Interpreting core-level spectra of oxidizing phosphorene: Theory and experiment

We combine ab initio density functional theory calculations with the equivalent cores approximation to determine core-level binding-energy shifts at phosphorus sites caused by oxidation of phosphorene. We find that presence of oxygen increases the core-level binding energies of P atoms and expect binding-energy shifts of up to 6 eV in highly defective geometries. We have identified likely binding geometries of oxygen that help to interpret the observed core-level photoemission spectra in samples at different stages of oxidation and allow us to determine the fractions of specific local geometries

Navigating Text-To-Image Customization:From LyCORIS Fine-Tuning to Model Evaluation

Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes itself as a leading open-source model in this fast-growing field. However, the intricacies of fine-tuning these models pose multiple challenges from new methodology integration to systematic evaluation. Addressing these issues, this paper introduces LyCORIS (Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion) [https://github.com/KohakuBlueleaf/LyCORIS], an open-source library that offers a wide selection of fine-tuning methodologies for Stable Diffusion. Furthermore, we present a thorough framework for the systematic assessment of varied fine-tuning techniques. This framework employs a diverse suite of metrics and delves into multiple facets of fine-tuning, including hyperparameter adjustments and the evaluation with different prompt types across various concept categories. Through this comprehensive approach, our work provides essential insights into the nuanced effects of fine-tuning parameters, bridging the gap between state-of-the-art research and practical application.Comment: 77 pages, 54 figures, 6 table

The Use of Machine Learning for the Prediction of the Uniformity of the Degree of Cure of a Composite in an Autoclave

The difference in the degree of cure of the composite in an autoclave is one of the main characterization parameters of the uniformity of the degree of cure of the composite material. Therefore, it is very important to develop an effective method for predicting the difference in the curing degree of a composite autoclave to improve the uniformity of the curing degree of the composite materials. We researched five machine learning models: a fully connected neural network (FCNN) model, a deep neural network (DNN) model, a radial basis function (RBF) neural network model, a support vector regression (SVR) model and a K-nearest neighbors (KNN) model. We regarded the heating rate, holding time and holding temperature of the composite material’s two holding-stage cure profile as input parameters and established a rapid estimation model of the maximum curing degree difference at any time during the molding process. We simulated the molding process of the composite material in an autoclave to obtain the maximum difference in the curing degree as the test sample data to train five machine learning models and compared and verified the different models after the training. The results showed that the RBF neural network model had the best prediction effect among the five models and the RBF was the most suitable algorithm for this model

Anti-Wind Experiments and Damage Prediction of Transmission Tower under Typhoon Conditions in Coastal Areas

Typhoons are a serious threat to transmission towers and lines in coastal areas. The anti-wind performance of a transmission tower needs to be reinforced and optimized to avoid tower collapse. Here, an improved real-time wind-field mathematical model and a tower-line coupled simulation model were established to reproduce the wind field distribution, mechanical vibration, and interaction between the wind field and tower. The damage prediction of the transmission tower was analyzed. Furthermore, an actual scaled tower-line model was built, which was used to measure the acceleration and displacement responses in the anti-wind experiments. The research results show that the improved model is feasible and correct based on the verification of Typhoon Mujigae. The tower’s vibration response is mainly characterized by low frequencies, whereas the lines indicate a high frequency response. The transmission line has a remarkable impact on tower vibrations in high turbulence. A flow direction angle of 50° and a long span are dangerous conditions for transmission systems in coastal regions. The acceleration and displacement responses of the main bars show opposite trends to that of the auxiliary cross-bars. The contribution of this article is the possibility of tower collapse prediction and prevention

Anti-Wind Experiments and Damage Prediction of Transmission Tower under Typhoon Conditions in Coastal Areas

Typhoons are a serious threat to transmission towers and lines in coastal areas. The anti-wind performance of a transmission tower needs to be reinforced and optimized to avoid tower collapse. Here, an improved real-time wind-field mathematical model and a tower-line coupled simulation model were established to reproduce the wind field distribution, mechanical vibration, and interaction between the wind field and tower. The damage prediction of the transmission tower was analyzed. Furthermore, an actual scaled tower-line model was built, which was used to measure the acceleration and displacement responses in the anti-wind experiments. The research results show that the improved model is feasible and correct based on the verification of Typhoon Mujigae. The tower’s vibration response is mainly characterized by low frequencies, whereas the lines indicate a high frequency response. The transmission line has a remarkable impact on tower vibrations in high turbulence. A flow direction angle of 50° and a long span are dangerous conditions for transmission systems in coastal regions. The acceleration and displacement responses of the main bars show opposite trends to that of the auxiliary cross-bars. The contribution of this article is the possibility of tower collapse prediction and prevention

Mechanical Properties of Laminates after Injection Repair

The tensile and compressive mechanical properties of laminates repaired by injection repair were experimentally studied, and the influences of repair on strength, stiffness and failure mode were evaluated. Control experiments between pristine, damaged and repaired specimens were designed, which including tensile and buckling unrestrained compression. The strain gauges were used to collect the deformation data in the test, and all the experimental data were analyzed and explained. The results demonstrate that strength of half-depth damage laminates is partially restored after repair. Strength recovery rate of tensile strength is 73.7% and that of buckling is 77.4%, and the weakest area is a filling area or the interface of panel and filler. Strength and stiffness of damaged specimens are reduced due to the coupling between extension/ compression and bending load as well as stress concentration, which are caused by the missing of material in damaged area. The stiffness of filler is smaller than that of motherboard, and the filler reduces the asymmetry of the damaged laminates and increases the stiffness of the repaired laminates