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Aflatoxin B1 (AFB1), commonly found in agriculture products, has been considered as a carcinogen. Thus, to develop analytical methods that can be used to rapidly screen the presence of AFB1 in complex samples is important. Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) uses inorganic materials as assisting materials to facilitate desorption/ionization of analytes. The feasibility of using GO as the affinity probe against AFB1 and as the assisting material in SALDI-MS analysis was first demonstrated. We also explored a facile method to impose magnetism on GO to generate magnetic GO (MGO) nanoprobes by simply incubating GO in aqueous FeCl3 under microwave heating. The generated MGO nanoprobes possessed magnetism and were capable of enriching trace AFB1 from complex samples. AFB1 enrichment took only 6 min by incubating MGO with samples under microwave heating (power = 90 W). Followed by magnetic isolation, the isolated conjugates were ready for SALDI-MS analysis. The enrichment steps including trapping and isolation can be completed within ∼10 min. The lowest detectable concentration of our method toward AFB1 was ∼1 nM. Results also showed that AFB1 can be selectively detected from complex samples, including cell lysates of fungal spores, AFB1-spiked peanut, and wheat samples, by using the developed method. The selectivity of our method against AFB1 from the samples containing other toxins including aflatoxin G1 and ochratoxin A was also examined. According to these results, we believe that the developed method should have the potential to be used for rapid screening of AFB1 from real-world samples.

Public rejection of recycled water hinders the application of recycled water use projects in green communities. An effective information outreach strategy could help to overcome this obstacle. This study used message frames and reference points as control variables to design experimental materials and conduct eye-movement experiments to determine the effect of different information promotion strategies. The results of the study show that: (1) compared with the loss frame, the gain-framed messages are more effective; (2) self-referencing messages are more suitable for recycled water use promotion than other-referencing messages; (3) message frame (gain vs. loss) and reference point (self vs. others) have an interactive effect on the public’s information cognitive behavior; (4) the average duration of fixations for advertising message plays an intermediary role in the path of message frame and reference point jointly influencing the public acceptance. This study provides managerial implications for determining information dissemination strategies for applying recycled water-use projects in green communities.

This paper mainly introduces the basic dynamics model of evolutionary game theory: asymmetric replicator dynamic model and asymmetric replicator dynamic model and its relative conclusions. In order to facilitate understanding, a few simple examples are cited in the text to illustrate the differences between them. On the basis of the above, it also introduces theorists for random dynamic researches and theoretical achievement. Finally, this paper compares the classic game theory and evolutionary game theory in dynamic conceptual difference.

Many probabilistic scheduling models have been developed to determine the duration of construction projects. However, these models are not appropriate to capture the effect of the factors that are involved construction interface problems on the durations of multiple activities. This work presents a simulation-based probabilistic scheduling model that includes the impact of construction interface problems on the duration of building projects. The proposed model is established according to an activity duration model and a work-group (WG)-based schedule network. The activity duration model is applied to reflect the effects of general factors on the duration of each activity; the WG-based schedule network is employed to evaluate the effects of construction interface problems on the durations of multiple activities. The results of applying the proposed model to an example project reveal that the duration of a project can be over-optimistically estimated if the effects of construction interface problems are neglected.

As an important research method in the field of modern machine learning engineering technology, BP neural network has gradually developed into the most widely used and now the most widely used in the industry with its powerful non-linear feature function mapping computing capabilities, good data induction and feature recognition computing capabilities. This paper closely links the actual activities of project engineering technology management with modern machine learning network technology, and proposes a new type of engineering management activity model based on machine-integrated GABP neural network. This article briefly introduces the valuation theory based on basic knowledge points such as artificial intelligence technology, neural network theory, and genetic algorithm. It does a detailed analysis and research on the valuation models of commonly used neural networks in China, and points out the defects and defects of the neural network structure itself. Good generalization ability, in order to minimize the impact of subjective factors on the valuation results. Although there are many management software specializing in construction project engineering currently on the market, the service scope and demand range of these management software specializing in construction project engineering are relatively narrow. For example, construction project management software can only be used Manage a construction project. In the actual operation process, it is also found that unreasonable time arrangements often occur in various links such as personnel scheduling and material distribution of engineering projects, resulting in a waste of manpower and resources. In response to the urgent requirements of government departments for efficient management of a large number of engineering project resources, the paper develops a universal project engineering management system based on GABP neural network and artificial intelligence technology.

Building sufficient recycled water infrastructure is an effective way to solve problems related to water shortages and environmental degradation, and is of great strategic significance for saving resources, protecting the ecological environment, and promoting sustainable social and economic development. Although recycled water is environmentally friendly, the public is still skeptical about its use, which has led to the failure of a large number of recycled water infrastructure investments; therefore, increasing the public’s willingness to re-use is critical for the construction of recycled water infrastructure. To identify the influence mechanism of user comments on public re-use behaviors, we conducted an eye-tracking experiment in China. The results demonstrated that (1) perceived usefulness, perceived quality, and perceived risk have significant impacts on the public’s willingness to buy; (2) user reviews can enhance the public’s perceived usefulness of recycled products and increase their willingness to buy; and (3) in the process of consumption, the public tends to pay attention to negative reviews, where user reviews alter the perceived risks and perceived prices of recycled products, thereby affecting the willingness to buy of consumers. This study provides a scientific reference for the construction of recycled water infrastructure and the further promotion of recycled water.