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As an essential community of practice which could facilitate teachers' professional development and enrich research in teacher education, the teacher--researcher partnership (TRP) has not been sufficiently investigated in China. As reported in this paper, we conducted an empirical study on TRPs between secondary school mathematics teachers and university researchers by applying activity theory to investigate their collaboration and contradictions, through the process of co-designing lesson studies. We found four levels of contradictions, namely, primary, secondary, tertiary, and quaternary, within which the fourth one occurs less frequently compared to the other three. To ameliorate or even resolve the contradictions, it requires both sides to (1) achieve agreement on understanding the objects, (2) promote equality in communication and mutual respect, (3) facilitate transformation in identity, and (4) construct a united community. In addition, the investigation provided a depiction of how teachers learn and grow in the TRP as well as how teachers and researchers interact in the partnership, which in turn enriches both teacher education and activity theory.

The extensive accumulation of coal-derived solid wastes in China has become a significant environmental challenge. Utilizing these wastes as resources is considered an effective approach. This study investigated activation systems for coal gangue to determine the optimal dosage for incorporation into cementitious materials. The effects of types and dosages of admixtures on the mechanical properties were examined. The results showed that augmenting the coal gangue content within a certain range enhanced the compressive strength of the composite cementitious materials. However, exceeding 20 wt.% led to a decline in compressive strength. The addition of coal gangue led to an increase in porosity. Furthermore, the addition of 4% lime and 4% gypsum resulted in enhanced properties. The performance of the resultant optimal cementitious material surpasses that of P·O 32.5 cement, making it a viable replacement in construction sector and contributing to the realization of resource recycling and ecological civilization construction goals. [ABSTRACT FROM AUTHOR]

Hydrogen sulfide (H2S) contamination of food has raised widespread public health concerns, leading to substantial medical and economic burdens. Herein, a 2D/0D heterojunction fluorescent probe (TCTG) with Schottky barriers (SB) is designed and synthesized, utilizing Ti3C2Tx MXene‐loaded graphene quantum dots (GQDs), for the detection of H2S during food spoilage. The microstructures observed through SEM and TEM reveal that uniformly sized GQDs are evenly attached to the surface of a monolayer Ti3C2Tx. The chemisorption between GQDs and Ti3C2Tx facilitates charge transfer and the formation of SB, resulting in intramolecular charge transfer (ICT) effects. With the introduction of H2S, TCTG(50%) exhibits the highest sensitivity, selectivity, and anti‐interference properties, with ultra‐fast fluorescence transient reaction (3s) and remarkably low detection limit of 41.82 ppb as well as noticeable color change. When TCTG(50%) reacted with H2S, the ICT effects are inhibited, leading to the recovery of photoinduced electron transfer (PET) and fluorescence quenching. Notably, probe TCTG is effectively utilized to detect changes in H2S levels in raw foods to assess their quality. Overall, the significance of this study is its potential to revolutionize food spoilage detection, offering a fast, reliable, and sensitive method to ensure food safety and reduce associated health and economic burdens. [ABSTRACT FROM AUTHOR]

The accumulation of phosphogypsum (PG) has become a significant environmental and economic challenge, making PG reuse an urgent concern. This study aims to explore the potential of combining PG with steel slag (SS), ground granulated blast-furnace slag (GGBS), and cement clinker (CC) to create excessive-sulfate phosphogypsum slag cement (EPSC) for use in self-compacting concrete (SCC). However, residual acid and other impurities in PG can significantly slow down the setting time of EPSC. To optimize the setting time, different methods including wet grinding, washing, and the addition of pure gypsum, limestone powder (LP), and aluminum cement (AC) are used. Results show that the wet grinding method and washing method remove soluble phosphorus impurities and reduce fluorine ion levels by 64%. However, nonsoluble impurities that cannot be removed by wet grinding and washing continue to affect EPSC's setting time. Furthermore, the addition of LP shortens the setting time, but not enough. Using a 3% dosage of AC successfully reduces the setting time to a satisfactory level and EPSC SCC with a 28-day strength greater than 40 MPa is produced using the paste rheological threshold method. [ABSTRACT FROM AUTHOR]

Self-compacting concrete (SCC), known for its excellent fluidity and self-compacting ability, is widely used in civil engineering. To enhance the comprehensive performance of SCC, dolomite powder (DP) is integrated as a substitute for cement. This study aims to analyze the impact of DP on the yield stress relationship between self-compacting mortar (SCM) and self-compacting paste (SCP) from a multi-scale perspective. A new predictive model for the yield stress relationship between SCM and SCP incorporating DP is established by improving the n value in the existing ϕ e model, which characterizes the sensitivity of the mortar yield stress relative to changes in the paste yield stress. By conducting mini-slump flow tests on nine sets of cement–DP mixtures, it is found that DP impacts the yield stress relationship between SCM and SCP mainly through changes in the inter-particle filling effect, and the n value in the predictive model is roughly between 2.4 and 3.6. When the DP content is kept constant and the particle size is changed, the n value shows a strong positive linear relationship with the packing density of the paste ( ϕ e , p ). The relationship between n and ϕ e , p is derived using the linear fitting method, which improves the model's predictive accuracy by 95.2%. [ABSTRACT FROM AUTHOR]

Herein, a carbon felt (CF) cathode modified by the acidic oxidised carbon nanotubes (OCNTs) exhibited a high yield of the H2O2 generation in electro-Fenton. Rotating disk electrode (RDE) measurements showed that the selective generation of H2O2 occurred on the CF cathode coated by OCNTs (OCNTs/CF), which was attributed to the high amount of oxygen-containing functional groups in OCNTs. Moreover, the pollutant degradation efficiency could almost reach 100% within 60 min in electro-Fenton with OCNTs/CF as the cathode. Furthermore, the pollutant removal efficiency was kept constant after five consecutive cycles, indicating the high stability of OCNTs/CF cathode. Besides, the hydrophilicity of OCNTs/CF cathode was significantly enhanced owing to the abundant oxygen-contained functional groups on the surface of the OCNTs/CF cathode, which facilitated the mass transfer between the OCNTs/CF cathode and the reactants in the bulk solution. To reveal the possible mechanism in electro-Fenton equipped with the OCNTs/CF cathode, quenching experiments and electron paramagnetic resonance (EPR) investigations were further conducted. This work provided valuable insights into the fabrication of the non-metallic cathode with a high ability towards H2O2 generation in electro-Fenton for efficient pollutant removal. [ABSTRACT FROM AUTHOR]

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults difficult in practice. In order to analyze the joint effect of the error and the compound faults in a PGT, a carrier eccentricity error model is proposed and incorporated into the TVMS model by considering the time-varying center distance, line of action (LOA), meshing angle, and contact ratio. Then, the TVMS of the cracked gear is derived based on the potential energy method. On this basis, the dynamic model of a PGT with both the carrier eccentricity error and compound gear cracks as internal excitations are established. Furthermore, the meshing characteristics and dynamic responses of the PGT are simulated to investigate the compound fault features. A series of experiments are conducted to further analyze the influence of the compound fault on the vibration response. The relevant conclusions can provide a reference for the compound fault diagnosis of a PGT in practice. [ABSTRACT FROM AUTHOR]

The identification of compound fault components of a planetary gearbox is especially important for keeping the mechanical equipment working safely. However, the recognition performance of existing deep learning-based methods is limited by insufficient compound fault samples and single label classification principles. To solve the issue, a capsule neural network with an improved feature extractor, named LTSS-BoW-CapsNet, is proposed for the intelligent recognition of compound fault components. Firstly, a feature extractor is constructed to extract fault feature vectors from raw signals, which is based on local temporal self-similarity coupled with bag-of-words models (LTSS-BoW). Then, a multi-label classifier based on a capsule network (CapsNet) is designed, in which the dynamic routing algorithm and average threshold are adopted. The effectiveness of the proposed LTSS-BoW-CapsNet method is validated by processing three compound fault diagnosis tasks. The experimental results demonstrate that our method can via decoupling effectively identify the multi-fault components of different compound fault patterns. The testing accuracy is more than 97%, which is better than the other four traditional classification models. [ABSTRACT FROM AUTHOR]

The magnesium silicate hydrate system (MgO-SiO2-H2O) possesses issues such as susceptibility to cracking, brittleness, and poor volumetric stability, which hinder its development and practical use in engineering applications. This study aimed to enhance the properties of the MgO-SiO2-H2O system by incorporating glass fiber as a reinforcing material. The mechanical properties, shrinkage properties, and properties during accelerated aging were tested at different content levels of glass fiber. Additionally, the reaction mechanism and microscopic morphology were characterized using microscopic testing methods. The results revealed that the addition of glass fiber improved the mechanical properties of the MgO-SiO2-H2O system; meanwhile, with an increase in fiber content, the mechanical properties showed an initial increase followed by a decreasing trend. With a glass fiber content of 0.6%, the system exhibited a flexural strength of 7.9 MPa at 28 d, a compressive strength of 42.5 MPa at 28 d, and a 27.2% increase in splitting tensile strength compared to the control group. At a fiber content of 0.9%, the flexural toughness steadily increased, reaching a maximum value of 2.238 N·m, which is 5.41 times greater than that of the control group. Moreover, the incorporation of glass fiber effectively inhibited the shrinkage of the MgO-SiO2-H2O system. Accelerated aging experiments confirmed that the glass fiber in the MgO-SiO2-H2O system did not undergo significant deterioration or corrosion, thereby maintaining long-term stability. These findings have important theoretical and practical significance for the application and development of the MgO-SiO2-H2O system. [ABSTRACT FROM AUTHOR]

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. Methods: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)-derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine-Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model-positive, i.e., benefited from ADT, and -negative subgroup treatment effects. Results: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. Conclusions: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.) [ABSTRACT FROM AUTHOR]