Conceptual models as representations of real-world systems are based on diverse techniques in various disciplines but lack a framework that provides multidisciplinary ontological understanding of real-world phenomena. Concurrently, systems complexity has intensified, leading to a rise in developing models using different formalisms and diverse representations even within a single domain. Conceptual models have become larger; languages tend to acquire more features, and it is not unusual to use different modeling languages for different components. This diversity has caused problems with consistency between models and incompatibly with designed systems. Two main solutions have been adopted over the last few years: (1) A currently dominant technology-based solution tries to harmonize or unify models, e.g., unifies EER and UML. This solution would solidify modeling achievements, reaping benefits from huge investments over the last thirty years. (2) A less prevalent solution is to pursuit deeper roots that reveal unifying modeling principles and apparatuses. An example of the second method is a category theory-based approach that utilizes the strengths of the graph and set theory, along with other topological tools. This manuscript is a sequel in a research venture that belongs to the second approach and uses a model called thinging machines (TMs) founded on Stoic ontology and Lupascian logic. TM modeling contests the thesis that there is no universal approach that covers all aspects of an application, and the paper demonstrates that pursuing such universality is anything but a dead-end method. This paper continues in this direction, with emphasis on TM foundation (e.g., existence and subsistence of things) and exemplifies this pursuit by proposing an alternative representation of set theory.

翻译：概念模型作为现实世界系统的表征，基于各学科中多样化的技术，但缺乏一个能提供对现实世界现象的多学科本体论理解的框架。与此同时，系统复杂性日益增强，导致即使在单一领域内，使用不同形式主义和多样化表示来开发模型的情况也日益增多。概念模型变得更大；建模语言倾向于获取更多特性，并且针对不同组件使用不同建模语言也并非罕见。这种多样性引发了模型间的一致性问题以及与所设计系统的不兼容性。过去几年中，主要采用了两种解决方案：（1）当前占主导地位的基于技术的解决方案试图协调或统一模型，例如统一EER和UML。这一方案将固化建模成果，并从过去三十年的大量投资中获益。（2）一种较不普遍的解决方案是追求更深层次的根源，以揭示统一的建模原理和工具。第二种方法的一个例子是基于范畴论的方法，该方法利用了图论和集合论的优势，以及其他拓扑工具。本文是隶属于第二种方法的一项研究工作的延续，并使用了一种名为“事物机器”（TMs）的模型，该模型基于斯多葛本体论和卢帕斯逻辑。TM建模质疑了不存在能够涵盖应用所有方面的通用方法的论点，并论证了追求这种通用性绝非死胡同。本文继续沿此方向推进，重点阐述TM基础（例如，事物的存在与存续），并通过提出集合论的替代表示来示例这一追求。