Policy integration, problem-solving, and the coronavirus disease crisis: lessons for policy design

The coronavirus disease pandemic has exposed differences in the capacity of governments around the world to integrate and coordinate different policy instruments into a coherent response. In this article, we conceptualize and empirically examine policy integration in responses to the coronavirus disease crisis in 35 countries. We then discuss how the interplay between restrictions, health protection, and economic policy has been articulated between, on the one hand, a policy design based on the complementarity of pro-public health and pro-economy measures, implying an integrated response, and, on the other, a policy design based on the perception of an inherent trade-off between the two. Finally, we discuss three implications from our analysis of policy integration against the coronavirus disease crisis for the post-COVID state: (a) the normalization and adaptation of integrated crisis responses; (b) the possible acceleration and “catching up” of problem-solving capacity as governments may use the crisis as an instance to put into place new social policies; and (c) policy integration as an accelerator of policy complexity and resistance against technocracy in the post-COVID state.

Integration Of RFID With WLAN

Radio Frequency Identification (RFID) and Wi-Fi WLANs have achieved widespread applicability in different application domains. However, tag range of RFID systems is very short. Hence, integrating RFID with WLAN networks can contribute to wider application of RFID since Wi-FI nodes have much larger communication range. However, both RFID and WLAN use the same frequency band and incurs interference by [sic] each other for channel utilization. In this thesis, an efficient approach to solve the coexistence and integration problem of RFID and Wi-Fi WLAN is proposed. This solution allows these networks to access the medium in a time sharing manner by making the WLAN Access Point (AP) aware of the RFID neighboring network at the Medium Access Control (MAC) layer. Thus, it is possible to locate and identify the RFID tags in the physical space, with co-located Wi-Fi WLANS. Simulation results show that both networks work together by maintaining the performance such as higher throughput and lower collision probability, as is desired.

Integration Of RFID With WLAN

Radio Frequency Identification (RFID) and Wi-Fi WLANs have achieved widespread applicability in different application domains. However, tag range of RFID systems is very short. Hence, integrating RFID with WLAN networks can contribute to wider application of RFID since Wi-FI nodes have much larger communication range. However, both RFID and WLAN use the same frequency band and incurs interference by [sic] each other for channel utilization. In this thesis, an efficient approach to solve the coexistence and integration problem of RFID and Wi-Fi WLAN is proposed. This solution allows these networks to access the medium in a time sharing manner by making the WLAN Access Point (AP) aware of the RFID neighboring network at the Medium Access Control (MAC) layer. Thus, it is possible to locate and identify the RFID tags in the physical space, with co-located Wi-Fi WLANS. Simulation results show that both networks work together by maintaining the performance such as higher throughput and lower collision probability, as is desired.

Unsupervised weights selection for optimal transport based dataset integration

A formulation of the dataset integration problem describes the task of aligning two or more empirical distributions sampled from sources of the same kind, so that records of similar object end up close to one another. We propose a variant of the optimal transport- and Gromov-Wasserstein-based dataset integration algorithm introduced in SCOT. We formulate a constrained quadratic program to adjust sample weights before OT or GW so that weighted point density is close to be uniform over the point cloud, for a given kernel. We test this method with one synthetic and two real-life datasets from single-cell biology. Weights adjustment allows distributions with similar effective supports but different local densities to be reliably integrated, which is not always the case with the original method. This approach is entirely unsupervised, scales well to thousands of samples and does not depend on dimensionality of the ambient space, which makes it efficient for the analysis of single-cell datasets in biology. We provide an open-source implementation of this method in a Python package, woti.

Mapping of the descriptive logic into RDF using binary relational data model

This paper is dedicated to the data integration problem. To establish relationships between data models is one of the key tasks in this solution. The descriptive logic and the relational data model are at the heart of a study. They have been used to create a mapping method on the theoretical level. The binary relational data model has been developed as a part of a mapping method. The previous studies are continued in this paper to prove on practice a mapping creation method between the descriptive logic and the binary relational data model. The method uses the binary relational data model as an integrating model. This paper continues the previous research of practical implementation of the mapping creation between the descriptive logic and the binary relational data model. The task to prove the theoretical mapping method on practice was formulated. A question how to map the binary relational data model into RDF-triples was considered. A brief overview of the R2R ML conversion tool was given. Triple maps were created to convert a conceptual information model of descriptive logic into RDF triplets with the help of R2R ML. Also, triples maps are described to convert basic mapping mechanisms into RDF with the help of R2R ML.

Improving the Integration of Distributed Applications

The interaction of distributed applications raises an integration problem that needs to be solved. Current integration technologies, such as Web Services and RESTful APIs, solve the interoperability problem but usually entail more coupling than required by the interacting applications. This is caused by sharing data schemas between applications, even if not all features of those schemas are actually exercised. The fundamental problem of application integration is therefore how to provide at most the minimum coupling possible while ensuring at least the minimum interoperability requirements. This article proposes compliance and conformance as the concepts to achieve this goal, by sharing only the subset of the features of the data schema that are actually used.

Implementation of mappings between the description logic and the binary relational data model on the RDF level

This paper is dedicated to the data integration problem. In article the task of practical implementation of mappings between description logic and a binary relational data model is discussed. This method was formulated earlier at a theoretical level. A practical technique to test mapping engines using RDF is provided in the current paper. To transform the constructs of the description logic ALC and its main extensions into RDF triplets the OWL 2-to-RDF mappings are used. To convert RDB to RDF graph, the R2R Mapping Language (R2R ML) was chosen. The mappings DL ALC and its main extensions to the RDF triplets are described in the publication. The mapping of the DL axioms into an RDF triplet also is considered in the publication. The main difficulties in describing DL-to-RDF transformations are given in the corresponding section. For each constructor of concepts and roles a corresponding expression in OWL 2 and its mapping into the RDF triplet. A schematic representation of the resulting RDF graph for each mapping is created. The paper also provides an overview of existing methods that relate to the use of RDF when mapping RDB to ontology and vice versa.

MultiBaC: A strategy to remove batch effects between different omic data types

Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.