Medical misinformation propagates rapidly across digital platforms, posing substantial risks to public health decision-making. Traditional content-based detection approaches exhibit limited effectiveness when confronting the nuanced language and domain-specific terminology inherent in health-related claims. While foundation language models demonstrate remarkable semantic understanding capabilities, their direct application to medical misinformation detection encounters two fundamental obstacles: hallucination-induced evidence unreliability and insufficient explainability in high-stakes health contexts. Rather than treating foundation models as monolithic end-to-end predictors, we reconceptualize them as modular evidence generators and verifiers within a collaborative reasoning architecture. We propose Medical misinformation GUard via multi-model Adversarial Reasoning and Dimensional evidence (MedGUARD), a two-stage framework that leverages foundation model collaboration for explainable detection. The evidence generation stage employs adaptive in-context learning and multi-model adversarial negotiation to synthesize reliable multidimensional evidence, effectively mitigating hallucination artifacts. The semantic fusion stage integrates claim representations with evidence-augmented features through multimodal factorized bilinear pooling. Experiments across four medical misinformation benchmarks demonstrate that MedGUARD achieves improvements of 3.2 to 4.5 F1 points over 25 baseline methods, with all advantages confirmed by McNemar’s significance test. Adversarial negotiation reduces hallucination rates by 66% to 70% while generating transparent reasoning trails across seven evidential dimensions. Computational efficiency analysis quantifies MedGUARD’s cost-performance trade-off against all baselines, and cross-domain generalization experiments confirm that its accuracy advantage holds when training and test distributions differ, enabling medical professionals to verify automated assessments before clinical deployment.
Many U.S. states have adopted the goal of achieving carbon neutrality in the coming decades. Pathways for achieving these targets would likely yield a reduction of air pollutant emissions as well. We assess several state-level decarbonization pathways for Massachusetts, including electrification of buildings and transportation and decarbonization of the electric grid, estimating their impacts on in- and out-of-state emissions of carbon dioxide (CO2) and several air pollutants. The analysis applies the GCAM-USA integrated assessment model with state-level resolution. We consider interactions between existing and proposed state and regional policies. The most aggressive state decarbonization pathway that was examined would achieve only a 65% reduction in CO2 emissions by 2050 relative to 2015, indicating the need for further decarbonization options within the industry and non-road transportation sectors to achieve net-zero emission goal stated by the current administration. The air pollution co-benefits under these decarbonization pathways are substantial, especially for primary fine particulate matter. While policies focusing only on Massachusetts are shown to deliver in-state reductions of CO2 and air pollutant emissions, these reductions were partially countered by emissions increases in other states, indicating the need for coordinated regional planning to prevent emissions leakage.
When different individuals view the same image, their eye movements are often similar. The degree of this similarity is called interobserver congruency (IOC). We investigated how IOC is determined by three factors that impact gaze control: the visual features of an image, its semantic content, and the task observers perform when viewing it. We first quantified the low-level physical features and semantic complexity of images using a well-established measure of visual clutter and a novel measure based on a distributional semantic model, respectively. Then, we selected an image set in which these two measures were uncorrelated and used it in an eye-tracking experiment, in which observers either freely viewed the images or memorised their content. We assessed IOC using four metrics: two capturing purely spatial IOC and two sensitive to spatiotemporal IOC, i.e., considering both the locations and order of fixations. The values of the two spatial metrics decreased with increasing clutter and remained insensitive to the remaining factors we investigated. The two spatiotemporal metrics were sensitive to both clutter and semantic complexity, but, interestingly, in different ways. Specifically, according to a metric that was insensitive to the presence of objects in images, IOC decreased with clutter and increased with semantic complexity, while the metric sensitive to objects showed the opposite pattern. Additionally, we found that the memorization task rendered oculomotor behaviour more exploratory, which had modulatory effects on spatiotemporal – but not spatial – IOC. Together, our findings reveal how factors known to shape gaze behaviour determine IOC.
Abstract Sustainable development of resource-based cities has emerged as a core agenda in contemporary urban construction. Existing studies usually investigated sustainable development based on objective statistical data. Since many supporting policies are introduced in the transformation process of resource-based cities, it is also necessary to consider textual data. To address this gap, this study aims to develop a comprehensive assessment framework to analyze the sustainable development pathways of 16 resource-based cities in northeastern China by integrating both textual and statistical data. A combined subjective–objective weighting method is proposed, with subjective weights reflecting governmental priorities, while objective weights measuring the development difference. The results show that most cities had positive development trends. However, over half of the cities experienced negative growth in environmental sustainability, highlighting persistent challenges in ecological transformation. Additionally, all the cities showed marked variations in economic and environmental sustainability, whereas they had relative homogeneity in social sustainability. Based on the findings, specific recommendations are provided to help local governments optimize their sustainable development strategies. The research provides valuable references for the sustainable development of resource-based cities in northeastern China, and future studies will focus on the improvement of the indicator system and the capturing of uncertainty and diversity inherent in the policies.
This work is devoted to the study of traveling semifronts for an SIS epidemic model in a patchy environment. In the SIS epidemic model, infective individuals become susceptible again after recovery, thereby forming a cyclic structure from the infected class back to the susceptible class. The existence of traveling semifronts of the system is established via Schauder's fixed point theorem with the aid of a pair of upper and lower solutions distinct from those in SI-type epidemic models. We further verify the persistence of these traveling semifronts by virtue of the boundedness of the second component, a core prerequisite for establishing the existence of critical-speed traveling semifronts. To address the obstacles posed by the cyclic structure and linear growth of the second component, we employ Harnack-type properties for its corresponding traveling wave equation, together with a series of refined analytical arguments, to establish the boundedness of the traveling semifronts. Moreover, traveling waves connecting the disease-free equilibrium to the endemic equilibrium of the system with a specific demographic structure for the susceptible group and a bilinear incidence function are achieved by constructing a suitable Lyapunov function. Finally, the convergence of persistent positive traveling semifronts at +infinity for the original system is illustrated by numerical simulations.
