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Urban energy issues have attracted global attentions due to the key role in controlling climate change. As one of the world's largest metropolises, Beijing has been focusing on improving urban energy performances to achieve green transition and sustainable development during the past decades, for which purpose the measurement and decomposition of energy performance is of vital importance. In this paper, exergy is introduced as one of the indicators under the frame of index decomposition analysis. An hierarchical framework is constructed to reveal the contributions from different level of urban energy system to overall energy performance. Beijing's energy and exergy consumption and intensity are then analyzed and compared from the city and sector level during 2008–2017. Results suggest that Beijing's city and sector-level energy performance has been improving during the last 10 years and technical improvements contribute significantly from both energy and exergy perspectives. Meanwhile, changes from Beijing's industrial structure and energy supply structure display neutral contributions, which probably indicate the underestimation of structural contribution is caused under the IDA framework. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to investigate the changes of epidemic characteristics of influenza activity pre– and post–coronavirus disease 2019 (COVID-19) in Beijing, China. Methods Epidemiologic data were collected from the influenza surveillance system in Beijing. We compared epidemic intensity, epidemic onset and duration, and influenza transmissibility during the 2022–2023 season with pre-COVID-19 seasons from 2014 to 2020. Results The overall incidence rate of influenza in the 2022–2023 season was significantly higher than that of the pre-COVID-19 period, with the record-high level of epidemic intensity in Beijing. The onset and duration of the influenza epidemic period in 2022–2023 season was notably later and shorter than that of the 2014–2020 seasons. Maximum daily instantaneous reproduction number (Rt) of the 2022–2023 season (Rt = 2.31) was much higher than that of the pre-COVID-19 period (Rt = 1.49). The incidence of influenza A(H1N1) and A(H3N2) were the highest among children aged 0–4 years and 5–14 years, respectively, in the 2022–2023 season. Conclusions A late, intense, and short-term peak influenza activity was observed in the 2022–2023 season in Beijing. Children <15 years old were impacted the most by the interruption of influenza circulation during the COVID-19 pandemic. Maintaining continuous surveillance and developing targeted public health strategies of influenza is necessary. [ABSTRACT FROM AUTHOR]

This study explores the correlation between coping style, quality of life, and illness uncertainty in the family caregivers of patients with liver cancer. Methods: Employing convenience sampling, 210 family caregivers of patients with liver cancer who met the admission criteria were selected from a grade A infectious disease hospital in Beijing between January and December 2022. A cross-sectional survey was conducted using the Simplified Coping Style Questionnaire, Caregiver Quality of Life, and the Mishel Uncertainty in Illness Scale for Family Members. This study analysed the correlations between coping styles, quality of life, and illness uncertainty in these caregivers. Results: The study found that family caregivers of patients with liver cancer had average scores for illness uncertainty (83.44 ± 11.86), coping style (33.19 ± 9.79; both positive [23.02 ± 6.81] and negative [10.17 ± 5.05]), and quality of life (169.53 ± 32.46). A negative association was observed between illness uncertainty in these caregivers and positive coping style (r = − 0.207, p = 0.003), physical status (r = − 0.182, p = 0.008), psychological status (r = − 0.200, p = 0.004), and social adaptation (r = − 0.229, p = 0.001). Conclusion: The study concludes that illness uncertainty in family caregivers of patients with liver cancer is at a moderate level. Furthermore, there is a notable correlation between illness uncertainty, coping style, and quality of life in these caregivers. [ABSTRACT FROM AUTHOR]

Aim: To develop and internally validate a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas (PPGLs). Methods: Clinical data of patients with PPGLs who presented to Peking Union Medical College Hospital from 2013 to 2022 and underwent genetic testing were retrospectively collected. Variables were screened by backward stepwise and clinical significance and were used to construct multivariable logistic models in 50 newly generated datasets after the multiple imputation. Bootstrapping was used for internal validation. A corresponding nomogram was generated based on the model. Sensitivity analyses were also performed. Results: A total of 556 patients with PPGLs were included, of which 99 had a germline SDHB mutation. The prediction model revealed that younger age of onset [Odds ratio (OR): 0.93, 95% CI: 0.91-0.95], synchronous metastasis (OR: 6.43, 95% CI: 2.62-15.80), multiple lesion (OR: 0.22, 95% CI: 0.09-0.54), retroperitoneal origin (OR: 5.72, 95% CI: 3.13-10.47), negative 131I-metaiodobenzylguanidine (MIBG) (OR: 0.34, 95% CI: 0.15-0.73), positive octreotide scintigraphy (OR: 3.24, 95% CI: 1.25-8.43), elevated 24h urinary dopamine (DA) (OR: 1.72, 95% CI: 0.93-3.17), NE secretory type (OR: 2.83, 95% CI: 1.22- 6.59), normal secretory function (OR: 3.04, 95% CI: 1.04-8.85) and larger tumor size (OR: 1.09, 95% CI: 0.99-1.20) were predictors of SDHB mutations in PPGLs, and showed good and stable predictive performance with a mean area under the ROC curve (AUC) of 0.865 and coefficient of variation of 2.2%. Conclusions: This study provided a novel and useful tool for predicting SDHB mutations by integrating easily obtained clinical data. It may help clinicians select suitable genetic testing methods and make appropriate clinical decisions for these high-risk patients. [ABSTRACT FROM AUTHOR]

In disaster rescue, multiple resource allocation strategies before and after disasters are a key research issue. Response planning for sudden-onset disasters must take into account the inherent uncertainties associated with the catastrophe and potential ensuing disasters. This article investigates the problem of resource allocation optimization in the context of natural disasters accompanied by the occurrence of multiple secondary hazards. A two-stage stochastic optimization model is proposed to simulate the scenario of random occurrence and severity of multiple natural hazards to optimize the resource allocation of rescue teams, warehousing items and medical resources when multiple natural hazards occur in combination. A particle swarm optimization (PSO) algorithm is designed and compared with a commercial solver (CPLEX) and lower bound at various scales. The results show that PSO performs optimally for large-scale cases. A case study, undertaken in Beijing, demonstrates that this method can improve the estimated casualty rescue success rate to 90.6%. [ABSTRACT FROM AUTHOR]