed-loop errors between the reference input and the system output are directly chosen to be the MTN controller's inputs. The back propagation algorithm is introduced for its learning which can update its weights online at stable learning rate by the errors caused by the system's uncertain factors. Convergence and stability analysis are given to guarantee the performance of our proposed approach. Finally, two examples are given to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: Kidney stone disease (KSD) is a multifactorial disease involving both environmental and genetic factors, whose pathogenesis remains unclear. This study aims to explore the hub genes related to stone formation that could serve as potential therapeutic targets. Methods: Based on the GSE73680 dataset with 62 samples, differentially expressed genes (DEGs) between Randall's plaque (RP) tissues and normal tissues were screened and weighted gene co-expression network analysis (WGCNA) was applied to identify key modules associated with KSD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological functions. The protein–protein interaction (PPI) network was constructed to identify hub genes. Meanwhile, CIBERSORT and ssGSEA analysis were used to estimate the infiltration level of the immune cells. The correlations between hub genes and immune infiltration levels were also investigated. Finally, the top hub gene was selected for further GSEA analysis. Results: A total of 116 DEGs, including 73 up-regulated and 43 down-regulated genes, were screened in the dataset. The red module was identified as the key module correlated with KSD. 53 genes were obtained for functional enrichment analysis by taking the intersection of DEGs and genes in the red module. GO analysis showed that these genes were mainly involved in extracellular matrix organization (ECM) and extracellular structure organization, and others. KEGG analysis revealed that the pathways of aldosterone-regulated sodium reabsorption, cell adhesion molecules, arachidonic acid (AA) metabolism, and ECM-receptor interaction were enriched. Through PPI network construction, 30 hub genes were identified. CIBERSORT analysis revealed a significantly increased proportion of M0 macrophages, while ssGSEA revealed no significant differences. Among these hub genes, SPP1, LCN2, MMP7, MUC1, SCNN1A, CLU, SLP1, LAMC2, and CYSLTR2 were positively correlated with macrophages infiltration. GSEA analysis found that positive regulation of JNK activity was enriched in RP tissues with high SPP1 expression, while negative regulation of IL-1β production was enriched in the low-SPP1 subgroup. Conclusions: There are 30 hub genes associated with KSD, among which SPP1 is the top hub gene with the most extensive links with other hub genes. SPP1 might play a pivotal role in the pathogenesis of KSD, which is expected to become a potential therapeutic target, while its interaction with macrophages in KSD needs further investigation. [ABSTRACT FROM AUTHOR]

In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control. [ABSTRACT FROM AUTHOR]

This study is concerned with the robust model predictive control (MPC) based on recurrent multi‐dimensional Taylor network (RMTN) for the discrete‐time non‐linear time‐delay systems. Regarding the MPC algorithm for the discrete‐time time‐delay systems, the existing literature only considers the linear case. In this study, by designing the suitable terminal cost and terminal region, the MPC scheme is firstly investigated for the non‐linear case. Meanwhile, to reduce the computational burden of MPC using the existing model types (e.g. recurrent neural network) as the identified model, an RMTN possessing the concise structure and high computational efficiency is constructed to approximate the state‐space model of the system. After trained by the backpropagation through time algorithm, the RMTN obtaining the high accuracy of prediction over a long‐range horizon is capable of serving as the prediction model in the MPC scheme. Furthermore, aiming at alleviating the adverse effect of the inevitable identification error, the tube‐based MPC is proposed via leveraging dual‐mode MPC to guarantee that actual trajectory is contained within a robust tube. The stability of the considered system is proved theoretically, and numerical simulation is employed to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an inverse control scheme based on the novel dynamic network (multi-dimensional Taylor network (MTN)) is proposed for the real-time tracking control of nonlinear time-varying systems with noise disturbances. Utilized in this scheme are the three MTNs: the adaptive model identifier for system modeling, the adaptive inverse controller for inverse modeling, and the adaptive nonlinear filter for eliminating the noise disturbances, whose weights are modified by the variable forgetting factor recursive least squares (VFF-RLS), back propagation through model (BPTM), normalized least mean square (NLMS) algorithms, respectively. To avoid "compromise", this scheme is designed into a structure wherein controlling the object dynamic response and eliminating the noise disturbances are implemented in two relatively independent processes. Furthermore, the weight-elimination algorithm is introduced for choice of effective regression items to avoid the dimension explosion, thus overcoming the shortcoming that the number of middle nodes needs to be determined before using the traditional neural network. After a certain number of training, the more streamlined MTNs are observed to contribute to satisfying the real-time requirements of software implementation and engineering application. To ensure that MTN inverse control is strict in theory, the general conditions for the existence of single-input/single-output (SISO) nonlinear inverse systems are identified. Simulation of the MTN inverse control is conducted to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of multi-dimensional Taylor network (MTN) decentralised adaptive output-feedback tracking control is investigated for large-scale stochastic nonlinear systems. MTNs are used to approximate the unknown nonlinear functions, and a MTN state observer is designed for estimating the unmeasured states. Based on the designed MTN state observer, and by combining the backstepping technique with dynamic surface control (DSC), an adaptive MTN decentralised output-feedback tracking control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability, and the tracking errors converge to an arbitrarily small neighbourhood around the origin in the sense of mean quartic value. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach, and the simulation results demonstrate that the proposed method promises desirable real-time performance and tracking control. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive control approach based on the multidimensional Taylor network (MTN) is proposed here for the real‐time tracking control of multiple‐input–multiple‐output (MIMO) time‐varying uncertain nonlinear systems with noises. Two MTNs are used to formulate the optimum control and adaptive filtering approaches. The feed‐forward MTN controller (MTNC) is developed to realize the precise tracking control. The closed‐loop errors between the filtered outputs and expected values are directly chosen as the MTNC's inputs. A valid initial value selection scheme for the weights of the MTNC, which can ensure the initial stability of adaptive process, is introduced. The proposed MTNC can update its weights online according to errors caused by system's uncertain factors, based on stable learning rate. The resilient backpropagation algorithm and the adaptive variable step size algorithm via linear reinforcement are utilized to update the MTNC's weights. The MTN filter (MTNF) is developed to eliminate measurement noises and other stochastic factors. The proposed adaptive MTN filtering system possesses the distinctive properties of the Lyapunov theory–based adaptive filtering system and MTN. Lyapunov function of the filtering errors between the measured values and MTNF's outputs is defined. By properly choosing the weights update law in the Lyapunov sense, the MTNF's outputs can asymptotically converge to the desired signals. The design is independent of the stochastic properties of the input disturbances. Simulation of the MTN‐based control is conducted to test the effectiveness of the presented results. [ABSTRACT FROM AUTHOR]

Calcium oxalate (CaOx) stones are the most prevalent type of kidney stones. CaOx crystals can stimulate reactive oxygen species (ROS) generation and induce renal oxidative stress to promote stone formation. Intracellular Ca2+ is an important signaling molecule, and an elevation of cytoplasmic Ca2+ levels could trigger oxidative stress. Our previous study has revealed that upregulation of Ang II/AT1R promoted renal oxidative stress during CaOx exposure. IP3/IP3R/Ca2+ signaling pathway activated via Ang II/AT1R is involved in several diseases, but its role in stone formation has not been reported. Herein, we focus on the role of AT1R/IP3/IP3R-mediated Ca2+ release in CaOx crystals-induced oxidative stress and explore whether inhibition of this pathway could alleviate renal oxidative stress. NRK-52E cells were exposed to CaOx crystals pretreated with AT1R inhibitor losartan or IP3R inhibitor 2-APB, and glyoxylic acid monohydrate-induced CaOx stone-forming rats were treated with losartan or 2-APB. The intracellular Ca2+ levels, ROS levels, oxidative stress indexes, and the gene expression of this pathway were detected. Our results showed that CaOx crystals activated AT1R to promote IP3/IP3R-mediated Ca2+ release, leading to increased cytoplasmic Ca2+ levels. The Ca2+ elevation was able to stimulate NOX2 and NOX4 to generate ROS, induce oxidative stress, and upregulate the expression of stone-related proteins. 2-APB and losartan reversed the referred effects, reduced CaOx crystals deposition and alleviated tissue injury in the rat kidneys. In summary, our results indicated that CaOx crystals promoted renal oxidative stress by activating the AT1R/IP3/IP3R/Ca2+ pathway. Inhibition of AT1R/IP3/IP3R-mediated Ca2+ release protected against CaOx crystals-induced renal oxidative stress. 2-APB and losartan might be promising preventive and therapeutic agents for the treatment of kidney stone disease. • The interaction between CaOx crystals and RTECs is an important step in stone formation. • CaOx crystals activate AT1R/IP3/IP3R-mediated Ca2+ release to induce renal oxidative stress. • Inhibition of AT1R/IP3/IP3R/Ca2+ protects against CaOx crystals-induced renal oxidative stress. • Losartan and 2-APB are promising agents for the treatment of kidney stone disease. [ABSTRACT FROM AUTHOR]

Design time forecast is characterized by problems of small samples and heteroscedastic noise. For better control of product development process, the forecast method is expected to yield not only highly precise forecast values but valid forecast variances. For this end, relative entropy kernel regression (REKR) is proposed based on the combination of kernel-based regression and Gaussian margin machines (GMM). REKR maintains a Gaussian distribution over weight vectors for the kernel-based regression to minimize the combination of the relative entropy and the negative log probability densities of the targets. To simplify the formulation of REKR, the covariance matrix of Gaussian distribution is set as a diagonal matrix. The simplified optimization problem of REKR is solved based on particle swarm optimization (PSO). Having inherited the benefits of GMM, REKR can simultaneously offer the desirable forecast value and forecast variance. Experiments on the time forecast of plastic injection mold design by both synthetic and real datasets do verify the feasibility and validity of REKR. [ABSTRACT FROM AUTHOR]

Summary This paper investigates the problem of adaptive multi‐dimensional Taylor network (MTN) decentralized tracking control for large‐scale stochastic nonlinear systems. Minimizing the influence of randomness and complex nonlinearity, which increases computational complexity, and improving the controller's real‐time performance for the stochastic nonlinear system are of great significance. With combining adaptive backstepping with dynamic surface control, a decentralized adaptive MTN tracking control approach is developed. In the controller design, MTNs are used to approximate nonlinearities, the backstepping technique is employed to construct the decentralized adaptive MTN controller, and the dynamic surface control technique is adopted to avoid the "explosion of computational complexity" in the backstepping design. It is proven that all the signals in the closed‐loop system remain bounded in probability, and the tracking errors converge to a small residual set around the origin in the sense of a mean quartic value. As the MTN contains only addition and multiplication, the proposed control method is more simplified and of good real‐time performance, compared with the existing control methods for large‐scale stochastic nonlinear systems. Finally, a numerical example is presented to illustrate the effectiveness of the proposed design approach, and simulation results demonstrate that the method presented in this paper has good real‐time performance and control quality, and the dynamic performance of the closed‐loop system is satisfactory. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive control approach based on the multi‐dimensional Taylor network (MTN) is proposed to control the non‐linear uncertain time‐varying systems with noise disturbances. MTNs are introduced to formulate adaptive filtering, non‐linear identification and optimal control. First, an MTN filter is developed to eliminate the control interference and measurement noise, so that the model output without stochastic disturbance can be obtained. Then, an MTN identifier (MTNI) is so designed as to be capable of dynamic mapping and require fewer weights than traditional neural networks. On the basis of the above, the MTN controller (MTNC) is developed to realise the precise tracking control of the system. The non‐linear uncertain time‐varying system is identified by MTNI, which then provides sensitivity information of the plant to MTNC to make it adaptive. Furthermore, the skeletonisation algorithm is adopted to remove redundant inputs and redundant regression items from MTNI and MTNC for concise MTNs. Successful convergence and faster learning are guaranteed using the Lyapunov theorem, and the optimal learning rates are identified. Simulation results demonstrate that the proposed approach features its accurate identification, excellent tracking and better anti‐interference capability for the adaptive real‐time control of uncertain, stochastic and time‐varying non‐linear systems. [ABSTRACT FROM AUTHOR]

ATPase family AAA domain-containing protein 2 (ATAD2) is involved in various types of cancers, including colorectal cancer. This study aimed to determine the role of ATAD2 in angiogenesis in colorectal cancer. Here, we downregulated ATAD2 expression in HCT116 and SW480 cells, and collected the conditioned medium (CM) from control and ATAD2-silenced cells. The effect of CM on human umbilical vein endothelial cells (HUVEC) was evaluated by using CCK-8, wound healing, tube formation, Western blot, and dual-luciferase reporter assays. Our results showed that the proliferation, migration, and tube formation of HUVEC were reduced in presence of ATAD2-silenced CM, and the levels of phosphorylated vascular endothelial growth factor receptor 2 (P-VEGFR2), CD31, and CD34 were downregulated. Mechanism studies showed that ATAD2 silencing regulated the expression of vascular endothelial growth factor A (VEGFA) and miR-520a. Moreover, we found that miR-520a could bind to ATAD2, and its inhibitor partly reversed the alterations in HUVEC induced by CM from ATAD2-silenced cells. In addition, we demonstrated that miR-520a directly bound to 3′-UTR of VEGFA and inhibited its expression. Collectively, our results indicate that ATAD2 inhibition suppresses VEGFA secretion by increasing miR-520a levels. Our study suggests ATAD2 as a potential therapeutic target for angiogenesis in colorectal cancer. [ABSTRACT FROM AUTHOR]

Nonlinear time-varying systems without mechanism models are common in application. They cannot be controlled directly by the traditional control methods based on precise mathematical models. Intelligent control is unsuitable for real-time control due to its computation complexity. For that sake, a multidimensional Taylor network (MTN) based output tracking control scheme, which consists of two MTNs, one as an identifier and the other as a controller, is proposed for SISO nonlinear time-varying discrete-time systems with no mechanism models. A MTN identifier is constructed to build the offline model of the system, and a set of initial parameters for online learning of the identifier is obtained. Then, an ideal output signal is selected relative to the given reference signal. Based on the system identification model, Pontryagin minimum principle is introduced to obtain the numerical solution of the optimal control law for the system relative to the given ideal output signal, with the corresponding optimal output taken as the desired output signal. A MTN controller is generated automatically to fit the numerical solution of the optimal control law using the conjugate gradient (CG) method, and a set of initial parameters for online learning of the controller is obtained. An adaptive back propagation (BP) algorithm is developed to adjust the parameters of the identifier and controller in real time, and the convergence for the proposed learning algorithm is verified. Simulation results show that the proposed scheme is valid. [ABSTRACT FROM AUTHOR]

The randomness and nonlinearity of stochastic nonlinear systems increase computational complexity and impede their tracking performance. However, randomness and nonlinearity are inevitable in practical applications, and the existing methods can hardly achieve the desirable control effect, especially in real-time control. For this end, a new network control strategy based on multi-dimensional Taylor network (MTN), whose design depends only on the system output, is put forward to solve the optimal output-feedback tracking problem of the SISO stochastic nonlinear systems. The network structure of the MTN is given first, and its approximation properties are proven. Based on the quadratic cost function design learning algorithm, the tracking error is minimized to update the controller parameters, and the desired tracking performance is obtained. Using the Lyapunov stability theorem, it is proved that the corresponding closed-loop system is bounded in the sense of probability and it can be ensured that the output tracking error converges to a small residual set around the origin in the sense of probability. An example is provided to illustrate the effectiveness of the proposed design approach. Comparative simulation study reveals that the proposed solution promises desirable real-time dynamic performance. [ABSTRACT FROM AUTHOR]

In view of the low generalization capacity of standard support vector machine for some types of noises existing in time series such as white noise, singularities, and biggish magnitude noises, a robust loss function is designed to inhibit (penalize) the above hybrid noises. The structure risk minimization (SRM) based on a geometrical interval with a special hyper-plane is theoretically proved. On the basis of that, a new support vector machine called R v -SVM (robust v -support vector machine) that meets SRM is proposed to handle sale series. Moreover, by modifying the standard v -SVM formulation, R v -SVM is characterized with its simpler dual optimization and fewer output parameters. With the help of the above, a sale forecasting method based on R v -SVM and its relevant parameter-choosing algorithm is formulated and applied to car sales forecasting, the convincing results of which definitely confirm the feasibility and validity of the forecasting method. Compared with the standard v -SVM and traditional model, the R v -SVM method is of better estimating precision and higher generalization capacity. [ABSTRACT FROM AUTHOR]

In this study, the problem of adaptive multi-dimensional Taylor network (MTN) control for single-input single-output (SISO) uncertain stochastic non-linear systems is investigated. How to minimise the influence of randomness and uncertain non-linearity for less complex computation, and how to improve the real-time performance of the controller are of great significance. To this end, a control approach based on MTN is proposed for tracking control of stochastic non-linear systems. MTNs are used to approximate the non-linearities, and the backstepping technique is employed to construct the MTN controller (MTNC). MTNC involves only addition and multiplication, featuring desirable simplicity and real-time performance. Stability of the system is guaranteed via Lyapunov approach, and it is proved that the proposed controller can guarantee that all signals of the closed-loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighbourhood around the origin. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach, and simulation results demonstrate that the method presented in this study has good real-time performance and control quality. [ABSTRACT FROM AUTHOR]

In this study, the problem of adaptive multi‐dimensional Taylor network (MTN) control for single‐input single‐output (SISO) uncertain stochastic non‐linear systems is investigated. How to minimise the influence of randomness and uncertain non‐linearity for less complex computation, and how to improve the real‐time performance of the controller are of great significance. To this end, a control approach based on MTN is proposed for tracking control of stochastic non‐linear systems. MTNs are used to approximate the non‐linearities, and the backstepping technique is employed to construct the MTN controller (MTNC). MTNC involves only addition and multiplication, featuring desirable simplicity and real‐time performance. Stability of the system is guaranteed via Lyapunov approach, and it is proved that the proposed controller can guarantee that all signals of the closed‐loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighbourhood around the origin. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach, and simulation results demonstrate that the method presented in this study has good real‐time performance and control quality. [ABSTRACT FROM AUTHOR]

A new approach to combining forecasts by rules based on reliability and validity is proposed in terms of the basic principle of the personnel scrutiny method. Robust models that fit in current forecasting problems are chosen by means of matching rules based on reliability and validity, forming the participating model sets of historical case set and current case set. Then, these models are assigned the degrees of satisfaction according to their forecasting accuracy on the above-mentioned two case sets and endowed with corresponding weights. A rational experiment on validating the combination model is designed. The result shows the proposed method not only attains a forecasting accuracy comparable to that of the theoretically optimal combining methods but also provides some valuable insights into combination methodology, which verifies that the combining forecast upgrades the forecasting performance. [ABSTRACT FROM AUTHOR]

The actual controlled objects are generally multi-input and multioutput (MIMO) nonlinear systems with imprecise models or even without models, so it is one of the hot topics in the control theory. Due to the complex internal structure, the general control methods without models tend to be based on neural networks. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. The newly developed multidimensional Taylor network (MTN) requires only addition and multiplication, so it is easy to realize real-time control. In the present study, the MTN approach is extended to MIMO nonlinear systems without models to realize adaptive output feedback control. The MTN controller is proposed to guarantee the stability of the closed-loop system. Our experimental results show that the output signals of the system are bounded and the tracking error goes nearly to zero. The MTN optimal controller is proven to promise far better real-time dynamic performance and robustness than the BP neural network self-adaption reconstitution controller. [ABSTRACT FROM AUTHOR]

For non‐linear control, it is important to secure a generally structured controller that promises wide application and desirable performance. This study deals with the problem of asymptotic tracking and dynamic regulation of single‐input single output (SISO) non‐linear systems via output feedbacks by the discrete multi‐dimensional Taylor network (MTN) controller, a novel controller with fixed structure and sampled‐data control mechanism. For verification of its validity, differential geometry and polynomial approximation are adopted. Using the emulation technique and regional pole assignment, the asymptotic tracking and dynamic regulation without online optimisation of the system by discrete MTN controller is tested. With the dynamic change of error signals, the dynamic regulation by given index is realised. As a convex optimisation problem, the controller parameters can be acquired by parametric learning. Based on the delta operator model, the procedure of the controller design is given in detail. Simulation results confirm the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The manufacturing enterprise needs price forecasting in order to attain maximum profit and guild production. However, it is difficult to trace the changing rules of their historical data since product price may fluctuate abruptly. Therefore, a price forecasting method based on correlative price net and neural networks is proposed in the paper. Firstly, by analyzing the main factors to product price changes, a model of correlative price net (CPN) that connects many products whose prices affect each other is set up. Then, the theoretical proof that the whole CPN can be substituted by its correlative price sub-net for price forecasting is provided. Based on the correlative price sub-net, a set of BP neural networks are introduced for building a new network, named the price forecasting net (PFN), which can reflect the factors of price fluctuation in the correlative price sub-net. Finally, since the change of factors in the correlative price sub-net relates to the variation of product price caused by it, the product price can be forecasted by the change of factors in the correlative price sub-net by means of PFN. The theoretic analysis and simulation experiment show that the proposed method adapts well to product price forecasting, especially when prices change abruptly. [ABSTRACT FROM AUTHOR]

There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR. [ABSTRACT FROM AUTHOR]

This paper presents a systematic design approach to synthesize feasible configurations of parallel transmissions for a hybrid electric vehicle using single electric motor/generator. Based on the studies of existing parallel hybrid systems, four main types of parallel hybrid transmissions are classified and studied. The mechanisms, which were studied in present research, are two degrees of freedom compound planetary gear trains consisting of two simple planetary gear trains. By applying the proposed design approach, twelve feasible mechanisms for parallel hybrid transmissions are generated. Then, four potential mechanisms, which are used in existing designs, are employed to systematically synthesize 37 corresponding feasible parallel hybrid transmissions, including some existing parallel systems. To demonstrate the feasibility of the synthesized systems, a new parallel hybrid transmission is taken arbitrarily as a numerical example to illustrate its working principle with operation modes, and kinematics and power flow analysis. [ABSTRACT FROM AUTHOR]

Number synthesis of kinematic chains is one of the major steps in the structural synthesis or conceptual design of mechanisms. The work presents a comprehensive literature review on the number synthesis of kinematic chains of mechanisms. The terminology and definitions regarding kinematic chains in literature are introduced first. Then, various methods for the number synthesis of kinematic chains are presented and discussed. Finally, the numbers of kinematic chains with up to 15 links and 7 degrees of freedom are listed. [ABSTRACT FROM AUTHOR]

There exist problems of small samples and heteroscedastic noise in design time forecast. To solve them, support vector regression with probabilistic constraints (PC-SVR) is proposed in this article. The mean and variance functions are simultaneously constructed based on a heteroscedastic regression model. Probabilistic constraints are designed to make sure that for every sample, the forecast value is in a neighborhood of the target value with high probability. The optimization objective is formatted in the form of par-v-SVR. Prior knowledge about maximum completion time can be embedded in probabilistic constraints, and provides the size of the neighborhood of the target value. The results of application in injection mold design have confirmed the feasibility and validity of PC-SVR. [ABSTRACT FROM AUTHOR]

Abstract: Generating generalized kinematic chains is one of the most important steps in the conceptual design or structural synthesis of mechanisms. An improved method for the systematic construction of generalized kinematic chains (including both kinematic chains and rigid chains) is presented based on two algorithms, namely the cut-links checking algorithm and Kuratowski (nonplanar) graph checking algorithm. Examples are provided to illustrate the algorithms. The atlases of generalized kinematic chains comprising up to sixteen links are listed. [Copyright &y& Elsevier]

An adaptive scheduling strategy based on state-action uncertainty bias based Q-learning (SAUBQ-learning) is proposed for adaptive scheduling in knowledgeable manufacturing environment. Aiming at the problem of slow convergence and long training time in conventional Q-learning process, a state-action uncertainty measure is defined by information entropy, on the basis of which a Q-learning action bias information function is defined. The bias information is integrated into Q-learning system by designing heuristic Q-learning reward function, and the optimal strategy invariance and convergence of SAUBQ-learning are proved. In learning process, search space is adjusted by bias information, the effective state-action number explored by Q-learning is reduced. Furthermore, the bias information is also continually updated by Q-learning results, and misleading Q-learning process is avoided. Simulation experiment results indicate that the strategy has better adaptive feature in dynamic environment and the feature of converging quickly in large state space, and improves the efficiency of scheduling. [ABSTRACT FROM AUTHOR]

Proposed in this paper is a new economic production lot size (EPLS) model, i.e., a multi-cycle flexible production/inventory model under random demands, which considers the holding and shortage costs of finished products as well as the setup costs of production, and employs an (s,Q) policy for production/inventory control. We have theoretically proved that the total expected cost of the model is a convex function with respect to variables s and Q. On this basis, an iterative learning algorithm is designed to speed up the process by using the model convexity, which enables us to obtain the optimal production preparation point and the optimal production quantity. Results from the comparison between the proposed algorithm and the genetic algorithm (GA) show that the control quantities secured by the two algorithms are consistent but the former has higher solution speed, which verifies the accuracy and effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Using Jiangxi Yanshan red bud taro (Colocasia esculenta var. cormosus cv. Hongyayu) embryogenic calli as materials, the efects of various factors on its cryopreservation by vitrification were studied. The results showed that beter preculture condition of Jiangxi Yanshan red bud taro embryogenic cali vitrification cryopreservation was mol.L-1 sucrose for 3 days Beter loading time was 60% PVS2 for 20 min. Beter dehydration time of 100% PVS2 was 30 min at 25 °C. Beter thaw temperature was 40 °C. Beter sucrose concentration in washing media was 1.2 mol . L-1 Beter culture condition after cryopreservation was dark culture for 7 d and then transferred to the photoperiod The average survival rate of embryogenic cali after cryopreservation by vitrification amounted to about 70%. No significant diference was observed in the morphological, physiological and cytological indexes of plantlets coming from control and cryopreserved embryogenic calli. [ABSTRACT FROM AUTHOR]

With the growth in customer demand diversification, research on mixed-model assembly lines have been given increasing attention in the field of management. Sequencing decisions are crucial for managing mixed-model assembly lines. To improve production efficiency, the product sequencing problem with skip utility work strategy and sequence-dependent setup times is focused on in this study, and its mathematical model is established, whereby the idle cost, the utility cost and the setup cost are to be optimized simultaneously. A necessary condition for skip policy of the system is set, and a lower bound of utility work cost is given and theoretically proved. Strong NP-hardness of the problem is confirmed. Addressing the main features of the problem, a hybrid EMVNS (electromagnetism-like mechanism-variable neighborhood search) algorithm is developed. To enhance the local search ability of EM, a VNS algorithm is employed and five neighborhood structures are designed. With the aid of the VNS algorithm, the fine neighbour search of the optimum individual is made available, thus improving the solution to a certain extent. Simulation results demonstrate that the algorithm is feasible and valid. Significance: This paper presents a hybrid EMVNS algorithm to solve the product sequencing problem of a mixed-model assembly line with skip utility work strategy and sequence-dependent setup times. The simulation results demonstrate that the proposed method is feasible and valid. [ABSTRACT FROM AUTHOR]

Abstract: Antikythera mechanism should be the oldest astronomical geared calculator in the world. It is confirmed that the solar anomaly motion could display on the front dial even though the corresponding solar mechanism has been lost. This work focuses on the reconstruction synthesis of the lost solar mechanism and provides a systematic approach to generate all feasible designs. Based on the kinematic and mechanism analysis, the design specifications of three possible types concluded by different input conditions are obtained. Through a study of historical literature and the existing design, the required design constraints are further concluded. Then, according to the concepts of generalization and specialization as well as the manufacture consideration, four feasible designs, including Wright's design in 2002, are respectively synthesized for different input types. These designs are consistent with the scientific theories and techniques of the subject's time period and match the existing evidences. [Copyright &y& Elsevier]