Polydimethylsiloxane (PDMS) foam is a widely used porous material with excellent properties across various fields. However, its flammability poses a significant challenge to its broader application. In this study, ferric phytate (PA-Fe) was introduced as a novel flame retardant into PDMS foam. Experimental and molecular dynamics (MD) simulation results reveal that its exceptional flame retardancy derives from the formation of dense SiO2 layers induced by Fe3+ during combustion. These SiO2 layers exhibit more dense structure compared to those generated by pure PDMS foam combustion, providing the foam with enhanced self-extinguishing properties. This study offers a novel strategy for developing highly flame-retardant silicone materials.

In recent years, the increasing frequency of building fires has highlighted the limitations of traditional polymeric materials due to their inadequate fireproof performance. Ceramifiable polymer composites have emerged as a promising alternative by incorporating ceramic-forming fillers that create rigid ceramic-like structures through high-temperature eutectic reactions, offering exceptional thermal insulation and fireproof properties. These composites maintain structural integrity under fire exposure through sufficient mechanical strength retention. The effects of several ceramifiable inorganic fillers (CIFs) on the properties of polydimethylsiloxane (PDMS) foams were systematically investigated in this study. The research demonstrated that fillers with better matrix compatibility significantly enhance the foaming quality, mechanical performance, and fireproof capabilities. Notably, the CaCO3-filled PDMS foam composite (CPF-Ca) demonstrates exceptional foaming characteristics with 84% porosity and a remarkably low density of 0.36 g/cm3. The material achieves tensile and compressive strengths of 0.22 MPa and 0.84 MPa, representing 22% and 127% enhancements, respectively, compared to pure PDMS foam (PPF). Regarding the ceramic conversion capability, the sintered residue of CPF-Ca maintains a compressive strength of 4.39 MPa under high-temperature conditions. This composite material exhibited superior fireproof performance, successfully withstanding a butane torch for 300 s without penetration while maintaining a remarkably low backside temperature of merely 83.6 °C.

Proper optimization of machining parameters can effectively improve production efficiency and precision in high-precision machining, such as turbine blade manufacturing. However, in industrial practice, current studies on the optimization of process parameters suffer from two drawbacks: the first is missing measurement values due to network latency and packet loss, while the second is the slow convergence rate of high-dimensional parameters to be optimized. This article proposes a prediction and optimization framework based on an accelerated limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm; the framework addresses the high-dimensional parameter optimization issues in high-precision machining processes, under incomplete sensor measurement scenarios. Incomplete measurements are imputed based on learned data distributions, significantly improving data utilization of the subsequent deep learning model trained after imputation. Then, to achieve fast high-dimensional parameter optimization, we introduce an accelerated L-BFGS algorithm, termed Powerball L-BFGS, with a Powerball function to optimize the search direction. To evaluate the effectiveness of the proposed framework, we conduct experiments on two series of aviation turbine blades (named Blade A and Blade B) with complex curved surfaces. Incomplete coordinate measuring machine data at key stages (i.e., blade root milling and comprehensive accurate milling) are used as inputs to predict and optimize the final geometrical errors. The proposed Powerball L-BFGS reduces the final optimization root mean square error (RMSE) to 0.053 mm for Blade A and 0.028 mm for Blade B; it decreases optimization iterations from 60 to 21 for Blade A and from 85 to 32 for Blade B, and achieving maximum process capability values of 5.23 and 2.39, respectively.

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, in this paper, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. For this reason, we consider CT models since discrete-time approximations often require fine-grained measurements and uniform sampling steps. The developed method applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank-Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The convergence property of the developed method is robust to the dimension of data sources. Monte Carlo simulations indicate that the developed method outperforms state-of-the-art methods including group sparse Bayesian learning (GSBL), BINGO, kernel-based methods, dynGENIE3, GENIE3, and ARNI. The simulations include random and ring networks, and a synthetic biological network. These are challenging networks, suggesting that the developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks, and communication systems.

Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep -learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R -to -R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.

Aiming at the wind power characteristics of temporality, periodicity and complexity, the periodic law of short-term and long-term repetitive patterns is studied, and an integrated dual-channel prediction model is proposed. A practical periodic characteristic extracting strategy is designed to show the hidden periodic law of the original signal. Combining the grid search algorithm with the variation trend of amplitude/period, the optimal periodic step is determined. Based on the above analysis, the original signal is decomposed into temporal and periodic components. Then the temporal attention network and the encoder-decoder attention network are schemed out to dispose the two components respectively. Finally, the linear regression attention network is adopted to realize data fitting. The integrated forecasting framework can deal with the long-term and short-term dependencies of the original data at the same time, and ensure the rapid convergence of training process, thereby improve the prediction accuracy and stability. The multi-dimensional experimental verification is carried out through the comparison of evaluation indicators, prediction trends, scatter plots and box plots.

In modern manufacturing industries, with the dramatic increase in uncertainty and complexity of production processes, dynamic scheduling methods are urgently needed. To address this issue, this paper proposes a scheduling method based on deep reinforcement learning. First, the DFJSP is modeled as a mark decision process, and five generic features are designed to normalize the states. Moreover, four composite scheduling rule is proposed to select the best scheduling solution by combining several single scheduling rules. Finally, a deep Q network algorithm with softmax action selection policy is constructed. The experimental results show that the scheduling effect of our method is significantly better than the rule-based heuristic algorithm and the meta-heuristic algorithm in the dynamically changing manufacturing environment.

Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.

Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.

In this study, gutter oil biodiesel-based polyols (GOBP) and a reactive-type flame retardant named diethyl bis(2-hydroxyethyl)-aminomethylphosphonate (BHAPE) were both synthesized. Subsequently, gutter oil biodiesel-based flame-retardant rigid polyurethane foams (BPUFs) were prepared with GOBP, BHAPE, aluminum hydroxide (an additive-type flame retardant) and polymeric diphenylmethane diisocyanate. The properties of the BPUFs, including density, thermal conductivity, compressive properties, chemical component, morphology, thermal stability, limiting oxygen index, cone calorimetry testing, char residues stability were characterized. Among them, BPUF-AB owning 6.97 wt% of GOBP, 18.59 wt% of BHAPE and 39.77 wt% of aluminum hydroxide obtained a lower density of 0.097 g/cm(3), a lower thermal conduction of 0.048 W/(m * k) and the highest compressive strength of 187 +/- 13 kPa. Fire testing revealed a highest limiting oxygen index value of 30.1% and passed the UL-94 standard test. Cone calorimetry testing revealed that the peak heat release rate and peak smoke production release of BPUF-AB were reduced by 51.08% and 57.10%, respectively. Meanwhile, the total heat release rate and total smoke production were respectively decreased by 36.88% and 40.79%. The results indicate that BPUFs derived from gutter oil biodiesel are prosperously potential biomaterials with good thermal stability for fire insulation porous materials in future architecture applications.

Multi-view video plus depth (MVD) is the promising and widely adopted data representation for future 3D visual applications and interactive media. However, compression distortions on depth videos impede the development of such applications, and filters are crucially needed for the quality enhancement at the terminal side. Cross-view priors can intuitively be involved in filter design, but these priors are also distorted in compression and thus the contribution of them can hardly be considered in previous research. In this article, we propose a cross-view optimized filter for depth map quality enhancement by making full use of inner- and cross-view priors. We dedicate to evaluate the contributions of distorted cross-view priors in filtering the current view of depth, and then both inner- and cross-view priors can be involved in the filter design. Thus, distortions of cross-view priors are not barriers again as before. For the purpose of that, mutual information guided cross-view consistency is designed to evaluate the contributions of cross-view priors from compression distortions of MVD. After that, under the framework of global optimization, both inner- and cross-view priors are modeled and taken to minimize the designed energy function where both data accuracy and spatial smoothness are modeled. The experimental results show that the proposed model outperforms state-of-the-art methods, where 3.289 dB and 0.0407 average gains on peak signal-to-noise ratio and structural similarity metrics can be obtained, respectively. For the subjective evaluations, object details and structure information are recovered in the compressed depth video. We also verify our method via several practical applications, including virtual view synthesis for smooth interaction and point cloud for 3D modeling for accuracy evaluation. In these verifications, the ringing and malposition artifacts on object contours are properly handled for interactive video, and discontinuous object surfaces are restored for 3D modeling. All of these results suggest that compression distortions in MVD can be properly filtered by the proposed model, which provides a promising solution for future bandwidth constrained 3D and interactive visual applications.

Inevitable compression artifacts in multi-view video (MVV) can clearly degrade the quality of experience in many interaction-oriented 3D visual applications. Under the framework of asymmetric coding, low-quality images can be enhanced with high-quality images from the neighboring viewpoints considering the similarity among different views. However, compression artifacts and warping error cause different cross-view quality gaps for various sequences, and thus the contribution of cross-view priors can hardly be located and considered in previous works. In this paper, we propose a multi-view graph neural network (MV-GNN) to reduce compression artifacts in multi-view compressed images. We dedicate to design a fusion mechanism which can exploit contributions from neighboring viewpoints and meanwhile suppress the misleading information. In our method, a GNN-based fusion mechanism is designed to fuse the cross-view information under the aggregation and update mechanism of GNN. Experiments show that 1.672 dB and 0.0242 average gains on PSNR and SSIM metrics can be obtained, respectively. For the subjective evaluations, blocking effect in the compressed images are clearly suppressed and the damaged object boundary are better recovered. The experimental results demonstrate that our MV-GNN outperforms the state-of-the-art methods.

Nonisocyanate polyurethane (NIPU) is a research hotspot in polyurethane applications because it does not use phosgene. Herein, a novel method of solvent- and catalyst-free synthesis of a hybrid nonisocyanate polyurethane (HNIPU) is proposed. First, four diamines were used to react with ethylene carbonate to obtain four bis(hydroxyethyloxycarbonylamino)alkane (BHA). Then, BHA reacted with dimer acid under condensation in the melt to prepare four nonisocynate polyurethane prepolymers. Further, the HNIPUs were obtained by crosslinking prepolymers and resin epoxy and cured with the program temperature rise. In addition, four amines and two resin epoxies were employed to study the effects and regularity of HNIPUs. According to the results from thermal and dynamic mechanical analyses, those HNIPUs showed a high degree of thermal stability, and the highest 5% weight loss reached about 350 °C. More importantly, the utilization of these green raw materials accords with the concept of sustainable development. Further, the synthetic method and HNIPUs don't need isocyanates, catalysts, or solvents.

Dimer acid cyclocarbonate (DACC) is synthesized from glycerol carbonate (GC) and Sapium sebiferum oil-derived dimer acid (DA, 9-[(Z)-non-3-enyl]-10-octylnonadecanedioic acid). Meanwhile, DACC can be used for synthetic materials of bio-based non-isocyanate polyurethane (bio-NIPU). In this study, DACC was synthesized by the esterification of dimer acid and glycerol carbonate using Novozym 435 (Candida antarctica lipase B) as the biocatalyst. Via the optimizing reaction conditions, the highest yield of 76.00% and the lowest acid value of 43.82 mg KOH/g were obtained. The product was confirmed and characterized by Fourier transform-infrared spectroscopy (FTIR) and nuclear magnetic resonance spectroscopy (NMR). Then, the synthetic DACC was further used to synthesize bio-NIPU, which was examined by FTIR, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC), indicating that it possesses very good physio-chemical properties and unique material quality with a potential prospect in applications.

Bio-based polyurethane (PU) composites with superior thermal and mechanical properties have received wide attention. This is due to the recent rapid developments in the PU industry. In the work reported here, novel nano-composites with graphene oxide (GO)-modified Sapium sebiferum oil (SSO)-based PU has been synthesized via in situ polymerization. GO, prepared using the improved Hummers method from natural graphene (NG), and SSO-based polyol with a hydroxyl value of 211 mg KOH/g, prepared by lipase hydrolysis, were used as raw materials. The microstructures and properties of GO and the nano-composites were both characterized using Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and tensile tests. The results showed that GO with its nano-sheet structure possessed a significant number of oxygen-containing functional groups at the surface. The nano-composites containing 1 wt % GO in the PU matrix (PU1) exhibited excellent comprehensive properties. Compared with those for pure PU, the glass transition temperature (Tg) and initial decomposition temperature (IDT) of the PU1 were enhanced by 14.1 and 31.8 °C, respectively. In addition, the tensile strength and Young’s modulus of the PU1 were also improved by 126% and 102%, respectively, compared to the pure PU. The significant improvement in both the thermal stability and mechanical properties for PU/GO composites was attributed to the homogeneous dispersion and good compatibility of GO with the PU matrix. The improvement in the properties upon the addition of GO may be attributable to the strong interfacial interaction between the reinforcing agent and the PU matrix.

Multi-view depth is crucial for describing positioning information in 3D space for virtual reality, free viewpoint video, and other interaction- and remote-oriented applications. However, in cases of lossy compression for bandwidth limited remote applications, the quality of multi-view depth video suffers from quantization errors, leading to the generation of obvious artifacts in consequent virtual view rendering during interactions. Considerable efforts must be made to properly address these artifacts. In this paper, we propose a cross-view multi-lateral filtering scheme to improve the quality of compressed depth maps/videos within the framework of asymmetric multi-view video with depth compression. Through this scheme, a distorted depth map is enhanced via non-local candidates selected from current and neighboring viewpoints of different time-slots. Specifically, these candidates are clustered into a macro super pixel denoting the physical and semantic cross-relationships of the cross-view, spatial and temporal priors. The experimental results show that gains from static depth maps and dynamic depth videos can be obtained from PSNR and SSIM metrics, respectively. In subjective evaluations, even object contours are recovered from a compressed depth video. We also verify our method via several practical applications. For these verifications, artifacts on object contours are properly managed for the development of interactive video and discontinuous object surfaces are restored for 3D modeling. Our results suggest that the proposed filter outperforms state-of-the-art filters and is suitable for use in multi-view color plus depth-based interaction- and remote-oriented applications.

Time-series plots have been widely used in the fields of data analysis and data mining because of its good visual characteristics. However, when researching and analyzing the massive data formed in industrial, some shortcomings of the traditional time-series plot make the visualization of big data ineffective, which is not conducive to data analysis and mining. In this paper, the traditional time-series graph is improved and a segmented time-series plot that can be used for massive industrial data analysis is proposed. In addition, this paper describes in detail the steps of making the segmented time-series plot. The method can reduce the information overload and the interface issues by limiting the amount of information presented.

This work demonstrated an efficient way for the synthesis of thermoreversible cross-linked epoxy resin based on Diels-Alder reaction (DAER) via two steps: a side-chain pendant furan-functionalized liner epoxy resin (LER) was designed and synthesized in the first step; a cross-linked network was constructed via DA reaction between the bismaleimide (BMI) and the pendant furan functional groups in the side chain of the LER in the second step. The cross-linking density could be adjusted by changing the mole ratio of maleic imide in BMI to furan groups in LER. The DAERs could perform de-crosslinking above 100°C and reform the network below 60°C. The results of the tensile experiment showed that the mechanical properties of the DAERs can be compared with the traditional aromatic amine cured epoxy resin. The thermostability of all the cross-linked DAERs were improved significantly compared to the DAER0, especially DAER100 with about 40°C higher transition temperature, which indicated that DA cross-linking was an effective way to improve the heat resistance of the polymer material.

Sapium sebiferumoil-based polyol was modified by lipase hydrolysis for primary alcohols and further synthesis of polyurethane with improved properties.

Vegetable oil is one of the most potential alternatives of petroleum and has become a hot issue in recent years.This review focuses on the influence of vegetable oil structure on platform compounds and polymers properties,and further systematically introduces their developments and the latest progress.Meanwhile,we also summarized the main confronting problems and the future development directions in the research of oil-based platform compounds and polymers.The review provides useful information for readers to fully understand biochemical engineering of vegetable oils and their prospects.