Accurate simulation and prediction of occupants’ energy use behavior are crucial in building energy consumption research. However, few studies have focused on household energy use behavior in severely cold regions that have unique energy use patterns because of the low demand of cooling in summer and the use of central heating system in winter. Thus, we developed an agent-based model to simulate the household electricity use behavior in severely cold regions, according to data for Harbin, China. The model regards apartments, residents, household appliances, and energy-management departments as agents and generates the household electricity consumption with respect to time, temperature, and energy-saving events. The simulation parameters include basic information of the residents, their energy-saving awareness, their appliance use behaviors, and the impact of energy-saving management. Electricity use patterns are described by decision-making mechanisms and probabilities obtained through a questionnaire survey. In the end, the energy-saving effects of different management strategies are evaluated. The results indicate that the model can visually present and accurately predict the dynamic energy use behavior of residents. The energy-saving potential of household electricity use in severely cold regions is mainly concentrated in lighting and standby waste, rather than cooling and heating, since the cooling demand in summer is low and the heating in winter mainly relies on central heating system of the city, not on household electricity appliances. Energy-saving promotion can significantly reduce the amount of energy waste (41.89% of lighting and 97.79% of standby energy consumption), and the best frequency of promotional events is once every four months. Residents prefer incentive policies, in which energy-saving effect is 57.7% larger than that of increasing electricity prices. This study realized the re-presentation of the changes of energy consumption in a large number of households and highlighted the particularity of household energy-saving potential in severely cold regions. The proposed model has a simple structure and high output accuracy; it can help cities in severely cold regions formulate energy-saving management policies and evaluate their effects.

The accurate estimation of the impact of urban form on CO2 emissions is essential for the proposal of effective low-carbon spatial planning strategies. However, few studies have focused on the relationship between urban form and CO2 emissions in small and medium-sized cities, and it is especially unclear whether the relationship varies across cities with different socioeconomic characteristics. This study took 132 small and medium-sized cities in the Yangtze River Delta in China to explore how urban form affects CO2 emissions, considering the socioeconomic factors of industrial structure, population density, and economic development level. First, nighttime light data (DMSP-OLS and NPP-VIIRS) and provincial energy data were used to calculate CO2 emissions. Second, four landscape metrics were used to quantify the compactness and complexity of the urban form based on Chinese urban land-use data. Finally, panel data models were established to analyze whether and how different socioeconomic factors impacted the relationship between urban form and CO2 emissions. The results showed that the three socioeconomic factors mentioned above all had obvious influences on the relationship between urban form and per capita CO2 emissions in small and medium-sized cities. The effect of compactness on per-capita CO2 emissions increased with a rise in the proportion of the tertiary industry, population density, and per-capita GDP. However, compactness shows no effects on per-capita CO2 emissions in industrial cities and low-development-level cities. The effect of complexity on per-capita CO2 emissions only increased with the rise in population density. The results may support decision-makers in small and medium-sized cities to propose accurate, comprehensive, and differentiated plans for CO2 emission control and reduction.

BACKGROUND Chronic gastritis (CG) is an inflammatory disease of the gastric mucosa. Shen-ling-bai-zhu san (SLBZS), a traditional Chinese medicine formula, is widely used for treating CG. Nevertheless, its effects are currently unclear. AIM To determine the clinical evidence and potential mechanisms of SLBZS for the treatment of CG. METHODS We systematically searched 3 English (PubMed, Embase, Medline) and 4 Chinese databases (Cochrane Library Central Register of Controlled Trials, China National Knowledge Infrastructure database, Wanfang Data Knowledge Service Platform, and the VIP information resource integration service platform) without language or publication bias restriction. Qualified studies were selected according to pre-set inclusion and exclusion criteria. RevMan 5.3 software was used for meta-analysis and literature quality assessment, Stata 14.0 software was used for sensitivity analysis, GRADE profiler 3.6 was used to evaluate the quality of evidence. And then, network pharmacology analysis was applied to primary research the mechanisms of action of SLBZS on CG. RESULTS Fourteen studies were finally included, covering 1335 participants. Meta-analysis indicated that: (1) SLBZS was superior to conventional therapies [risk ratio (RR): 1.29, 95% confidence interval (CI): 1.21 to 1.37, P < 0.00001]; (2) SLBZS was better than conventional therapies [RR: 0.24, 95% confidence interval (95%CI): 0.11 to 0.55, P = 0.0007] in terms of recurrence rate and reversal of Helicobacter pylori positivity (RR: 1.20, 95%CI: 1.11 to 1.30, P < 0.00001); and (3) The safety of SLBZS for CG remains unclear. According to the GRADE method, the quality of evidence was not high. Besides, SNZJS might treat CG by acting on related targets and pathways such as EGFR tyrosine kinase inhibitor resistance, the PI3K-Akt signaling pathway, and others. CONCLUSION SLBZS might be useful in treating CG, but long-term effects and specific clinical mechanisms of it maintain unclear. More samples and high-quality clinical experiments should be assessed and verified in the next step.

Biocatalysis is increasingly replacing traditional methods of manufacturing fine chemicals due to its green, mild, and highly selective nature, but biocatalysts, such as enzymes, are generally costly, fragile, and difficult to recycle. Immobilization provides protection for the enzyme and enables its convenient reuse, which makes immobilized enzymes promising heterogeneous biocatalysts; however, their industrial applications are limited by the low specific activity and poor stability. Herein, we report a feasible strategy utilizing the synergistic bridging of triazoles and metal ions to induce the formation of porous enzyme-assembled hydrogels with increased activity. The catalytic efficiency of the prepared enzyme-assembled hydrogels toward acetophenone reduction is 6.3 times higher than that of the free enzyme, and the reusability is confirmed by the high residual catalytic activity after 12 cycles of use. A near-atomic resolution (2.1 Å) structure of the hydrogel enzyme is successfully analyzed via cryogenic electron microscopy, which indicates a structure–property relationship for the enhanced performance. In addition, the possible mechanism of gel formation is elucidated, revealing the indispensability of triazoles and metal ions, which guides the use of two other enzymes to prepare enzyme-assembled hydrogels capable of good reusability. The described strategy can pave the way for the development of practical catalytic biomaterials and immobilized biocatalysts.