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For a higher classification accuracy of disturbance signals of power quality, a disturbance classification method for power quality based on gram angle field and multiple transfer learning is proposed in this paper. Firstly, the one-dimensional disturbance signal of power quality is transformed into a Gramian angular field (GAF) coded image by using the gram angle field, and then three ResNet networks are constructed. The disturbance signals with representative signal-to-noise ratios of 0 dB, 20 dB and 40 dB are selected as the input of the sub-model to train the three sub-models, respectively. During this period, the training weights of the sub-models are transferred in turn by using the method of multiple transfer learning. The pre-training weight of the latter model is inherited from the training weight of the previous model, and the weight processing methods of partial freezing and partial fine-tuning are adopted to ensure the optimal training effect of the model. Finally, the features of the three sub-models are fused to train the classifier with a full connection layer, and a disturbance classification model for power quality is obtained. The simulation results show that the method has higher classification accuracy and better anti-noise performance, and the proposed model has good robustness and generalization.

Abstract Background Chronic lung disease of prematurity (CLD) is the most prevalent complication of preterm birth and indicates an increased likelihood of long-term pulmonary complications. The accurate diagnosis of this condition is critical for long-term health management. Numerous definitions define CLD with different clinical parameters and radiology findings, making diagnosis of the disease ambiguous and potentially inaccurate. Methods 95 patients were identified for this study, as determined by the diagnosis or confirmation of CLD in the impression of the radiologist’s report on chest x-ray. Pulmonary function and complications were recorded at multiple benchmark timeframes within each patient’s first few months of life and used for determining eligibility under each definition. Results Each clinical definition of CLD had a high sensitivity for patients identified to have CLD by radiologists, correctly fitting over 90% of patients. Most patients included required invasive mechanical ventilation or positive pressure ventilation at 36 weeks postmenstrual age, indicating patients with radiographically confirmed CLD tended to have more severe disease. Radiologists tended to diagnose CLD before 36 weeks postmenstrual age, a timepoint used by multiple standard clinical definitions, with cases called earlier fitting under a larger percentage of definitions than those called later. Conclusions Radiologists tend to diagnose CLD in young patients with severe respiratory compromise, and can accurately diagnose the condition before developmental milestones for clinical definitions are met.

The utilization of artificial intelligence (AI) in daylight analysis has recently experienced an increase. Its capabilities have been demonstrated through different studies. While using AI for daytime simulation can reduce time, it also presents significant challenges. Whether we utilize the conventional approach of simulating daylight or employ artificial intelligence, the process of going from hand drawing to obtaining the daylight analysis result involves intricate procedures associated with modeling, programming, and integrating AI models. This paper introduces an efficient approach to rapidly predict daylight performance directly from the user's hand-drawn sketches. The core idea is to leverage a deep learning model for predicting directly from hand drawing and augmented reality (AR) to seamlessly display results in an AR environment, overlaying them on the original drawings. This method not only increases user engagement and accessibility but also eliminates the dependency on traditional daylight simulation software and workflow. Artificial neural network (ANN) was exploited using data derived from 3D building models and Useful Daylight Illuminance simulations created by Rhino3D and Honeybee. The color code method, various imaging, and data processing techniques were implemented to ensure compatibility between the hand-drawn input and the simulation model. Unity3D is used as the development platform with the AR engine Vuforia. Notably, the ANN model yields promising results, with R2 ranging from 0.872 to 0.985. This is achieved by efficiently capturing and converting grid-based hand-drawn images into suitable input for the ANN. The obtained results demonstrate the feasibility and emphasize the concept's practicality.

The water states of poly(styrene sulfonic acid) (PSSA) grafted poly(ethylene-co-tetrafluoroethylene) copolymers (ETFE-PEMs) with grafting degree (GD)=8.8-30.5% are investigated under ambient conditions by using FTIR analysis. Detailed analysis of the FT-IR spectra of ETFE-PEMs allows the observation of each component of water in the two broad bands of 1500-2000 cm-1 and 2400-3800 cm-1. These components include protonated water (H3O+…(H2O)n), H-bonded water to sulfonic acid groups ((SO3)…(H3O+)(H2O)n), H-bonded water to other water molecules ((H2O)…(H2O)), and non-H-bonded water (HOH…F). Of these water forms, ((SO3)…(H3O+)(H2O)n) is predominantly represented. Free water (non-H-bonded water) is assumed to be trapped in the holes of the ETFE backbone and is thus difficult to remove at 50oC over a 24 h period. The total peak areas of the water molecules significantly increase with GD indicating that the ETFE-PEM with higher GD possesses higher water content. Moreover, the increase of the total peak area with GD via two steps reveals an interesting relationship between the hierarchical structures and the capacity of water content under ambient conditions. The observed water content at ambient conditions can significantly affect the conductance of ETFE-PEMs under conditions of low relative humidity such as during fuel cell operation or measurement procedures of specific characterisations or testing methods.

“Rotavin-M1” is an oral live-attenuated vaccine that prevents diarrhea in children under five years old, produced from rotavirus strains G1P [1] on Vero cells. This vaccine has been studied for 16 years beginning with monitoring the local circulating strain and eventually establishing a seed lots system. The safety and immunogenicity of the vaccine were found equivalent to internationallyimported vaccines.