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In this study, a microwave hot press sintering (MHPS) strategy was developed for metal-bonded diamond composites (MBDC). The main focus was on the MHPS process of Fe60Cu40 pre-alloy bonded diamond composites. The temperature rise characteristics of the metal powders were obtained within a microwave field. Further, the composition change law of the composites was determined during the sintering process by analyzing the properties and microstructure of the composites. The successful development of this strategy will provide a reliable reference for the application of microwave sintering in the fabrication of MBDC.

Academic writing poses a consistent challenge in many students' university career. This study conducted a systematic diagnostic assessment to identify specific discourse strengths and weaknesses in first-year undergraduate students' English writing. Our study found that the top three prevalent weaknesses are related to overall discourse awareness, syntactic proficiency, and idea development. Based on the knowledge of students' specific strengths and weaknesses, more targeted remediation prescription can be designed and delivered for maximal support in facilitating first year undergraduate students' development of English writing proficiency in the classroom setting.

In this study, the mud-phosphorus slurry was used to simultaneously remove SO2 and NOx. The technology proposed new avenues for the purification and utilization of remove SO2 and NOx in flue gas. The effects of reaction temperature, solid-liquid ratio, and oxygen content on the efficiency of desulfurization and denitrification were studied experimentally. Results show that the parameters were solid-liquid ratio of 5.0 g/40 mL, T = 60 °C, φ (O2) = 20%, Q = 300 mL/min under the best experimental conditions. The maximum amount of ozone generated was 563.8 mg/m3. The reaction time with desulfurization rate ≥ 99% was 340 min; the reaction time with denitrification rate ≥ 99% was 160 min. Response surface analysis method was used to perform a three-factor three-level response surface experiment. Results show that the oxygen content had a highly significant effect on the desulfurization and denitrification efficiency, and the relationship between the desulfurization and denitrification efficiency was oxygen content > mud-phosphorus slurry liquid-solid ratio > reaction temperature. The process is simple, the solid waste is used to treat the flue gas, and the removal effect is good, which is convenient for popularization.

The dielectric properties of lignite were studied by using the resonant cavity perturbation method. The effect of temperature, particle size, moisture content, and microwave frequency were ...

Self-supervised learning approach like contrastive learning is attached great attention in natural language processing. It uses pairs of training data augmentations to build a classification task for an encoder with well representation ability. However, the construction of learning pairs over contrastive learning is much harder in NLP tasks. Previous works generate word-level changes to form pairs, but small transforms may cause notable changes on the meaning of sentences as the discrete and sparse nature of natural language. In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs. Using contrastive learning improves the generalization ability of adversarial training because contrastive loss can uniform the sample distribution. And at the same time, adversarial training also enhances the robustness of contrastive learning. Two novel frameworks, supervised contrastive adversarial learning (SCAL) and unsupervised SCAL (USCAL), are proposed, which yields learning pairs by utilizing the adversarial training for contrastive learning. The label-based loss of supervised tasks is exploited to generate adversarial examples while unsupervised tasks bring contrastive loss. To validate the effectiveness of the proposed framework, we employ it to Transformer-based models for natural language understanding, sentence semantic textual similarity and adversarial learning tasks. Experimental results on GLUE benchmark tasks show that our fine-tuned supervised method outperforms BERT$_{base}$ over 1.75\%. We also evaluate our unsupervised method on semantic textual similarity (STS) tasks, and our method gets 77.29\% with BERT$_{base}$. The robustness of our approach conducts state-of-the-art results under multiple adversarial datasets on NLI tasks.

A green and-easy to operate method, the microwave technology, was developed to promote the desulfurization process of phosphate rock, systematically investigates the strengthening effect of microwave, and uses XRD, BET, SEM, XRF, ICP, and EDS to characterize the reactants. The results show that the main reason for the desulfurization efficiency is improved by microwave heating under microwave conditions, different thermal stress phosphate rock materials lead to the destruction of each microstructure, and a specific surface area increased 40.25% phosphate rock. In addition, after microwave irradiation, the pore size of the phosphate rock at 2-5 nm is significantly increased, and the number of mesopores is significantly increased, thereby increasing the desulfurization efficiency of the phosphate rock. By investigating the effects of temperature, oxygen content, flow rate, and solid-liquid ratio on desulfurization efficiency, the paper concludes that the optimal conditions for desulfurization of phosphate rock after microwave irradiation are C(SO2) is 2500 mg·m-3, temperature is 40 °C, φ(O2) is 5%, solid-liquid ratio is 3.5 g:200 ml, and flue gas flow is 500 ml·min-1.

In this paper, novel WS2/Bi2MoO6 heterostructured photocatalysts were successfully fabricated via a facile solvothermal growth method using pre-exfoliated layered WS2 nanoslices as a substrate. The structure, morphology, and optical properties of the as-prepared WS2/Bi2MoO6 samples were characterized by XRD, XPS, SEM, TEM (HRTEM), and UV-vis diffuse reflectance spectra (DRS). Results confirmed the existence of an excellent nanojunction interface between layered WS2 nanoslices and Bi2MoO6 nanoflakes. Under visible light (>420 nm), the WS2/Bi2MoO6 composites exhibit significantly enhanced photocatalytic activity compared with pure Bi2MoO6 toward the decomposition of rhodamine B (RhB). Meanwhile, the active species trapping experiments indicated that holes (h+) were the main active species during the photocatalytic reaction. The enhanced photocatalytic performance can be ascribed to the effective light harvesting, fast photogenerated electron–hole pairs separation, and excellent charge carrier transport of the WS2/Bi2MoO6 heterostructures. Moreover, the prepared WS2/Bi2MoO6 composites also show good structural and activity stability in repeatability experiments.

We investigate the parameter estimation of adaptive FIR filter with noisy input. When the filter input is corrupted by additive noise, the parameter estimation of traditional LMS algorithm is biased. For the noise is unknown, we propose an input noise variance estimation method under unconstrained condition based on the LMS algorithm and combine the bias-compensated LMS (BCLMS) algorithm by removing the noise-induced bias. Simulation results show that our proposed algorithm has better estimate accuracy than the bias-compensated LMS algorithm with the input noise variance estimation under constrained condition, and provides an unbiased estimate of the filter parameters under any different noise levels.

A novel alarming assessment model of coalmine gas is presented in this paper. Based on the information fusion technique of multi-sensor, we introduce the thought of variable fuzzy set to assess complicated and risk coalmine gas. Through information fusion we can get more effective and reliable data about coalmine gas. The method for variable fuzzy set theory can reasonable identify the relative membership degree between the sample indexes and standard interval of each level index, and reasonably determine the evaluation of each sample by varying the model and its parameters. The numerical examples show that the proposed method is feasible and effective, and the evaluation results are reasonable.