Оцінка інвестиційної привабливості промислових підприємств

The article is devoted to methodological issues of assessing the investment attractiveness of industrial enterprises, based on the nature of investments and the goals of their initiators. Various methods for assessing the economic efficiency of investments in newly created, existing and reconstructed industrial enterprises are examined, and the conditions for their application are determined. The necessity of calculating the economic efficiency of direct investment as part of changing the entire production and economic system of the investor is justified. A structural and logical scheme of interaction between the potential investor and the investment object directly for the implementation of investment projects was proposed, taking into account the number of investments, type of investor and term of the investment. The authors also proposed a methodology that can be used by an investor to plan for increasing the investment attractiveness of an enterprise and a formula for calculating the coefficient of investment attractiveness of an enterprise.Стаття присвячена методологічним питанням оцінки інвестиційної привабливості промислових підприємств, виходячи з характеру інвестицій та цілей їх ініціаторів. Розглядаються різні методи оцінки економічної ефективності інвестицій у новостворені, існуючі та реконструйовані промислові підприємства та визначаються умови їх застосування. Обґрунтовано необхідність розрахунку економічної ефективності прямих інвестицій як частини зміни всієї виробничої та економічної системи інвестора. Запропонована структурно-логічна схема взаємодії між потенційним інвестором та об'єктом інвестування безпосередньо для реалізації інвестиційних проектів з урахуванням кількості інвестицій, типу інвестора та строку інвестування. Автори також запропонували методологію, за допомогою якої інвестор може планувати підвищення інвестиційної привабливості підприємства, та формулу для розрахунку коефіцієнта інвестиційної привабливості підприємства

BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems

In this report, we present work towards a framework for modeling and checking behavior of spatially distributed component systems. Design goals of our framework are the ability to model spatial behavior in a component oriented, simple and intuitive way, the possibility to automatically analyse and verify systems and integration possibilities with other modeling and verification tools. We present examples and the verification steps necessary to prove properties such as range coverage or the absence of collisions between components and technical details

Examining Connections between Gendered Dimensions of Inequality and Deforestation in Nepal

The United Nations recognizes empowering women as a key component of achieving numerous development-related goals. Qualitative studies suggest that communities where men and women have equal levels of agency over resource allocation and land tenure sometimes experience decreases in forest degradation and deforestation, all else being equal. However, these patterns are spatially heterogeneous, as are patterns of gender inequality in terms of land tenure and agency. This paper uses data from the Demographic and Health Surveys (DHS) to quantify the relationship between gender inequality and ecosystem degradation using three linear regression models, Empirical Bayesian Kriging, and mapping the intersections between gender inequality and deforestation. Results from LASSO, Ordinary Least Squares, and Stepwise regression models show that there is no linear relationship between gender inequality and deforestation. Additionally, the distributions of gender inequality as it pertains to land tenure and deforestation are highly heterogeneous over space, indicating potential sociocultural and sociodemographic factors not captured in my data. Further work should focus on identifying ways to incorporate complex gender dynamics into environmental planning at multiple levels of forest governance

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature

Categoricity, Open-Ended Schemas and Peano Arithmetic

One of the philosophical uses of Dedekind’s categoricity theorem for Peano Arithmetic is to provide support for semantic realism. To this end, the logical framework in which the proof of the theorem is conducted becomes highly significant. I examine different proposals regarding these logical frameworks and focus on the philosophical benefits of adopting open-ended schemas in contrast to second order logic as the logical medium of the proof. I investigate Pederson and Rossberg’s critique of the ontological advantages of open-ended arithmetic when it comes to establishing the categoricity of Peano Arithmetic and show that the critique is highly problematic. I argue that Pederson and Rossberg’s ontological criterion deliver the bizarre result that certain first order subsystems of Peano Arithmetic have a second order ontology. As a consequence, the application of the ontological criterion proposed by Pederson and Rossberg assigns a certain type of ontology to a theory, and a different, richer, ontology to one of its subtheories

IDEF5 Ontology Description Capture Method: Concept Paper

The results of research towards an ontology capture method referred to as IDEF5 are presented. Viewed simply as the study of what exists in a domain, ontology is an activity that can be understood to be at work across the full range of human inquiry prompted by the persistent effort to understand the world in which it has found itself - and which it has helped to shape. In the contest of information management, ontology is the task of extracting the structure of a given engineering, manufacturing, business, or logistical domain and storing it in an usable representational medium. A key to effective integration is a system ontology that can be accessed and modified across domains and which captures common features of the overall system relevant to the goals of the disparate domains. If the focus is on information integration, then the strongest motivation for ontology comes from the need to support data sharing and function interoperability. In the correct architecture, an enterprise ontology base would allow th e construction of an integrated environment in which legacy systems appear to be open architecture integrated resources. If the focus is on system/software development, then support for the rapid acquisition of reliable systems is perhaps the strongest motivation for ontology. Finally, ontological analysis was demonstrated to be an effective first step in the construction of robust knowledge based systems

An Inquiry-Infused Introductory Biology Laboratory That Integrates Mendel\u27s Pea Phenotypes With Molecular Mechanisms

We developed a multi-week laboratory in which college-level introductory biology students investigate Mendel\u27s stem length phenotype in peas. Students collect, analyze and interpret convergent evidence from molecular and physiological techniques. In weeks 1 and 2, students treat control and experimental plants with Gibberellic Acid (GA) to determine whether uncharacterized short mutant lines are GA responsive. These data allow students to place the mutation in the GA signal transduction pathway. During weeks 2 and 3, plants are genotyped for Mendel\u27s le mutation using a derived cleaved polymorphic sequences (dCAPS) PCR assay. This laboratory allows students to make a direct connection between modern molecular genetics and the easily scored phenotypes Mendel used as the basis of his fundamental discoveries. We administered surveys to assess student gains in accord with four learning goals: understanding the lab, basic science literacy, scientific practices, and working collaboratively. Student confidence increased significantly in the first three, but not in working collaboratively, although students reported greater confidence working in groups than alone