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The market for urban distributed photovoltaics (DPV) is expected to take off in the next decade. However, these systems are often subject to complex urban contexts and sub-optimal conditions, requiring scalable and comprehensive solutions to detect their underperformances. In recent years, deep generative models (DGMs) have exhibited outstanding performance in the anomaly detection domain, dealing with generic high-dimensional time series data. Nevertheless, the existing applications of DGMs in the photovoltaic (PV) sector are still unable to account for environmental information, limiting their performance under various environmental conditions. This study proposes the Sequential Conditional Variational Autoencoder (SCVAE), which can cope with the sequential impacts of the environment on PV power generation. Using real-world data collected from 30 rooftop PV sites located across China, a data processing pipeline is developed to construct the training datasets which contain mostly normal samples for unsupervised SCVAE model training. This work also constructs a synthetic dataset with a wide variety of artificial anomalies in reference to the domain insights and engineering practice of DPV systems. After checking and refining by experts, the synthetic dataset can finally be used to validate the anomaly detection models. The results demonstrate that the SCVAE model outperforms existing state-of-the-art unsupervised anomaly detection models and can be effectively generalized to unseen PV sites. Moreover, the latent variables of SCVAE could be used to identify the type of DPV failure, thereby enabling more targeted diagnostics of anomaly mechanisms. • An anomaly detection method is proposed for DPV in various environmental scenarios. • A novel unsupervised model is proposed for time series anomaly detection. • A real-world data processing pipeline and a labeled synthetic dataset are proposed. [ABSTRACT FROM AUTHOR]

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Chronic obstructive pulmonary disease (COPD) is a significant disease impacting health and quality of life. Yunnan Province, a major tobacco producer, lacks comprehensive COPD studies. The purpose of this study is to describe the epidemic situation of COPD in Yunnan province and explore its influencing factors., Methods: This study is a cross-sectional research conducted in a representative sample of adults aged 20 and older from 13 prefectures and cities in Yunnan Province, China. COPD was diagnosed using post-bronchodilator pulmonary function tests. Demographics were analyzed with descriptive statistics. The influencing factors of COPD were examined by using the multivariate logistic regression models., Results: Our study found that high-risk individuals for COPD accounted for 20.30% of the screened population aged 20 and above, with a COPD prevalence of 27.18% among this high-risk group. Male had a higher prevalence (33.01%) than did female (16.35%; p <0.001 for sex difference). Additionally, the proportion of severe and extremely severe COPD cases in Yunnan Province was higher than the national average and other provinces. After considering the potential confounding variables, male (OR=2.291, 95% CI: 1.584-3.313), age (OR=1.501, 95% CI: 1.338-1.685), underweight (OR=1.747, 95% CI: 1.225-2.491), previous smoking (OR=1.712, 95% CI: 1.182-2.478), passive smoking (OR=1.444, 95% CI: 1.159-1.800), and a history of respiratory system diseases in childhood (OR=2.010, 95% CI: 1.346-3.001) were significantly associated with an increased risk of COPD. Conversely, being overweight (OR=0.636, 95% CI: 0.489-0.828), and residing in high-altitude counties (OR=0.445, 95% CI: 0.263-0.754) were negatively correlated with the risk of COPD., Conclusion: There is significant prevalence of COPD (27.18%) among high-risk population aged 20 and above in Yunnan Province, China. Apart from male, smoking, BMI and other known risk factors for COPD. We found that high-altitude residence had a lower prevalence of COPD. There is no significant difference in COPD prevalence between Han and ethnic minority populations., Competing Interests: The authors declare that they have no conflicts of interest in this work., (© 2024 Wen et al.)