We develop semantics and syntax for bicategorical type theory. Bicategorical type theory features contexts, types, terms, and directed reductions between terms. This type theory is naturally interpreted in a class of structured bicategories. We start by developing the semantics, in the form of comprehension bicategories. Examples of comprehension bicategories are plentiful; we study both specific examples as well as classes of examples constructed from other data. From the notion of comprehension bicategory, we extract the syntax of bicategorical type theory, that is, judgment forms and structural inference rules. We prove soundness of the rules by giving an interpretation in any comprehension bicategory. The semantic aspects of our work are fully checked in the Coq proof assistant, based on the UniMath library.

In this study, we investigate the creation and persistence of interfirm ties in a large-scale business transaction network. Business transaction relations (firms buying or selling products or services to each other) are driven by economic motives, but because trust is essential to business relationships, the social connections of owners or the geographical proximity of firms can also influence their development. However, studying the formation of interfirm business transaction ties on a large scale is rare, because of the significant data demand. The business transaction and the ownership networks of Hungarian firms are constructed from two administrative datasets for 2016 and 2017. We show that direct or indirect connections in this two-layered network, including open triads in the business network, contribute to both the creation and persistence of business transaction ties. For our estimations, we utilize log-linear models and emphasize their efficiency in predicting links in such large networks. We contribute to the literature by presenting different patterns of business connections in a nationwide multilayer interfirm network.

Within the last decade, online sustainability knowledge-action platforms have proliferated. We surveyed 198 sustainability-oriented sites and conducted a review of 41 knowledge-action platforms, which we define as digital tools that advance sustainability through organized activities and knowledge dissemination. We analyzed platform structure and functionality through a systematic coding process based on key issues identified in three bodies of literature: (a) the emergence of digital platforms, (b) the localization of the sustainable development goals (SDGs), and (c) the importance of multi-level governance to sustainability action. While online collaborative tools offer an array of resources, our analysis indicates that they struggle to provide context-sensitivity and higher-level analysis of the trade-offs and synergies between sustainability actions. SDG localization adds another layer of complexity where multi-level governance, actor, and institutional priorities may generate tensions as well as opportunities for intra- and cross-sectoral alignment. On the basis of our analysis, we advocate for the development of integrative open-source and dynamic global online data management tools that would enable the monitoring of progress and facilitate peer-to-peer exchange of ideas and experience among local government, community, and business stakeholders. We argue that by showcasing and exemplifying local actions, an integrative platform that leverages existing content from multiple extant platforms through effective data interoperability can provide additional functionality and significantly empower local actors to accelerate local to global actions, while also complex system change.

This article proposes a control method for underactuated cartpole systems using semi-implicit cascaded proportional-derivative (PD) controller. The proposed controller is composed of two conventional PD controllers, which stabilizes the pole and the cart second-order dynamics respectively. The first PD controller is realized by transforming the pole dynamics into a virtual PD controller, with the coupling term exploited as the internal tracking target for the cart dynamics. Then, the second PD controller manipulates the cart dynamics to track that internal target. The solution to the internal tracking target relies on an equation set and features a semi-implicit process, which exploits the internal dynamics of the system. Besides, the design of second PD controller relies on the parameters of the first PD controller in a cascaded manner. A stability analysis approach based on Jacobian matrix is proposed and implemented for this fourth-order system. The proposed method is simple in design and intuitive to comprehend. The simulation results illustrate the superiority of proposed method compared with conventional double-loop PD controller in terms of convergence, with the theoretical conclusion of at least locally asymptotic stability.

The main goal of this study is to investigate if the publicly available sea state forecasts for the Aran Islands region in the Republic of Ireland can be improved. This improvement is achieved by using the combination of local scale sea state forecasts and Bayesian Model Averaging techniques. The question of a good forecast has been around since the start of forecasting. With current state-of-the-art numerical models, computational power, and vast data availability, we consider whether it is possible to improve model forecasts only by using the combination of publicly available forecasts, free open-source software, and very moderate computational power. It is shown that it is possible to improve the sea state forecast by at least $ 1\% $, and in some cases up to $ 8\% $. The reduction of error is between $ 6\% $ and $ 48\% $. With a more careful and specific selection of training parameters, it is possible to improve the forecast accuracy even more. The possibility of extending this local improvement to the whole coastal area around the island of Ireland is explored. Unfortunately, it is currently impossible, due to a lack of live data buoys in the coastal waters. Nonetheless, it is shown that the proposed process is simple and can be implemented by anyone whose livelihood depends on an accurate sea state forecast. It does not require large computational power, model forecasts are publicly available, and there is minimal to no training in forecasting and statistics required to enable one to perform such improvements for one’s area of interest, provided one has access to live wave data.

Soft sets were introduced as a means to study objects that are not defined in an absolute way and have found applications in numerous areas of mathematics, decision theory, and in statistical applications. Soft topological spaces were first considered in Shabir and Naz ((2011). Computers & Mathematics with Applications 61 (7) 1786–1799) and soft separation axioms for soft topological spaces were studied in El-Shafei et al. ((2018). Filomat 32 (13) 4755–4771), El-Shafei and Al-Shami ((2020). Computational and Applied Mathematics 39 (3) 1–17), Al-shami ((2021). Mathematical Problems in Engineering 2021). In this paper, we introduce the effective versions of soft separation axioms. Specifically, we focus our attention on computable u-soft and computable p-soft separation axioms and investigate various relations between them. We also compare the effective and classical versions of these soft separation axioms.