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Extensive use of selective catalytic reduction denitrogenation technology leads to the generation of a large amount of hazardous waste vanadium–tungsten–titanium catalysts. To valorize these waste ...

Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to the closed-loop control, it is possible that the fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) is proposed, which can not only tell whether the fault occurs but can also judge whether the fault affects the product quality in real time. First, to reduce process analysis complexity and to construct an accurate monitoring model, the original process variable space is divided into four subspaces. Second, the developed orthogonal CCA is conducted on process data and quality data for correlation feature extraction. Then, the quality-related and quality-unrelated features are obtained. Afterward, a total of six monitoring statistics are constructed and integrated to four statistics with physical interpretation via the Bayesian fusion strategy. Finally, the developed method is tested under an industrial case.

The hot deformation behavior and microstructure evolution of an Fe–30Cr–2Mo ultra-pure super ferritic stainless steel were investigated at the temperature range of 950–1150 °C and strain rate varying from 0.01 to 10 s−1. A strain compensated constitutive equation based on the Arrhenius-type model was established to predict the flow stress. The hot processing map based on the dynamic materials model was achieved to identify the optimum processing parameters. In addition, the features of microstructure evolution combined with the processing map were systematically investigated. The experimental results revealed that the flow stress increased with decreasing deformation temperature or increasing strain rate. Dynamic recovery was confirmed to be the predominant softening mechanism. The values of flow stress predicted by the strain compensated constitutive equation agreed well with the experimental values. The extent of dynamic recrystallization and recrystallized grain size increased with increasing deformation temperature or decreasing strain rate, and the continuous dynamic recrystallization was attributed to be the predominant mechanism of recrystallization during hot deformation. The optimum hot working parameters were determined to be the deformation temperature of 1070–1150 °C and strain rate of 0.1–1 s−1 with a peak power dissipation efficiency of 42%.

A MoSi2 coating was prepared on the Mo1 wire mesh via pack cementation method, and its failure mechanisms under isothermal oxidation and hot-fire test using a hydroxylammonium nitrate based monopropellant were comparatively studied. Under isothermal oxidation at 1300 °C and 1400 °C, degradation of MoSi2 into Mo5Si3 caused failure of the coating, and interdiffusion made a much larger effect relative to oxidation. However, the MoSi2 coating failed because of the synergy of oxidation, ablation, and interdiffusion under hot-fire test. Besides, dissolution of mullite into SiO2 and ablation of high velocity flame contributed to the failure of the coating as well.

Hydrotreating of vacuum residue by ebullated bed shows tremendous significance due to more stringent environmental regulations and growing demand for lighter fuels. However, enhancing the catalyst stability still remains as a challenging task. Herein, two Ni-Mo/Al2O3 catalysts with distinct morphologies (i.e., spherical and cylindrical) were first designed, and the morphology effect on deactivation was systematically elucidated employing multi-characterizations, such as HRTEM with EDX mapping, electron microprobe analysis, FT-IR, TGA and Raman. It is found that spherical catalyst exhibits superior hydrotreating stability over 1600 h. The carbonaceous deposits on spherical catalyst with less graphite structure are lighter, and the coke weight is also smaller. In addition, the metal deposits uniformly distribute in the spherical catalyst, which is better than the concentrated distribution near the pore mouth for the cylindrical catalyst. Furthermore, the intrinsic reason for the differences was analyzed by the bed expansion experiment. Higher bed expansion rate together with the better mass transfer ability leads to the enhanced performance. This work sheds new light on the design of more efficient industrial hydrotreating catalyst based on morphology effect.

While offering high-precision control of neural circuits, optogenetics is hampered by the necessity to implant fiber-optic waveguides in order to deliver photons to genetically engineered light-gated neurons in the brain. Unlike laser light, X-rays freely pass biological barriers. Here we show that radioluminescent Gd2(WO4)3:Eu nanoparticles, which absorb external X-rays energy and then downconvert it into optical photons with wavelengths of ∼610 nm, can be used for the transcranial stimulation of cortical neurons expressing red-shifted, ∼590-630 nm, channelrhodopsin ReaChR, thereby promoting optogenetic neural control to the practical implementation of minimally invasive wireless deep brain stimulation.

As the deep learning technology develops, many process monitoring methods based on auto-encoder (AE) are designed for the nonlinear industrial processes. However, these methods mainly focus on process variables and ignore the quality indicator which is crucial for the final production. To extract the latent variables which represent both process information and quality information, this paper proposes a novel algorithm named information concentrated variational auto-encoder (IFCVAE). To concentrate the quality-related information, a loading matrix regularization based on mutual information is designed, so that the strongly quality-related variables tend to have larger weights in the loading matrix. In addition, to monitor processes from the quality-related and unrelated aspects, IFCVAE decomposes the original space into two subspaces that are mutually orthogonal based on variational auto-encoder (VAE). With the help of an additional regression network, the two subspaces can correspond to the quality-related and unrelated spaces. For process monitoring, two statistics are designed for the subspaces according to Kullback–Leibler divergence. Finally, the effectiveness of IFCVAE is demonstrated by a numerical case and an industrial case.

Efficiency and multi-simultaneous-frequency (MSF) output capability are two major criteria characterising the performance of a power amplifier in the application of multi-frequency eddy current testing (MECT). Switch-mode power amplifiers are known to have very high efficiency, yet they have rarely been adopted in the instrumental development of MECT. In addition, switch-mode power amplifiers themselves are lacking in the research literature for MSF. In this paper, a Class D power amplifier is designed so as to address the two issues. An MSF selective harmonic elimination pulse width modulation (MSF-SHEPWM) method is proposed to generate alternating magnetic fields which are rich in selected harmonics. A field-programmable gate array (FPGA) based experimental system has been developed to verify the design. Results show that the proposed methodology is capable of generating high MSF currents in the transmitting coil with a low distortion of signal.

Ultra-high-purity silicon tetrachloride (SiCl4) is demanded as an electronic-grade chemical to meet the stringent requirements of the rapidly developing semiconductor industry. The high requirement for ultra-high-purity SiCl4 has created the need for a high-efficient process for reducing energy consumption as well as satisfying product quality. In this paper, a mass of production technology of ultra-high-purity SiCl4 was successfully developed through chlorination reaction in the ultraviolet (UV)-based photo microreactor coupled with the distillation process. The influences of key operational parameters, including temperature, pressure, UV wavelength and light intensity on the product quality, especially for hydrogen-containing impurities, were quantified by the infrared transmittance of Fourier transform infrared spectroscopy (FT-IR) at 2185 cm−1 and 2160 cm−1 indicating that characteristic vibrational modes of Si H bonds, as well as the operating conditions of distillation were also investigated as key factors for metal impurities removing. The advanced intensification of SiCl4 manufactured by the integration of photo microreactor and distillation achieves the products with superior specifications higher than the standard commercial products.

The technique of recognising text in natural scene pictures is widely used in social production. For the existing identification methods, it is difficult to accurately identify in complex environments. The accuracy of the detection determines the efficiency of the identification. A text detection method based on Multiscale Connectionist Text Proposal Network is proposed. The Multiscale-Region Proposal Network regresses and classifies the extracted region to obtain the final candidate region. Taking a large number of commodity image samples as a dataset, the multi-scale joint text proposal network is used to detect and locate the text content area in the image. The experimental results show that the proposed algorithm improves the detection accuracy in complex environments.

SiCp/Al composite is one of the key light metal matrix composites widely used in aerospace and electronics industries due to its excellent material properties. Understanding the mechanism of abrasive particle action and the cutting force generation is crucial for achieving desired hole quality and machining accuracy. However, there is no research on cutting force modeling in drilling of SiCp/Al composites. This paper proposes an energy-based mechanistic modeling method to predict the cutting force in drilling of SiCp/Al composites for the first time. A new comprehensive abrasive particle model considering different particle action mechanisms, such as cracking, debonding, and squeezing, is presented to depict the energy consumed by abrasive particle action. Experiments with a wide range of particle volume fractions and feed rates, and different particle sizes were carried out to validate the accuracy of the proposed model. Results show that the proposed model can accurately predict the cutting force with an average error of 6.55% in drilling of SiCp/Al composites. Furthermore, the energy consumed on abrasive particle action is estimated to be 8.2–13.6% of total cutting energy. The proposed model on SiCp/Al composites can be extended to other particle reinforced metal matrix composites.

The recently increasing interest in using super-elastic shape memory alloys (SMAs) for structural vibration control creates the need for a suitable mechanical model that considers the material’s close correlation with the strain amplitude and the strain rate. This paper proposes an improved Graesser-Cozzarelli (G-C) model for predicting the hysteretic responses of the super-elastic SMAs at various strain rates and strain amplitudes. A series of uniaxial tensile tests of super-elastic SMA wires are conducted first, and the effects of the strain amplitude and the strain rate on the characteristic parameters are quantified. Then, based on the original G-C model and experimental test results, an improved version is proposed. The revised model considers the nonlinear hardening behaviour of martensite and divides the complete hysteretic curve into two stages: a loading stage and an unloading stage. Finally, numerical simulations are conducted to evaluate the model’s performance in predicting the hysteresis responses at various strain amplitudes and strain rates. High accuracy is realized in all comparisons with the experimental results; thus, the improved G-C model is highly suitable for the simulation of super-elastic SMA-based dampers in applications for vibration control.

The electro-hydraulic driven multi-dimensional force loading parallel mechanism can meet the requirements of complex force loading. However, the force loading performance is affected by the strong dynamic coupling due to flexible load and structure characteristics of parallel mechanism, which is analyzed from the mathematical model built by the Newton–Euler method. In order to solve the coupling problem, a novel control strategy, modal space sliding mode control, is proposed. It can realize multi-dimensional force loading decoupling by a coordinating modal space controller. The modal space control framework is established based on the vibration theory, and the decoupling characteristics of modal space are analyzed. Furthermore, it is discussed that the consistency of modal space channels is required for the decoupling condition in degree of freedom space. In order to improve the tracking performance of modal space channels, a sliding mode controller is designed in the modal space by using the decoupling property of modal space. The modal space sliding mode controller not only reduces the chattering caused by coupling force, but also greatly improves the dynamic performance of modal space channels, as well as the consistency of modal space channels. The stability condition of the modal space sliding mode controller is given and proved by Lyapunov theorem. The experimental results show the effectiveness of the proposed modal space sliding mode control, which can significantly reduce coupling force and improve the dynamic tracking performance.

In this paper, we introduce the first simulation framework that provides cost-effective means to evaluate indoor navigation systems for different user groups (e.g., users with visual impairment), various positioning techniques, and navigation instructions algorithms. The simulation engine uses the Unity game engine, which tracks the virtual user interaction and motion in a virtual environment that represents the physical environment in which the user navigates. The framework includes the following modules that will be defined by the indoor navigation system developers: 1) Positioning module which simulates indoor localization techniques and their observed accuracy; 2) Indoor navigation algorithm module that generates the navigation instructions using natural language phrases, and 3) Virtual user model (VUM) that includes human perception and information processing that can understand navigation instructions, perceive the surrounding environment, and act based on this information. The framework also includes a performance analysis module that evaluates the indoor navigation system performance in terms of navigation success rate and route similarity.

Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the “hot spot” problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

In recent years, tannin degradation has been used to obtain tannin materials with an optimal molecular weight distribution (MWD) for synthesizing tannin-formaldehyde (TF) resin with high performance, but the optimal MWD of tannins is still unknown. The excellent formaldehyde reactivity of tannins is the basis for the synthesis of high-performance TF resin. Based on the formaldehyde reactivity of tannins, bayberry tannins and larch tannins were used to explore the optimal MWD of tannins for TF resin synthesis. Progressive solvent precipitation (PSP) was used to obtain tannin fractions with different MWDs. The formaldehyde reactivity of tannins was determined using the modified Stiansy method combined with the standard curve method (GB/T 17657-2013). The bayberry tannin fraction [weight-average molecular weight (Mw) of acetylated tannin: 4115, mean degree of polymerization (mDP): 6.64] and the larch tannin fraction (Mw of acetylated tannin: 3906, mDP: 5.84) had the best formaldehyde reactivity. Furthermore, significant differences in the formaldehyde reactivity of condensed tannins (CTs) with different MWDs were observed. The obtained results can be used to purposefully degrade tannins to achieve an optimal MWD, which is beneficial for the production of TF adhesives with high performance.

In this paper, a novel supervised nonlinear process monitoring method named comprehensive kernel principal component regression (C-KPCR) is proposed to monitor the quality-related/unrelated additive/multiplicative faults. Firstly, mutual information is used to classify the process variables into quality-related part and quality-unrelated part. Secondly, the original variables matrix and the variables variance matrix are constructed and the data is mapped into high-dimensional feature space to deal with the nonlinear problem. Then the quality-related additive and multiplicative faults can be detected based on the regression model using original variables matrix and variables variance matrix, respectively. Afterwards, the monitoring result of quality-unrelated fault is obtained through combining the quality-unrelated information in the regression model and the quality-unrelated process variables. Finally, the effectiveness of the proposed method is demonstrated by a numerical example and the Tennessee Eastman process.

Chlorella sorokiniana CY-1 was cultivated using palm oil mill effluent (POME) in a novel-designed photobioreactor (NPBR) and glass-made vessel photobioreactor (PBR). The comparison was made on biomass and lipid productions, as well as its pollutants removal efficiencies. NPBR is transparent and is developed in thin flat panels with a high surface area per volume ratio. It is equipped with microbubbling and baffles retention, ensuring effective light and CO2 utilization. The triangular shape of this reactor at the bottom serves to ease microalgae cell harvesting by sedimentation. Both biomass and lipid yields attained in NPBR were 2.3–2.9 folds higher than cultivated in PBR. The pollutants removal efficiencies achieved were 93.7% of chemical oxygen demand, 98.6% of total nitrogen and 96.0% of total phosphorus. Mathematical model revealed that effective light received and initial mass contributes toward successful microalgae cultivation. Overall, the results revealed the potential of NPBR integration in Chlorella sorokiniana CY-1 cultivation, with an aim to achieve greater feasibility in microalgal-based biofuel real application and for environmental sustainability., Graphical Abstract

The drying kinetics and detailed phytochemical profile of broccoli under microwave drying (500 and 900 W) and air-borne ultrasound (125.2 and 180.1 W/dm2) assisted air drying at 70 °C were compared...

In order to study the gas occurrence regularity and the main controlling factors of Panxi Mine, gas parameters of the mine were determined through the combination of field survey and experiment, an...

The preparation and fractionation of oligomeric proanthocyanidins (OPCs) are particularly important for the application of tannins in the biomedical field. By use of two different methods-gel filtration chromatography (GFC) with Sephadex LH-20 and progressive solvent precipitation-the OPCs were prepared and fractionated from mangosteen pericarp. The fractions were compared by reversed-phase and normal-phase high-performance liquid chromatography-electrospray ionization mass spectrometry and gel permeation chromatography. GFC directly purified oligomers (monomer to pentamer) with polydispersity values close to 1 and generated fractions with a higher level of total phenols (800.59 mg gallic acid equivalents per gram) but a lower yield (7.72%). Progressive solvent precipitation rapidly prepared and fractionated OPCs with a lower level of total phenols (609.57 mg gallic acid equivalents per gram) but a higher yield (24.74%) and higher polydispersity. Additionally, we found pronounced structural and quantitative differences among different tannin-rich fractions, and fractions obtained by GFC better reflected the structural diversity and complexity of OPCs from mangosteen pericarp. This study presents different ways of preparing and fractionating OPCs in the biomedical field.

In this study, we designed an experimental system of surface potential during coal adsorption of gas to examine the characteristics of surface potential in this process. The results suggested that ...

In this work, sonication (20-kHz) was conducted to assist the biosorption of phenolics from blueberry pomace extracts by brewery waste yeast biomass. The adsorption capacity of yeast increased markedly under ultrasonic fields. After sonication at 394.2 W/L and 40 °C for 120 min, the adsorption capacity was increased by 62.7% compared with that under reciprocating shaking. An artificial neural network was used to model and visualize the effects of different parameters on yeast biosorption capacity. Both biosorption time and acoustic energy density had positive influences on yeast biosorption capacity, whereas no clear influence of temperature on biosorption process was observed. Regarding the mechanism of ultrasound-enhanced biosorption process, the amino and carboxyl groups in yeast were considered to be associated with the yeast biosorption property. Meanwhile, ultrasound promoted the decline of the structure order of yeast cells induced by phenolic uptake. The interactions between yeast cells and phenolics were also affected by the structures of phenolics. Moreover, the mass transfer process was simulated by a surface diffusional model considering the ultrasound-induced yeast cell disruption. The modeling results showed that the external mass transfer coefficient in liquid phase and the surface diffusion coefficient under sonication at 394.2 W/L and 40 °C were 128.5% and 74.3% higher than that under reciprocating shaking, respectively.

This paper investigates building eave effect of fire-induced ejected plume from a room window and its heat flux imposing upon the facade wall. Based on a 1:8 cubic model and Froude modeling, a series of reduced scale experiments are set up. The window dimensions and total heat release rate due to a room fire are changed during experiments. The eave upon the facade wall, made of noncombustible fiber board is installed horizontally above the window top, varying with different vertical heights (distance of the eave to the window top) and lengths (direction normal to the facade wall). The facade flame height is recorded by a CCD camera, while the heat fluxes imposing upon the facade wall are collected by five water-cooled heat flux gauges. Results show that the ejected flame with horizontal eave can be distinguished into the “free flame stage”, the “flame horizontal spread stage” and the “flame overflow stage”. The visible flame height and heat flux within the “flame overflow stage” are detailed discussed, revealing the physical mechanism of the vertical height and length of the horizontal eave effects. Finally, an implicit correlation in regarding with the visible flame height, the updated flame extension area beneath the horizontal eave as well as the vertical height of the horizontal eave to the window top is established. Taking the horizontal eave conditions into consideration, the heat flux correlation is well corrected and proposed. The results would provide theoretical basis for the fire-induced thermal protection in high buildings.

In this paper, a novel method named distributed independent component–principal component regression (distributed ICPCR) is proposed to monitor the large-scale non-Gaussian process. First, multiple...

Highlights • Metabolic responses to ultrasonication made by Lactobacillus plantarum was studied. • Microbial growth, profiles of organic acids, amino acids and phenolics were examined. • Ultrasonication at lag and logarithmic phases benefited apple juice fermentation. • Ultrasonic effect on apple juice fermentation attenuated with fermentation. • Ultrasound could exert positive effect on derivation of some phenolic compounds., In this work, low-intensity ultrasonication (58.3 and 93.6 W/L) was performed at lag, logarithmic and stationary growth phases of Lactobacillus plantarum in apple juice fermentation, separately. Microbial responses to sonication, including microbial growth, profiles of organic acids profile, amino acids, phenolics, and antioxidant capacity, were examined. The results revealed that obvious responses were made by Lactobacillus plantarum to ultrasonication at lag and logarithmic phases, whereas sonication at stationary phase had a negligible impact. Sonication at lag and logarithmic phases promoted microbial growth and intensified biotransformation of malic acid to lactic acid. For example, after sonication at lag phase for 0.5 h, microbial count and lactic acid content in the ultrasound-treated samples at 58.3 W/L reached 7.91 ± 0.01 Log CFU/mL and 133.70 ± 7.39 mg/L, which were significantly higher than that in the non-sonicated samples. However, the ultrasonic effect on microbial growth and metabolism of organic acids attenuated with fermentation. Moreover, ultrasonication at lag and logarithmic phases had complex influences on the metabolism of apple phenolics such as chlorogenic acid, caffeic acid, procyanidin B2, catechin and gallic acid. Ultrasound could positively affect the hydrolysis of chlorogenic acid to caffeic acid, the transformation of procyanidin B2 and decarboxylation of gallic acid. The metabolism of organic acids and free amino acids in the sonicated samples was statistically correlated with phenolic metabolism, implying that ultrasound may benefit phenolic derivation by improving the microbial metabolism of organic acids and amino acids.

Graphical abstract, Highlights • Rapid disinfection of water containing E. coli is obtained by a pilot-scale ARHCR. • The ARHCR can cause serious cleavage and surface damages to E. coli. • Proposed disinfection mechanism includes hydrodynamical and sonochemical effects., Hydrodynamic cavitation is a promising technique for water disinfection. In the present paper, the disinfection characteristics of an advanced hydrodynamic cavitation reactor (ARHCR) in pilot scale were studied. The effects of various flow rates (1.4–2.6 m3/h) and rotational speeds (2600–4200 rpm) on the removal of Escherichia coli (E. coli) were revealed and analyzed. The variation regularities of the log reduction and reaction rate constant at various cavitation numbers were established. A disinfection rate of 100% was achieved in only 4 min for 15 L of simulated effluent under 4200 rpm and 1.4 m3/h, with energy efficiency at 0.0499 kWh/L. A comprehensive comparison with previously introduced HCRs demonstrates the superior performance of the presented ARHCR system. The morphological changes in E. coli were studied by scanning electron microscopy. The results indicate that the ARHCR can lead to serious cleavage and surface damages to E. coli, which cannot be obtained by conventional HCRs. Finally, a possible damage mechanism of the ARHCR, including both the hydrodynamical and sonochemical effects, was proposed. The findings of the present study can provide strong support to the fundamental understanding and applications of ARHCRs for water disinfection.

Highlights • Ultrasonic energy was input in both contact and airborne modes to dry blackberry. • Ultrasonic influences on heat and mass transfer were simulated numerically. • Shrinkage, input ultrasonic heat and temperature-dependent diffusivity were added. • Contact sonication was more capable to accelerate drying and save energy. • Contact sonication better preserved bioactives and flavors in blackberry., This study aimed at investigating the performances of air drying of blackberries assisted by airborne ultrasound and contact ultrasound. The drying experiments were conducted in a self-designed dryer coupled with a 20-kHz ultrasound probe. A numerical model for unsteady heat and mass transfer considering temperature dependent diffusivity, shrinkage pattern and input ultrasonic energies were applied to explore the drying mechanism, while the energy consumption and quality were analyzed experimentally. Generally, both airborne ultrasound and contact ultrasound accelerated the drying process, reduced the energy consumption and enhanced the retentions of blackberry anthocyanins and organic acids in comparison to air drying alone. At the same input ultrasound intensity level, blackberries received more ultrasound energies under contact sonication (0.299 W) than airborne sonication (0.245 W), thus avoiding the attenuation of ultrasonic energies by air. The modeling results revealed that contact ultrasound was more capable than airborne ultrasound to intensify the inner moisture diffusion and heat conduction, as well as surface exchange of heat and moisture with air. During air drying, contact ultrasound treatment eliminated the gradients of temperature and moisture inside blackberry easier than airborne ultrasound, leading to more homogenous distributions. Moreover, the total energy consumption under air drying with contact ultrasound assistance was 27.0% lower than that with airborne ultrasound assistance. Besides, blackberries dehydrated by contact ultrasound contained more anthocyanins and organic acids than those dried by airborne ultrasound, implying a higher quality. Overall, direct contact sonication can well benefit blackberry drying in both energy and quality aspects.

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

Access control encryption (ACE) is a useful concept introduced by Damgard, Haagh and Orlandi in TCC 2016, which not only protects the data privacy but also controls the information flow. However, their DDH-based scheme suffered from the ciphertext revealing attack (CRA) introduced by Badertscher, Matt and Maurer in Asiacrypt 2017 and just satisfied chosen plaintext attack (CPA) security. Badertscher, Matt and Maurer strengthened the security model to chosen ciphertext attack (CCA) security and constructed a CCA secure scheme under Naor-Yung paradigm. However, they did not indicate how to fix the DDH-based scheme proposed by Damgard, Haagh and Orlandi. Their CCA secure scheme is inefficient and complicated due to the non-interactive zero knowledge proofs (NIZKs) of a very complicated relation. And their scheme is constructed just for limited communication policies.

Identity-Based Cryptography (IBC) is a useful tool for the security of IoT devices, but securely deploying this cryptographic technique to the IoT systems is quite challenging. For instance, a leakage of the master secret key will result in the leakage of all IoT devices’ private keys. SM9 is the only approved IBC algorithm standard in China. It is critical to have mechanisms to protect the SM9 master secret keys. In this work, to reduce the risk of the master secret key leakage, we propose a (t, n)-threshold distributed private key generation scheme for SM9 with some techniques from multiparty computation. Our scheme is compatible with all the three SM9 sub-algorithms (i.e., the encryption, signature and key agreement). It is also provably secure and completely eliminates the single point of failures in SM9 that is concerned by the industry. The experimental analysis indicates that the proposed scheme is efficient, e.g., up to 1 million private key generation requests can be handled per day.

In this study, 4 Lactobacillus plantarum strains and 5 Lactobacillus fermentum strains adapting well to the unfavorable fruit system were isolated under different fruit environments. The fermentation ability of these autochthonous lactic acid bacteria (LAB) strains in blueberry juice, and the influence of microbial metabolism on juice composition were explored. After 48 h of fermentation, the viable cell counts exceeded 10.0 log CFU/mL, malic acid content decreased from 511.47 ± 10.50 mg/L to below 146.38 ± 3.79 mg/L, and lactic acid content increased from 0 mg/L to above 2184.90 ± 335.80 mg/L. Moreover, the metabolism of these strains exerted a profound influence on the phenolic composition of juice. Total phenolic content in blueberry juice increased by 6.1–81.2% under lactic acid fermentation, and the antioxidant capacity in vitro enhanced by at least 34.0%. Anthocyanin content showed a declining trend, while the profile of non-anthocyaninic phenolics exhibited complex changes. The increments of rutin, myricetin and gallic acid contents through 48 h lactic acid fermentation exceeded 136%, 71% and 38%, respectively. Instead, the contents of p-hydroxybenzoic acid and caffeic acid decreased with fermentation. Overall, Lactobacillus plantarum LSJ-TY-HYB-T9 and LSJ-TY-HYB-T7, and Lactobacillus fermentum LSJ-TY-HYB-C22 and LSJ-TY-HYB-L16 could be the suitable strains to produce fermented fruit juices, including blueberry in practical applications.

Titanium alloy is widely used in the area of aerospace and aviation due to its excellent properties. Eddy current testing (ECT) is among the most extensively used non-destructive techniques for titanium alloy material inspection. However, most previous research has focused on inspecting defects far from the edge of the material. It is a challenging task for edge crack detection because of edge effect. This study aims to investigate the influences of sensor parameters on edge effect and defect detection capability, and in the meantime, optimize sensor parameters to improve the capability of edge defect detection. The simulation method for edge effect evaluation is proposed including the 2k factorial design used for factor screening, and the regression model is fitted and validated for sensor design and optimization for edge defect detection. A simulation scheme is designed to investigate the defect detection capability. An approach comprehensively analyzing the influence of coil parameters on edge effect and defect detection capability is applied to determine the optimal coil parameters for edge defect detection.

The capacitated arc routing problem is a challenging combinatorial optimization problem with numerous real-world applications. In recent years, several multi-objective optimization algorithms have been applied to minimize both the total cost and makespan for capacitated arc routing problems, among which the decomposition-based memetic algorithm with extended neighborhood search has shown promising results. In this paper, we propose an improved decomposition-based memetic algorithm with extended neighborhood search, called D-MAENS2, which uses a novel method to construct a gene pool to measure and improve the diversity of solutions in decision variable space. Additionally, D-MAENS2 is capable of adapting online its hyper-parameters to various problem instances. Experimental studies show that our novel D-MAENS2 significantly outperforms D-MAENS on 81 benchmark instances and shows outstanding performance on instances of large size.

Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconstruction, and the reconstruction combines the current sample as the input of the sparse stack autoencoder (SSAE) to extract the correlation features between the current sample and the neighborhood information. Two statistics are constructed for fault detection. Considering that both types of neighborhood information contain spatial-temporal structural features, Bayesian fusion strategy is used to integrate the two parts of the detection results. Finally, the superiority of the method in this paper is illustrated by a numerical example and the Tennessee Eastman process.

The industrial control system (ICS) is facing increasing threats in underline communication infrastructure. The mathematical model of communication network traffic in ICS plays a crucial part in the precaution of the cyberattacks. To this end, this paper proposes an integrated prediction approach using learning rate exponential decay (ED-LSTM) method and fuzzy information granulation. The proposed prediction approach is designed to characterize the traffic patterns of ICS for both point and interval prediction. The traffic pattern prediction is essential to characterize the operation behaviors and unique traits in ICS. The experiments and numerical results demonstrate that the proposed integrated prediction approach outperforms the other prediction models in both point prediction and interval prediction.

The layout of temperature sensors mainly designed for the requirement of thermal management. The acquisition of temperature rise of lithium-ion battery is to get the condition for cooling on. However, in some extreme cases, the placed temperature sensors can not detect the precursor of thermal runaway of a specific cell. The objective of this paper is to optimize the temperature sensor placement to satisfy both thermal management and thermal runaway requirement. To achieve the goal, The temperature sensors placement of lithium-ion battery module was analyzed under charging and discharging conditions for thermal management requirement. Then, the temperature distribution of battery module was studied considering thermal runaway and the optimization results of temperature sensors placement was given. The temperature signal of the cell experiencing thermal runaway can be detected by the neighboring cell. In order to satisfy the requirement for thermal management and thermal runaway, one temperature sensor is placed every two cells on the middle part of the cell and two temperature sensors are placed on the upper part of the side cells.

In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical Process Monitoring (MSPM) methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor (DALOF). This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman (TE) process simulation demonstrate the effectiveness of DALOF.

Aramid fiber–reinforced plastic (AFRP) composites have been widely used in automotive, aerospace, and defense industries. The common AFRP drilling process tends to cause damage to the composite structures which subsequently affects their fatigue lives and in-service performance. Understanding the mechanism of cutting force generation is crucial in controlling the cutting process for achieving desired hole quality and machining accuracy. This study proposes a novel mechanistic model considering both the cutting action and the extrusion action of the chisel edge. For the first time, the extrusion force generated by the chisel edge has been considered as a rigid wedge penetrating into an elastic half space based on the Hertz contact theory. The total thrust force in AFRP drilling is divided into three components: (i) thrust force generated by the cutting lips, (ii) thrust force generated by the chisel edge cutting action, and (iii) extrusion force generated by the chisel edge extrusion action. The proposed model was then validated by experiments and data was compared with the case where extrusion was not considered. The results show that our novel mechanistic model can provide a more accurate thrust force prediction. The average error of our model was 2.54% against the experimental data, whereas the error seen in conventional model without accounting extrusion was 8.22%. This suggests that the chisel edge extrusion plays a significant part in the drilling of AFRP and hence confirms the necessity of considering extrusion in establishing the associated mechanistic model.

In the construction of metro tunnels, water gushing accidents caused by the rupture of underground pipeline often occur, and in loess area, the collapsibility of loess makes this problem more complex and difficult. To investigate the damage of metro tunnel caused by collapsible loess under the action of local dynamic water (gushing water), a model experiment was conducted based on the pipeline water gushing accident happened in the construction of metro tunnel located in loess area. Through the study of similar materials of loess and tunnel lining, the test materials and apparatus were prepared according to similarity criterion. By simulating water gushing environment in the loess stratum, this paper analyzed mechanical characteristics of tunnel (water pressure of surrounding rock, contact pressure and internal force of tunnel lining) and deformation of surrounding rock and tunnel. Furthermore, combining with the process of local collapse of loess in the model experiment, it is concluded that the formation of water transport channel is the main reason for the difference of tunnel structural response when water gushing occurs at different locations. Finally, a three-dimensional spatial model of water transport channel in loess stratum under the environment of local water gushing was established to study the rule of water transport.

Almond skin is an important by-product in the almond processing industry, rich in potentially health-promoting phenolic compounds. The objective of this present study is to separate the skin from the almond and extract its polyphenol contents using Ultrasound-Assisted Extraction (UAE) at room temperature. Optimization was performed according to a two-variable central composite design (CCD), and the optimum combination of ultrasonic intensity and extraction temperature was obtained through multi-response optimization: ultrasonic intensity (UI), 9.47 W.cm&minus, 2, and temperature, 20 &deg, C for an extraction time of 20 min. Under the above-mentioned conditions, total phenolic content was 258% higher with UAE than silent experiment. Mathematic modelling and microscopic investigations were achieved to enable understanding physical and structural effects of ultrasound on almond skins and comprehension of the mechanism behind the enhancement of mass transfer phenomena. Scanning Electron Microscopy (SEM) showed different acoustic cavitation impacts including fragmentation, sonoporation, and erosion. Extracts were analyzed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS), identifying a combination of flavanols, flavanones and non-flavonoids. UAE shows no negative effect on almond proteins and lipids when compared to natural almonds (NS).

Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifically, view-specific (uncorrelated) features are identified for each view when learning the common (correlated) feature across views in the latent semantic subspace. By eliminating the effects of uncorrelated information, useful inter-view feature correlations can be captured. We design a new objective function in CoUFC and derive an optimization approach to solve the objective with the analysis on its convergence. Experiments on real-world sensor, image, and text data sets demonstrate that the proposed method outperforms the state-of-the-art multiview learning methods.

This paper focuses on designing, modeling and validating a novel scalable multicopter whose deformation mechanism, called SNIAE-SSE, relies on a combination of simple non-intersecting angulated elements (SNIAEs) and straight scissor-like elements (SSEs). The proposed SNIAE-SSE mechanism has the advantages of single degree-of-freedom, fast actuation capability and large deformation ratio. In this work, enabled by the SNIAE-SSE mechanism, a quadcopter prototype with symmetrical and synchronous deformation is firstly developed, which facilitates a novel and controllably scalable multicopter system for us to analyze its modeling, as well as to validate its flight performance and dynamics during the deformation in several flight missions including hover, throwing, and morphing flying through a narrow window. Experimental results demonstrate that the developed scalable multicopter can maintain its stable flight behavior even both the folding and unfolding body deformations are fast performed, which indicates an excellent capability of the scalable multicopter to rapidly adapt to complex and dynamically changed environments.

Networked telerobots are operated by humans through remote interactions and have found applications in hazardous and/or unstructured environments, such as outer space, underwater, telesurgery, manufacturing, and production. In precision agricultural robotics, target monitoring, recognition, and detection is a complex task, requiring expertise. Hence, they can be more efficiently performed by collaborative human-robot systems. A HUB is an online portal, a platform to create and share scientific and advanced computing tools. Multiple HUBs have been developed recently for scientific research objectives. HUB-CI is a tool developed by PRISM Center at Purdue University to enable cyber-augmented collaborative interactions over cyber-supported complex systems. The research reported here, sponsored in part by BARD, implements the HUB-CI model to improve the Collaborative Intelligence (CI) of an agricultural telerobotic system for early detection of anomalies in pepper plants grown in greenhouses. Specific CI tools developed for this purpose include: (1) spectral image segmentation for detecting and mapping to anomalies in growing pepper plants; (2) workflow/task administration protocols for managing/coordinating interactions between software, hardware, and human agents, engaged in the monitoring and detection, which would reliably lead to precise, responsive mitigation. These CI tools aim to minimize interactions’ conflicts and errors that may impede detection effectiveness, thus reducing crops’ quality. On-going lab and field experiments indicate that planned and optimized collaborative interactions with HUB-CI (as opposed to ad-hoc or fixed-structured interactions) yield significantly fewer errors and better detection. Hence, it improves system productivity of precise monitoring for healthy growth of pepper plants in greenhouses.

Traditional quality-related process monitoring mainly focuses on the magnitude change of the quality variables caused by additive faults. However, the abnormal fluctuations in the quality variables caused by multiplicative faults are often overlooked. In this paper, a novel parallel dynamic principal component regression (P-DPCR) algorithm is proposed to monitor the changes in the magnitude and fluctuation of the quality variables simultaneously. Firstly, in order to eliminate the interference of quality-unrelated variables, the quality-related process variables are selected on the basis of correlation analysis. Secondly, the dynamic extension and moving window are carried out for process variables and quality variables, in which the dynamic variables space (called X-space/Y-space) and the variance space (called VX-space/VY-space) are constructed. Afterwards, double quality-related statistics based on the regression model of these four spaces are given, and the comprehensive monitoring decision can be obtained. Finally, two numerical cases and the Tennessee Eastman process are used to show the effectiveness of the proposed method.

In this paper we introduce the first spatial-temporal-textual framework that can facilitate the validation and optimization of indoor navigation systems using trial participants continuously recorded feedback. The proposed framework enables us to pinpoint specific areas of improvement such as which part of the user interface requires changes, what areas in the environment necessitate modifications in the localization algorithm, and/or which instructions should be improved. Conventional evaluation of such systems is based on collecting users' feedback after the trials in the form of interviews and/or questionnaires. This form of evaluation while necessary and important, it provides a summary of the users' view of the system. In contrast, the proposed framework provides a significant improvement by refining the user feedback resolution by continuously collecting the following information in a spatial-temporal context during the trials: user comments, user interface interactions, and navigation instructions. The framework includes the following four main components: 1) trial data preprocessing; 2) comments textual analysis; 3) spatial/temporal/instructions analysis, and 4) results visualization. We introduce a case study that illustrates the use of the framework using PERCEPT indoor navigation system for blind and visually impaired users.

Partial least squares (PLS) and linear regression methods have been widely utilized for quality-related fault detection in industrial processes recently. Since these traditional approaches assume that process variables follow Gaussian distribution approximately, their effectiveness will be challenged when facing non-Gaussian processes. To deal with this difficulty, a new quality relevant process monitoring approach based on improved independent component regression (IICR) is presented in this article. Taking high-order statistical information into account, ICA is performed onto process data to produce independent components (ICs). In order to remove irrelevant variation orthogonal to quality variable and keep as much quality-related fault information as possible, a new quality-related independent components selection method is applied to these ICs. Then the regression relationship between filtered ICs and the product quality is built. QR decomposition for regression coefficient matrix is able to give out quality-related and quality-unrelated projectors. After the measured variable matrix is divided into quality relevant and quality irrelevant parts, novel monitoring indices are designed for fault detection. finally, applications to two simulation cases testify the effectiveness of our proposed quality-related fault detection method for non-Gaussian processes.

Abutment pressure distribution is different when a longwall panel is passing through the abandoned gate roads in a damaged coal seam. According to the geological condition of panel E13103 in Cuijiazhai Coal Mine in China, theoretical analysis and finite element numerical simulation were used to determine the front pressure distribution characteristics when the longwall face is 70, 50, 30, 20, 10, and 5 m from the abandoned roadways. The research results show that the influence range of abutment pressure is 40 to 45 m outby the face, and the peak value of front abutment pressure is related to the distance between the face and abandoned roadways. When the distance between the longwall face and abandoned roadways is reduced from 50 to 10 m, the front abutment pressure peak value kept increasing. When the distance is 10 m, it has reached the maximum. The peak value is located in 5 to 6 m outby the faceline. When the distance between the longwall face and abandoned roadways is reduced from10 to5 m, the front abutment pressure sharply decreases, the intact coal yields and is even in plastic state. The peak value transfers to the other side of the abandoned roadways. The research results provide a theoretical basis for determining the advance support distance of two roadways in the panel and the reinforcement for face stability when the longwall face is passing through the abandoned roadways. Keywords: Abandoned roadways, Abutment pressure, Theoretical calculation, Numerical simulation

In order to detect coal and gas outburst disasters by means of electric exploration technology, the characteristics of gas adsorption/desorption electrical response were studied. The law of resistivity variation of different coal samples was investigated under different gas pressures with the aid of a self-built real-time test system of coal resistivity during gas adsorption/desorption. In addition, the mechanism of coal resistivity variation was studied. The research results indicate that coal resistivity decreases during gas adsorption and increases during gas desorption, but generally it cannot return to the initial value. Gas influences coal resistivity through a variety of mechanisms. In the gas adsorption process, coal resistivity decreases under the combined effect of surface energy decline, skeleton expansion, free gas pressure and adsorption swelling stress. In addition, as the gas pressure rises, the resistivity varies in a wider range. The coal resistivity shares the relation of y = a + b ln(x + c) with gas pressure and the relation of y = ax + b with the content of adsorbed gas. This study lays a foundation for the application of electrical prospecting technology to gas hazard prediction and provides technical support for safe production in coal mines.

Electro-hydraulic servo driving planar parallel mechanism (EHPM) is widely used in simulating large-scale vibration environments. However, the energy consumption of EHPM is large. There is little research on the energy consumption of EHPM. Therefore, this paper presents a structural optimization method, which can effectively reduce the energy consumption of EHPM by changing the arrangement of the planar redundant actuator. First, the mathematical model of the total power of EHPM is established. In order to distinguish the variables that affect energy consumption, the effect of a single structural variable on energy consumption is discussed, respectively, based on the mathematical model of the total power. And then the genetic algorithm is used to optimize the total power objective function with multiple structural variables. The influence of the initial value of the structure variable on the convergence range of total power is also discussed. The simulation results show the effectiveness of changing the actuator arrangement, which can significantly reduce energy consumption. Moreover, the component selecting of the hydraulic cylinder and servo valve can be smaller and the cost of which can be reduced.