Hong Jiang

The ground of human dignity constitutes a core problem in metaethics. Traditional conceptions of dignity—whether religious, rational, recognition-based, or capability-based—all depend on moral attribute presuppositions, and each exhibits explanatory fractures in marginal cases. This paper proposes an entirely new theory of dignity grounded in a foundational postulate of ethics and objective facts: Pre-structural dignity. The argument proceeds from a disciplinary postulate and indisputable objective facts—that the advancement of civilization (generating increments rather than maintaining the status quo) depends on a small number of decisive breakthrough contributors; that such contributors cannot be identified in advance at birth; that the process of civilizational advancement must therefore maintain a pool of possibility covering all who are born; that every newborn is structurally incorporated into this pool, objectively functions as a possibility-bearer for becoming such a contributor; and that birth is an irreversible physical fact—to derive the conclusion that each individual, by virtue of functioning as a possibility-bearer in the operation of the civilizational advancement system, possesses a corresponding structural value. This structural value, which cannot be objectively revoked, while can only be subjectively denied, is precisely the ground of human dignity. Pre-structural dignity requires no moral presuppositions, religious beliefs, or social recognition, and possesses four attributes: innateness, universality, stability, and finitude. It is capable of explaining the marginal ethical cases that traditional theories cannot cover, thereby providing a more solid philosophical foundation for modern human rights and judicial ethics.

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder, definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls. To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule and regional gray matter volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

This research aims to identify the investor’s decision-making styles and their impact on entrepreneurial opportunities through the mediation role of entrepreneurial intention and moderation effect of social capital in the healthcare sector of Pakistan. This study applied a structural equation modeling to test the hypotheses on a sample of 400 healthcare investors. Our findings reveal that the conditional indirect relationship of entrepreneurial intention through social capital was significant on opportunity creation and an insignificant influence on opportunity discovery from decision-making styles. This study provides implications for policymakers to enhance entrepreneurial opportunity creation by providing robust social environment investors and encouraging them to create business ventures in the healthcare sector.

The evolution of the technology standard alliance is examined using complex adaptive system theory. Taking TSA as a dynamic CAS, an echo model is constructed to depict the mechanism of its evolution, and a model is simulated on the NetLogo platform. The echo model includes a basic model, an extended model, and a three-layer echo model. The adhesive aggregation of agents is explained, and the three evolutionary stages of agents’ entry, migration, and exit are analyzed. Moreover, the adaptability of agents in TSA is quantified. The results of simulation show the evolution of the TSA in relation to the two aspects of agent adhesion aggregation and agent resource interaction, and they demonstrate the dynamic and complex hierarchical structure of the TSA system. It is proposed that greater matching ability, moderate behavior income, and lower behavior cost are more conducive to the evolution and development of TSA. Additionally, the echo model is reconstructed to expand the range of application of CAS theory.