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

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

Consensus Adversarial Defense Method Based on Augmented Examples

Deep learning has been used in many computer-vision-based industrial Internet of Things applications. However, deep neural networks are vulnerable to adversarial examples that have been crafted specifically to fool a system while being imperceptible to humans. In this study, we propose a consensus defense (Cons-Def) method to defend against adversarial attacks. Cons-Def implements classification and detection based on the consensus of the classifications of the augmented examples, which are generated based on an individually implemented intensity exchange on the red, green, and blue components of the input image. We train a convolutional neural network using augmented examples together with their original examples. For the test image to be assigned to a specific class, the class occurrence of the classifications on its augmented images should be the maximum and reach a defined threshold. Otherwise, it is detected as an adversarial example. The comparison experiments are implemented on MNIST, CIFAR-10, and ImageNet. The average defense success rate (DSR) against white-box attacks on the test sets of the three datasets is 80.3%. The average DSR against black-box attacks on CIFAR-10 is 91.4%. The average classification accuracies of Cons-Def on benign examples of the three datasets are 98.0%, 78.3%, and 66.1%. The experimental results show that Cons-Def shows a high classification performance on benign examples and is robust against white-box and black-box adversarial attacks

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

StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image Generation

This paper presents a LoRA-free method for stylized image generation that takes a text prompt and style reference images as inputs and produces an output image in a single pass. Unlike existing methods that rely on training a separate LoRA for each style, our method can adapt to various styles with a unified model. However, this poses two challenges: 1) the prompt loses controllability over the generated content, and 2) the output image inherits both the semantic and style features of the style reference image, compromising its content fidelity. To address these challenges, we introduce StyleAdapter, a model that comprises two components: a two-path cross-attention module (TPCA) and three decoupling strategies. These components enable our model to process the prompt and style reference features separately and reduce the strong coupling between the semantic and style information in the style references. StyleAdapter can generate high-quality images that match the content of the prompts and adopt the style of the references (even for unseen styles) in a single pass, which is more flexible and efficient than previous methods. Experiments have been conducted to demonstrate the superiority of our method over previous works.Comment: AIG

Using outlier elimination to assess learning-based correspondence matching methods

Recently, deep learning (DL) technology has been widely used in correspondence matching. The learning-based models are usually trained on benign image pairs with partial overlaps. Since DL model is usually data-dependent, non-overlapping images may be used as poison samples to fool the model and produce false registrations. In this study, we propose an outlier elimination based assessment method (OEAM) to assess the registrations of learning-based correspondence matching method on partially overlapping and non-overlapping images. OEAM first eliminates outliers based on spatial paradox. Then OEAM implements registration assessment in two streams using the obtained core correspondence set. If the cardinality of the core set is sufficiently small, the input registration is assessed as a low-quality registration. Otherwise, it is assessed to be of high quality, and OEAM improves its registration performance using the core set. OEAM is a post-processing technique imposed on learning-based method. The comparison experiments are implemented on outdoor (YFCC100M) and indoor (SUN3D) datasets using four deep learning-based methods. The experimental results on registrations of partially overlapping images show that OEAM can reliably infer low-quality registrations and improve performance on high-quality registrations. The experiments on registrations of non-overlapping images demonstrate that learning-based methods are vulnerable to poisoning attacks launched by non overlapping images, and OEAM is robust against poisoning attacks crafted by non-overlapping images

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Safety and feasibility of laparoscopic radical resection for bismuth types III and IV hilar cholangiocarcinoma: a single-center experience from China

BackgroundSurgery represents the only cure for hilar cholangiocarcinoma (HC). However, laparoscopic radical resection remains technically challenging owing to the complex anatomy and reconstruction required during surgery. Therefore, reports on laparoscopic surgery (LS) for HC, especially for types III and IV, are limited. This study aimed to evaluate the safety and feasibility of laparoscopic radical surgery for Bismuth types III and IV HC.MethodsThe data of 16 patients who underwent LS and 9 who underwent open surgery (OS) for Bismuth types III and IV HC at Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, between December 2017 and January 2022 were analyzed. Basic patient information, Bismuth–Corlette type, AJCC staging, postoperative complications, pathological findings, and follow-up results were evaluated.ResultsSixteen patients underwent LS and 9 underwent OS for HC. According to the preoperative imaging data, there were four cases of Bismuth type IIIa, eight of type IIIb, and four of type IV in the LS group and two of type IIIa, four of type IIIb, and three of type IV in the OS group (P>0.05). There were no significant differences in age, sex, ASA score, comorbidity, preoperative percutaneous transhepatic biliary drainage rate, history of abdominal surgery, or preoperative laboratory tests between the two groups (P>0.05). Although the mean operative time and mean intraoperative blood loss were higher in the LS group than in OS group, the differences were not statistically significant (P=0.121 and P=0.115, respectively). Four patients (25%) in the LS group and two (22.2%) in the OS group experienced postoperative complications (P>0.05). No significant differences were observed in other surgical outcomes and pathologic findings between the two groups. Regarding the tumor recurrence rate, there was no difference between the groups (P>0.05) during the follow-up period (23.9 ± 13.3 months vs. 17.8 ± 12.3 months, P=0.240).ConclusionLaparoscopic radical resection of Bismuth types III and IV HC remains challenging, and extremely delicate surgical skills are required when performing extended hemihepatectomy followed by complex bilioenteric reconstructions. However, this procedure is generally safe and feasible for hepatobiliary surgeons with extensive laparoscopy experience

Generation of Human Epidermis-Derived Mesenchymal Stem Cell-like Pluripotent Cells and their reprogramming in mouse chimeras

Stem cells can be derived from the embryo (embryonic stem cells, ESCs), from adult tissues (adult stem cells, ASCs), and by induction of fibroblasts (induced pluripotent stem cells, iPSs). Ethical problems, immunological rejection, and difficulties in obtaining human tissues limit the use of ESCs in clinical medicine. Induced pluripotent stem cells are difficult to maintain in vitro and carry a greater risk of tumor formation. Furthermore, the complexity of maintenance and propagation is especially difficult in the clinic. Adult stem cells can be isolated from several adult tissues and present the possibility of self-transplantation for the clinical treatment of a variety of human diseases. Recently, several ASCs have been successfully isolated and cultured in vitro, including hematopoietic stem cells (HSCs) , mesenchymal stem cells (MSCs), epidermis stem cells, neural stem cells (NSCs), adipose-derived stem cells (ADSCs), islet stem cells, and germ line stem cells. Human mesenchymal stem cells originate mainly from bone marrow, cord blood, and placenta, but epidermis-derived MSCs have not yet been isolated. We isolated small spindle-shaped cells with strong proliferative potential during the culture of human epidermis cells and designed a medium to isolate and propagate these cells. They resembled MSCs morphologically and demonstrated pluripotency in vivo; thus, we defined these cells as human epidermis-derived mesenchymal stem cell-like pluripotent cells (hEMSCPCs). These hEMSCPCs present a possible new cell resource for tissue engineering and regenerative medicine