Sorption-based atmospheric water harvesting (SAWH) followed by solar-driven desorption is emerging as a promising energy and cost-effective solution to alleviate the worldwide freshwater scarcity. To achieve efficient atmospheric water harvesting, a SAWH system with low water transfer resistance and high sorption-desorption kinetics that integrates photothermal properties is demanded. Herein, inspired by nature, a sodium alginate (SA)-based SAWH hemisphere with spatially centripetal conical channels loaded with CaCl ₂ crystals is designed. The device demonstrates unidirectional water transfer properties and high moisture absorption capacity. To enable solar-driven water desorption, the device is engineered with a photothermal layer by chelation of tannic acid (TA) with Fe ³⁺. The multifunctional SAWH device with a special channel structure presents a superb water absorption of 0.90-2.29 g g ⁻¹ within a wide range of relative humidity (RH) (40-90%) and a fast solar-driven water desorption rate of 1.77 kg m ⁻² h ⁻¹ under one sun illumination. In outdoor tests, 82.3% of the water absorbed overnight could be released during the daytime under natural sunlight, achieving an ultrahigh daily water production of 3.72 L per m ² that is superior to that of most previously reported all-in-one SAWH systems. This proposed design strategy provides an effective solution for collecting water from the air by SAWH followed by solar-driven water desorption.

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Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.@en

Targeting the challenges in the risk analysis of laboratory fire accidents, particularly considering fire accidents in Chinese universities, an integrated approach is proposed with the combination of association rule learning, a Bayesian network (BN), and fuzzy set theory in this study. The proposed approach has the main advantages of deriving conditional probabilities of BN nodes based on historical accident data and association rules (ARs) and making good use of expert elicitation by using an augmented fuzzy set method. In the proposed approach, prior probabilities of the cause nodes are determined based on expert elicitation with the help of an augmented fuzzy set method. The augmented fuzzy set method enables the effective aggregation of expert opinions and helps to reduce subjective bias in expert elicitations. Additionally, an AR algorithm is applied to determine the probabilistic dependency between the BN nodes based on the historical accident data of Chinese universities and further derive conditional probability tables. Finally, the developed fuzzy Bayesian network (FBN) model was employed to identify critical causal factors with respect to laboratory fire accidents in Chinese universities. The obtained results show that H4 (bad safety awareness), O1 (improper storage of hazardous chemicals), E1 (environment with hazardous materials), and M4 (inadequate safety checks) are the four most critical factors inducing laboratory fire accidents.@en