Vad Vangsgaard (actyew97)

Penicyrone A's configurational assignment underscored a requirement for revising the previously reported C-6 absolute configuration for molecules similar to verrucosidin. ) to (6 ). The 9 /9 Compounds exhibiting epimeric relationships possess identical structures except for a single stereocenter. These substances demonstrated the capacity to inhibit the growth of certain bacterial pathogens, thereby indicating their potential as lead compounds in the formulation of antimicrobial agents. The online version's supplemental material is accessible through the provided link: 101007/s42995-023-00173-2. The URL 101007/s42995-023-00173-2 points to supplementary materials that are part of the online version. The intricate complexities of uncertain environments demand both human and artificial reasoning capabilities. Probabilistic information, ideally, is at hand. Despite this, the probabilistic information could be imprecise, or entirely non-existent. Higher-order uncertainty is instrumental in our reasoning process in such cases. Within artificial intelligence, specifically concerning Dung's abstract argumentation, a key formal method for modeling defeasible reasoning is formal argumentation. In terms of cognition, the nature of reasoning has been understood to be argumentative and social, a perspective articulated by Mercier and Sperber. Reasoning with higher-order uncertainty is framed in this paper through the application of formal argumentation. In developing our approach, we build upon Haenni's probabilistic argumentation, adding several key improvements to the existing system. To integrate this, we utilize deductive argumentation, incorporating the depiction of arguments and attacks, and employing abstract argumentation semantics for the selection of arguments from a possible set of conflicting arguments. Our system's performance under the rationality postulates of formal argumentation is showcased. Secondarily, we explore a range of perspectives concerning argumentative persuasiveness, examined both abstractly and via real-world examples. This paper introduces a formal model of reasoning concerning higher-order uncertainty, having possible implications for the fields of artificial intelligence and human cognition. Analyzing the profiles of hate speech authors in a multilingual Facebook reaction data set focusing on news regarding migrants and the LGBT+ community is our aim. This collection contains English, Dutch, Slovenian, and Croatian as its languages. The process of determining whether an utterance was hateful or acceptable speech involved manual annotation for each statement. Binary logistic regression was used to examine the relationship between authors' profiles—their age, gender, and language—and the creation of hateful content, specifically focusing on comments. In all four languages, our results align with prior research: men generate more hateful comments than women, and there is a clear correlation between age and hate speech production. Our research, though consistent with prior findings, also introduces essential subtleties. Differences in age and gender dynamics, slightly distinct across various languages and cultures, suggest the presence of distinctive socio-political considerations. In the final analysis, we investigate the role that author demographics play in understanding hate speech; the characteristics of typical hate-mongers can be leveraged to identify hate speech, to cultivate sensitivity, and for counteracting the spread of (online) hatred. We conclude by examining the importance of author demographics within hate speech studies, demonstrating how the traits of typical hate speakers can inform strategies for detecting hate speech, raising awareness, and mitigating the spread of (online) animosity. Long COVID's development is hypothesized to be influenced by the neurological aftermath of SARS-CoV-2 infection, speci