Determinants of users' unverified information sharing on social media platforms: A herding behavior perspective.

The proliferation of unverified or false information by irresponsible users can significantly amplify the spread of misinformation or fake news. Despite growing research on unverified information sharing, a comprehensive understanding of the varying influences of different factors and strategies to mitigate this issue remains under investigation. To address this research gap, this study, rooted in the theory of herd behavior, develops, and tests a model theorizing the reasons behind social media users' unverified information sharing. Data was collected from 510 respondents across six regions of China using a convenience sampling method. The collected data was analyzed using Mplus. The results from this study indicated that perceived severity, state uncertainty, and herding have a significant positive influence on unverified information sharing. These results enrich the understanding of unverified information-sharing behavior among Chinese social media users. Drawing from these results, we suggest platform administrators and policymakers mitigate herd behavior tendencies and stem the spread of misinformation by disseminating timely, accurate, and authoritative information. Since this action will reduce users' perceptions of severity and uncertainty. Social media users are also advised to stay vigilant over the implications of herd behavior and foster a more critical attitude towards information sharing.

Synergistic effects of peracetic acid and free ammonia pretreatment on anaerobic fermentation of waste activated sludge to promote short-chain fatty acid production for polyhydroxyalkanoate biosynthesis: Mechanisms and optimization.

Peracetic acid (PAA) combined with free ammonia (FA) pretreatment can be utilized to promote anaerobic fermentation (AF) of waste activated sludge (WAS) to produce short-chain fatty acids (SCFAs), and the resulting SCFAs are desirable carbon sources (C-sources) for polyhydroxyalkanoate (PHA) biosynthesis. This work aimed to determine the optimum conditions for PAA + FA pretreatment of sludge AF and the feasibility of using anaerobic fermentation liquor (AFL) for PHA production. To reveal the mechanisms of integrated pretreatment, the impacts of PAA + FA pretreatment on different stages of sludge AF and changes in the microbial community structure were explored. The experimental results showed that the maximum SCFA yield reached 491.35 ± 6.02 mg COD/g VSS on day 5 after pretreatment with 0.1 g PAA/g VSS +70 mg FA/L, which was significantly greater than that resulting from PAA or FA pretreatment alone. The mechanism analysis showed that PAA + FA pretreatment promoted sludge solubilization but strongly inhibited methanogenesis. According to the analysis of the microbial community, PAA + FA pretreatment changed the microbial community structure and promoted the enrichment of bacteria related to hydrolysis and acidification, and Proteiniclasticum, Macellibacteroides and Petrimonas became the dominant hydrolytic and acidifying bacteria. Finally, after alkali treatment, the AFL was utilized for batch-mode PHA production, and a maximum PHA yield of 55.05 wt% was achieved after five operation periods.

Users' unverified information-sharing behavior on social media: The role of reasoned and social reactive pathways.

Unverified or false information spread by irresponsible users can amplify the dissemination of fake news or misinformation. This phenomenon may not only undermine the credibility of social media platforms but also pose severe consequences for individuals and society. This study applies and extends the prototypical willingness model with the aim of comprehending the reasons, and decision-making process driving users' unverified information-sharing behavior a reasoned and intended pathway or an impulsive and unconscious one. Data from a sample of 646 users were analyzed using Structural Equation Modeling to assess the determinative effect of both the reasoned pathway (attitude toward unverified information-sharing, subjective norm, and perceived behavioral control) and the social-reaction pathway (prototype favorability and similarity). Findings highlight the substantial role of the social-reaction pathway in forecasting users' unverified information-sharing behavior, with prototype similarity and attitude being the dominant predictors. Meanwhile, components of the reasoned pathway, specifically perceived behavior control, and attitude, also exhibited significant contributions toward predicting the behavior. In summary, while a deliberate, reasoned process has some influence, the sharing of unverified information on social media by users is primarily an intuitive, spontaneous response to specific online circumstances. This study therefore offers valuable insights that could aid relevant stakeholders in effectively regulating the spread of misinformation. Against this backdrop, highlighting potential risks associated with sharing unverified information and the negative portrayal of users propagating misinformation may contribute to the development of a more critical perspective toward online information sharing by users themselves.

A new perspective on anthropogenic nitrogen loss mitigation strategies: Integrated control via sustainable regional integration.

Unregulated regional integrated development disrupts the reactive nitrogen (Nr) cycle, adding complexity to anthropogenic Nr environmental losses. The objective of this study was to establish a framework for mitigating anthropogenic Nr loss through a new regional integration perspective by analyzing anthropogenic Nr loss and integrated control strategies in the Yangtze River Delta (YRD) region from 2011 to 2020. The results revealed that the total Nr loss in the YRD ranged from 1780.7 to 1972.0 Gg N yr-1. Re-linking cropland and livestock is crucial for reducing Nr loss, as they act as the main sources of Nr loss. Spatial analysis at the regional scale revealed that regional integration has led to a dispersion of Nr loss, while uneven development among cities has resulted in a westward shift of 8.6 km in the Nr loss centroid, suggesting the need for the implementation of collaborative governance and integrated environmental regulation in the YRD. At the city scale, 27 cities were clustered into six types based on the similarity of Nr loss structural characteristics, allowing for the development of targeted reduction policies based on the specific Nr structural characteristics of each city. The results of driver and mitigation potential analysis indicated the feasibility of achieving the shared goal of sustainable regional integration and the application of optimal mitigation strategies in different cities and the YRD. Overall, the new-perspective framework established in this study provides valuable references for sustainable Nr management in the context of regional integration.

Micropollutant rejection by nanofiltration membranes: A mini review dedicated to the critical factors and modelling prediction.

Nanofiltration (NF) membranes, extensively used in advanced wastewater treatment, have broad application prospects for the removal of emerging trace organic micropollutants (MPs). The treatment performance is affected by several factors, such as the properties of NF membranes, characteristics of target MPs, and operating conditions of the NF system concerning MP rejection. However, quantitative studies on different contributors in this context are limited. To fill the knowledge gap, this study aims to assess critical impact factors controlling MP rejection and develop a feasible model for MP removal prediction. The mini-review firstly summarized membrane pore size, membrane zeta potential, and the normalized molecular size (λ = rs/rp), showeing better individual relationships with MP rejection by NF membranes. The Lindeman-Merenda-Gold model was used to quantitatively assess the relative importance of all summarized impact factors. The results showed that membrane pore size and operating pressure were the high impact factors with the highest relative contribution rates to MP rejection of 32.11% and 25.57%, respectively. Moderate impact factors included membrane zeta potential, solution pH, and molecular radius with relative contribution rates of 10.15%, 8.17%, and 7.83%, respectively. The remaining low impact factors, including MP charge, molecular weight, logKow, pKa and crossflow rate, comprised all the remaining contribution rates of 16.19% through the model calculation. Furthermore, based on the results and data availabilities from references, the machine learning-based random forest regression model was trained with a relatively low root mean squared error and mean absolute error of 12.22% and 6.92%, respectively. The developed model was then successfully applied to predict MPs' rejections by NF membranes. These findings provide valuable insights that can be applied in the future to optimize NF membrane designs, operation, and prediction in terms of removing micropollutants.