Upcycling Steel Slag in Producing Eco-Efficient Iron–calcium Phosphate Cement

In the present study, steel slag powder (SSP) was utilized as the raw material to prepare iron-calcium phosphate cement (ICPC) by reacting with ammonium dihydrogen phosphate (ADP). The influences of the raw materials (SSP/ADP) mass ratios ranging from 2.0 to 7.0 on the properties and microstructures of ICPC pastes were investigated. The compressive strengths of ICPC pastes at all ages firstly increased and then decreased with the increase of SSP/ADP, and the SSP/ADP of 6.0 gave the highest strength. Crystalline mundrabillaite and amorphous phases [i.e. Fe(OH)3, Al(OH)3 and H4SiO4] were formed as the dominant binding phases through the reactions of the calcium-containing compounds (brownmillerite, monticellite and srebrodolskite) in the steel slag and ADP. Further, ADP could also react with the free FeO contained in the steel slag to yield amorphous iron phosphate phase. BSE analysis indicated that the hydration products formed and growed on the surface of steel slag particles and connect them to form the continuous, dense microstructure of ICPC paste. The utilization of high-volume steel slag as the base component will potentially bring great economic and environmental benefits for the manufacture of phosphate cement

Evaluating On-line Model Checking in UPPAAL-SMC using a Laser Tracheotomy Case Study

On-line model checking is a variant of model checking that evaluates properties of a system concurrently while deployed, which allows overcoming limitations of inaccurate system models. In this paper we conduct a laser tracheotomy case study to evaluate the feasibility of using the statistical model checker UPPAAL-SMC for on-line model checking in a medical application. Development of automatic on-line model checking relies on the precision of the prediction and real-time capabilities as real-time requirements must be met. We evaluate the case study with regards to these qualities and our results show that using UPPAAL-SMC in an on-line model checking context is practical: relative prediction errors were only 2% on average and guarantees could be established within reasonable time during our experiments

Influence of Phosphorus Sources on the Compressive Strength and Microstructure of Ferronickel Slag-Based Magnesium Phosphate Cement

Electric furnace ferronickel slag (EFS) is a typical magnesium-rich industrial by-product discharged from the manufacture of nickel and iron-nickel alloys. The approach to use it as the raw material for the preparation of magnesium phosphate cement (MPC) has potential and proves effec-tive. In this study, three different phosphorus sources (PS) including phosphoric acid (H3PO4, PA), sodium dihydrogen phosphate (NaH2 PO4, SDP) and potassium dihydrogen phosphate (KH2 PO4, PDP) were used to react with EFS to prepare the EFS-based MPC (EMPC), and the effects of raw material mass ratio (EFS/PA, EFS/SDP, EFS/PDP) on the compressive strength, early hydration temperature and microstructure of EMPC pastes were investigated. Results showed that the compressive strength of EMPC paste is significantly impacted by the type of phosphorus source and the raw materials mass ratio. When the EFS/PDP ratio is 4.0, the compressive strength of the MPC paste reaches up to 18.8, 22.8 and 27.5 MPa at 3, 7 and 28 d, respectively. Cattiite (Mg3(PO4 )2·22H2 O), K-struvite (KMgPO4·6H2O) and/or Na-struvite (NaMgPO4·6H2O) were identified as the main hydration products of EMPC. The development of EMPC mainly involves the dissolution of a phosphorus source, MgO and Mg2SiO4, formation of hydration product as binder, and combination of the unreacted raw materials together by binders to build a compact form

Influence of Phosphorus Sources on the Compressive Strength and Microstructure of Ferronickel Slag-Based Magnesium Phosphate Cement

Electric furnace ferronickel slag (EFS) is a typical magnesium-rich industrial by-product discharged from the manufacture of nickel and iron-nickel alloys. The approach to use it as the raw material for the preparation of magnesium phosphate cement (MPC) has potential and proves effec-tive. In this study, three different phosphorus sources (PS) including phosphoric acid (H3 PO4, PA), sodium dihydrogen phosphate (NaH2 PO4, SDP) and potassium dihydrogen phosphate (KH2 PO4, PDP) were used to react with EFS to prepare the EFS-based MPC (EMPC), and the effects of raw material mass ratio (EFS/PA, EFS/SDP, EFS/PDP) on the compressive strength, early hydration temperature and microstructure of EMPC pastes were investigated. Results showed that the compressive strength of EMPC paste is significantly impacted by the type of phosphorus source and the raw materials mass ratio. When the EFS/PDP ratio is 4.0, the compressive strength of the MPC paste reaches up to 18.8, 22.8 and 27.5 MPa at 3, 7 and 28 d, respectively. Cattiite (Mg3 (PO4 )2·22H2 O), K-struvite (KMgPO4·6H2 O) and/or Na-struvite (NaMgPO4·6H2 O) were identified as the main hydration prod-ucts of EMPC. The development of EMPC mainly involves the dissolution of a phosphorus source, MgO and Mg2 SiO4, formation of hydration product as binder, and combination of the unreacted raw materials together by binders to build a compact form

Influence of characteristic parameters of signal on fault feature extraction of singular value method

The detection of mechanical fault signals by singular value decomposition is a commonly used method in fault diagnosis. The delay time of the fault signal time series and the rationality of the value of the phase space embedding dimension, as well as the fluctuation of the characteristic parameters of the fault signal, will cause the singular value decomposition method to have a greater impact on the accuracy of fault feature identification and diagnosis. In this article, the simulation model of the similarity signal is established by the combination of the autocorrelation function method and the Cao’s algorithm. Then, the delay time of the signal sequence and the optimal value of the embedded dimension are obtained through simulation. Next, using this method to study the fluctuation of the characteristic parameters such as the frequency, amplitude and initial phase of the signal, the relationship between the characteristic parameters of the signal and the singular value of the signal is obtained. Finally, through the experimental study of the pitting corrosion of the gear tooth surface, the vibration of the fault feature is obtained. The research shows that the combination of autocorrelation function method and Cao's algorithm can calculate the optimal characteristic parameters for the singular value decomposition method and improve the ability of the method to identify fault features

A Novel Iron Phosphate Cement Derived from Copper Smelting Slag and its Early Age Hydration Mechanism

Copper slag (CS), a by-product of copper smelting, is normally stockpiled, leading to wastes of resource and space as well as environment pollution. It has not been massively reutilized as a supplementary cementitious material in Portland cement due to its low reactivity. In the present study, CS is for the first time utilized as the base component to prepare an iron phosphate cement (IPC) by reacting with ammonium dihydrogen phosphate (ADP) at room temperature. The influence of the raw materials mass ratio (CS/ADP) on the microstructure and performance of IPC pastes are investigated. It is found that the compressive strength of IPC pastes at all ages is not a monotonic function of CS/ADP, and the paste with CS/ADP of 2.0 gives the highest strengths, i.e., 26.8, 38.9 and 47.5 MPa at 1, 3 and 28 d, respectively. The crystalline phases including FeH2P3O10·H2O and FePO4 are formed as the main reaction products to bind the unreacted CS particles. The early age hydration of IPC is found to be a multi-stage process, involving the initial dissolution of ADP and iron-containing phases of CS, the formation of FeH2P3O10·H2O, the initial generation of FePO4, and the attainment of the hydration reaction equilibrium. Unlike the magnesium phosphate cement, a redox reaction of Fe(Ⅱ) into Fe(Ⅲ) occurs due to the suitable range of pH and oxidation-reduction potential of the IPC system during the hydration reaction

Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos

Generating text-editable and pose-controllable character videos have an imperious demand in creating various digital human. Nevertheless, this task has been restricted by the absence of a comprehensive dataset featuring paired video-pose captions and the generative prior models for videos. In this work, we design a novel two-stage training scheme that can utilize easily obtained datasets (i.e.,image pose pair and pose-free video) and the pre-trained text-to-image (T2I) model to obtain the pose-controllable character videos. Specifically, in the first stage, only the keypoint-image pairs are used only for a controllable text-to-image generation. We learn a zero-initialized convolu- tional encoder to encode the pose information. In the second stage, we finetune the motion of the above network via a pose-free video dataset by adding the learnable temporal self-attention and reformed cross-frame self-attention blocks. Powered by our new designs, our method successfully generates continuously pose-controllable character videos while keeps the editing and concept composition ability of the pre-trained T2I model. The code and models will be made publicly available.Comment: Project page: https://follow-your-pose.github.io/; Github repository: https://github.com/mayuelala/FollowYourPos

Efficient isolation of high quality RNA from tropical palms for RNA-seq analysis

Currently, RNA-seq as a high throughput technology is being widely applied to various species to elucidate the complexity of the transcriptome and to discover large number of novel genes. However, the technology has had poor success in elucidating the transcriptome of tropical palms, as it is difficult to isolate high quality RNA from tropical palm tissues due to their high polysaccharide and polyphenol content. Here, we developed an RNA-isolation protocol for tropical palms, the MRIP method (Methods for RNA Isolation from Palms). The integrity of the RNA molecules extracted using this protocol was determined to be of high quality by means of gel electrophoresis and Agilent 2100 Bioanalyzer microfluidic electrophoresis chip examination with a RIN (RNA Integrity Number) value of more than 9, indicating that the mRNAs were of good integrity. Subsequently the isolated RNA was used for transcription analysis without further purification. With Illumina sequencing, we obtained 54.9 million short reads and then conducted de novo assembly to gain 57,304 unigenes with an average length of 752 base pairs. Moreover, the RNA isolated with this protocol was also successfully used for real-time RT-PCR. These results suggested that the RNA isolated was suitable for Illumina RNA sequencing and quantitative real-time RT-PCR. Furthermore, this method was also successful in isolating total RNA from the leaves of various Palmaceae species

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions