Modular Data Research Articles

Braided Zestings of Verlinde Modular Categories and Their Modular Data

Zesting of braided fusion categories is a procedure that can be used to obtain new modular categories from a modular category with non-trivial invertible objects. In this paper, we classify and construct all possible braided zesting data for modular categories associated with quantum groups at roots of unity. We produce closed formulas, based on the root system of the associated Lie algebra, for the modular data of these new modular categories.

A Tidy Framework and Infrastructure to Systematically Assemble Spatio-temporal Indexes from Multivariate Data

Indexes are useful for summarizing multivariate information into single metrics for monitoring, communicating, and decision-making. While most work has focused on defining new indexes for specific purposes, more attention needs to be directed toward making it possible to understand index behavior in different data conditions, and to determine how their structure affects their values and the variability therein. Here we discuss a modular data pipeline recommendation to assemble indexes. It is universally applicable to index computation and allows investigation of index behavior as part of the development procedure. One can compute indexes with different parameter choices, adjust steps in the index definition by adding, removing, and swapping them to experiment with various index designs, calculate uncertainty measures, and assess indexes’ robustness. The article presents three examples to illustrate the usage of the pipeline framework: comparison of two different indexes designed to monitor the spatio-temporal distribution of drought in Queensland, Australia; the effect of dimension reduction choices on the Global Gender Gap Index (GGGI) on countries’ ranking; and how to calculate bootstrap confidence intervals for the Standardized Precipitation Index (SPI). The methods are supported by a new R package, called tidyindex. Supplemental materials for the article are available online.

Experimental and Numerical Investigation of Airflow Organization in Modular Data Centres Utilizing Floor Grid Air Supply

Under the background of “dual carbon” development goals, the rapid expansion of internet data centers driven by advancements in 5G technology has led to increased energy consumption and elevated heat densities within the server rooms in these facilities. In this study, the modular data center is taken as the research object for the purpose of figuring out a way to improve the thermal environment of the computer room, reduce power consumption, and ensure the safe and stable running of servers. To this end, this study established an airflow organization model for the modular data center and verified this model through experimental methods. Computational Fluid Dynamics (CFD) simulations were employed to investigate the effects of raised floor height, floor opening rate, and cold/hot air channel closure on airflow organization. Furthermore, the efficiency of airflow organization was evaluated using entransy loss metrics. The results show that optimal airflow conditions are achieved when the height of the raised floor is 600–800 mm, the opening rate is 40%, and the combined opening is 40%. Additionally, the closure of either the cold channel or both the cold and hot channels significantly improves airflow performance. Specifically, cold channel closure is recommended for new data centers with underfloor air supply systems, while combined cold and hot channel closure is suitable for data centers with high power density and extended air supply distances.

A non-unitary bulk-boundary correspondence: Non-unitary Haagerup RCFTs from S-fold SCFTs

We introduce a novel class of two-dimensional non-unitary rational conformal field theories (RCFTs) whose modular data are identical to the generalized Haagerup-Izumi modular data. Via the bulk-boundary correspondence, they are related to the three-dimensional non-unitary Haagerup topological field theories, recently constructed by a topological twisting of three-dimensional N=4 rank-zero superconformal field theories (SCFTs), called S-fold SCFTs. We propose that, up to the overall factors, the half-indices of the rank-zero SCFTs give the explicit Nahm representation of four conformal characters of the RCFTs including the vacuum character. Using the theory of Bantay-Gannon, we can successfully complete them into the full admissible conformal characters of the RCFTs.

Noise considerations for the distributed modular-based approach to cryptocurrency data centers

While the establishment of cryptocurrency data centers becomes increasingly prevalent, the significant noise impact of these facilities on the surrounding community and environment has also become of interest. One of the most common designs for cryptocurrency mining facilities is to utilize a set of modular data centers, commonly constructed out of modified shipping containers, which are distributed over a large space. Each individual data center module can contain a large number of cryptocurrency mining computers, with each of these equipped with its own cooling fan system. The individual miner fans, coupled with the large ventilation fans required to cool the data center modules themselves, can lead to a complex, widespread acoustic environment that can be difficult to mitigate. This work presents commonly implemented noise mitigation strategies for cryptocurrency mining operations and the common issues that come along with those strategies, as well as operational limitations that must be considered when designing the proper noise control solution. Applications to regulatory constraints on noise, or lack thereof, are discussed.

MosaicMPI: a framework for modular data integration across cohorts and -omics modalities.

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model

Abstract. This article describes a modular ensemble-based data assimilation (DA) system which is developed for an integrated surface–subsurface hydrological model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF), which provides various assimilation algorithms like the ensemble Kalman filters, non-linear filters, 3D-Var and combinations among them. The integrated surface–subsurface hydrological model is HydroGeoSphere (HGS), a physically based modelling software for the simulation of surface and variably saturated subsurface flow, as well as heat and mass transport. The coupling and capabilities of the modular DA system are described and demonstrated using an idealised model of a geologically heterogeneous alluvial river–aquifer system with drinking water production via riverbank filtration. To demonstrate its modularity and adaptability, both single and multivariate assimilations of hydraulic head and soil moisture observations are demonstrated in combination with individual and joint updating of multiple simulated states (i.e. hydraulic heads and water saturation) and model parameters (i.e. hydraulic conductivity). With the integrated model and this modular DA framework, we have essentially developed the hydrologically and DA-wise robust toolbox for developing the basic model for operational management of coupled surface water–groundwater resources.

A novel multi-sensor hybrid fusion framework

Multi-sensor data fusion has emerged as a powerful approach to enhance the accuracy and robustness of diagnostic systems. However, effectively integrating multiple sensor data remains a challenge. To address this issue, this paper proposes a novel multi-sensor fusion framework. Firstly, a vibration signal weighted fusion rule based on Kullback–Leibler divergence-permutation entropy is introduced, which adaptively determines the weighting coefficients by considering the positional differences of different sensors. Secondly, a lightweight multi-scale convolutional neural network is designed for feature extraction and fusion of multi-sensor data. An ensemble classifier is employed for fault classification, and an improved hard voting strategy is proposed to achieve more reliable decision fusion. Finally, the superiority of the proposed method is validated using modular state detection data from the Kaggle database.

Secured and Modular Data Portal: Database System to Manage Broadly Classified and Large-Scale Data

Secured and Modular Data Portal: Database System to Manage Broadly Classified and Large-Scale Data

Recurrent variational autoencoder approach for remaining useful life estimation

Abstract A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. A solution based on a novel recurrent version of a VAE is proposed in this paper to overcome this gap. The latent space learned by the model, trained with data from sensors placed in different parts of these engines, is exploited to build a self-explanatory map that can visually evaluate the rate of deterioration of the engines. Besides, a simple regressor model is built on top of the learned features of the encoder in order to numerically predict the RUL. As a result, remarkable prognostic accuracy is achieved, outperforming most of the novel and state-of-the-art approaches on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. In addition, a practical real-world application is included for Turbofan engine data. This study shows that the proposed prognostic and explainable framework presents a promising new approach.

Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.

Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore, there exists a need for more efficient retrieval and storage of curated patient data from dissimilar sources, such as biobanks, health records, and sensors. We describe a customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, and show its feasibility applied to research at our institution. It uses the workflow automation platform Camunda (Camunda Services GmbH, Berlin, Germany) to collect internal data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources. Using the web-based graphical user interface (GUI), the workflow runs tasks to completion according to visual representation, retrieving and storing results for patients meeting study inclusion criteria while segregating errors for human review. We showcase RIL-workflow with its library of ready-to-use modules, enabling researchers to specify human input or automation at fixed steps. We validated our workflow by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research. Further, we solicited user feedback to highlight the pros and cons associated with RIL-workflow. The source code is available at github.com/magnooj/RIL-workflow.

Adaptive Model Selection in Stock Market Prediction: A Modular and Scalable Big Data Analytics Approach

Adaptive Model Selection in Stock Market Prediction: A Modular and Scalable Big Data Analytics Approach

Development of a characterization and modeling method for Digital Twins in an assembly use case

This paper presents a cross-application approach for the efficient modeling of Digital Twins. In addition to the definition of the objectives and the corresponding requirements for modular data modeling, it also includes the design of simulation models and their necessary interfaces. In particular, the focus of the method is to simplify the development of a Digital Twin for adaptable assembly systems by using the approach as a base for the conceptional design. Besides a description of the method and possible future developments, this paper also contains the validation of the derived approach in an assembly use case.

Establishing a national indicator-based surveillance system for hospital bed utilization by COVID-19 patients in the Philippines.

In March 2020, the Philippine Department of Health (DOH) designed and rapidly implemented a national surveillance system for the utilization of hospital beds by patients with coronavirus disease (COVID-19) to produce complete and timely data for use by various levels of governance in response to the COVID-19 pandemic. The DOH launched the DOH DataCollect (DDC) Bed Tracker system, a web-based application that collects information from all 1906 public and private hospitals and infirmaries across the country using a modular data collection tool. Data on the maximum number of occupied COVID-19-designated beds (n=28 261), hospital bed utilization rate (71.7%), and mechanical ventilator number (n=1846) and utilization rate (58.5%) were recorded in September 2021 during the Delta surge of cases in the Philippines. Data on human resources, personal protective equipment and supplies, and other operational indicators were added to the system during various modifications. Information from the DDC was used to inform the COVID-19 response and operations at national and local levels and facilitated research at academic and nongovernmental agencies. The development of the DDC system demonstrates that an effective surveillance system for use by all health-care facilities is achievable through strong national leadership, the use of available technology and adaptive information systems, and the establishment of networks across different health facilities and stakeholders.

Modular differential equations with movable poles and admissible RCFT characters

Studies of modular linear differential equations (MLDE) for the classification of rational CFT characters have been limited to the case where the coefficient functions (in monic form) have no poles, or poles at special points of moduli space. Here we initiate an exploration of the vast territory of MLDEs with two characters and any number of poles at arbitrary points of moduli space. We show how to parametrise the most general equation precisely and count its parameters. Eliminating logarithmic singularities at all the poles provides constraint equations for the accessory parameters. By taking suitable limits, we find recursion relations between solutions for different numbers of poles. The cases of one and two movable poles are examined in detail and compared with predictions based on quasi-characters to find complete agreement. We also comment on the limit of coincident poles. Finally we show that there exist genuine CFT corresponding to many of the newly-studied cases. We emphasise that the modular data is an output, rather than an input, of our approach.

Development and experimental study of a compact silica gel-water adsorption chiller for waste heat driven cooling in data centers

In recent years, data centers have experienced rapid construction, leading to a significant surge in energy consumption, and cooling systems have emerged as prominent energy consumers of data centers. Among the cooling technologies, liquid cooling allows higher cooling circuit temperatures and provides potential for heat recovery. As a thermally driven cooling technology, adsorption refrigeration systems offer an alternative way to compression refrigeration systems by utilizing low-grade waste heat from liquid-cooled circuits to produce chilled water for air-cooled components. In this study, a compact silica gel-water adsorption chiller is developed for a small modular data center and experimentally investigated under extremely low driving temperatures. Under the typical summer conditions with the hot/cooling/chilled water inlet temperatures of 50 °C/30 °C/23 °C, the adsorption chiller achieves the cooling power of 4.9 kW and the COP of 0.50. When the cooling water inlet temperature changes from 32 °C to 24 °C, both the cooling power and COP show significant improvements, from 3.04 kW to 10.50 kW and from 0.44 to 0.66, respectively. Moreover, the increase of the hot water inlet temperature from 48 °C to 56 °C can enhance the cooling power to some extent, while the chilled water inlet temperature has little influence on the performance. The experimental results demonstrate the feasibility and performance of the developed adsorption chiller in data center waste heat driven cooling. Besides, potential adsorbents and possible optimization strategies are presented for performance improvement based on adsorption potential method.

Towards data-informed co-design in digital fabrication

Industry 4.0 has the potential to revolutionise design-to-fabrication processes in the Architecture, Engineering, and Construction (AEC) industry. However, early adoption of Industry 4.0 technology has led to challenges related to data consistency, interoperability, and collaboration among stakeholders. To address these challenges of interoperability and standardisation, this paper introduces a digital thread-based framework for integrating Industry 4.0 concepts into design-to-fabrication processes. At the framework's core are digital threads, which connect physical assets with their digital twin models, communication networks, and decision algorithms. More specifically, the framework comprises a modular and scalable fabrication data schema combined with a comprehensive services software architecture that leverages fabrication design, management, and control tools. The framework was evaluated through a large-scale case study, the livMats Biomimetic Shell. The case study results demonstrate an improvement in collaboration and communication among stakeholders, reduction of errors in the robotic fabrication process and increased efficiency throughout the design-to-fabrication process. By embracing and implementing the digital thread concept, this research showcases the benefits of data-driven decision-making within the construction sector.

Modular Hamiltonian for de Sitter diamonds

We determine the Tomita-Takesaki modular data for CFTs in double cone and light cone regions in conformally flat spacetimes. This includes in particular the modular Hamiltonian for diamonds in the de Sitter spacetime. In the limit where the diamonds become large, we show that the modular automorphisms become time translations in the static patch. As preparation, we also provide a pedagogical rederivation of the known results for Minkowski spacetime. With our results and using the Araki formula, it becomes possible to compute relative entanglement entropies for CFTs in these regions.

Modular Data Plane Verification for Compositional Networks

Modern networks are increasingly using layering and bridging to form a compositional architecture. Layering protocols like VXLAN create multiple overlay networks on top of a single underlay network infrastructure. This makes network configurations even more complex, and error-prone. To check the correctness of such compositional networks, one needs to model the dependency across multiple layers (underlay and overlay) and multiple domains (different VPNs/VPCs). Existing verifiers, which are optimized to scale in single-layer single-domain networks, exhibit scalability limitations when applied to compositional networks. This paper proposes MNV, a modular network verifier that scales to large compositional networks. At its core is a new verification method termed decompose-merge reasoning, which decomposes the network into self-contained modules, verifies each module independently, and merges the verification results. Our experiments show that for a typical data center network virtualized with VXLAN, to check reachability for more than 100 million pairs of subnets, MNV is at least 100x faster than state-of-the-art tools.

Design of a multiparameter data acquisition and control system for in situ seabed observation base stations

AbstractWith the exploration, development, and research of deep‐sea resources, there is an urgent need for long‐term and continuous observation data of the deep‐sea seabed boundary layer. The traditional method of deep‐sea seabed survey and sampling based on scientific research vessels has the discontinuity of observation data in space and time scales. There are some problems in the seabed in situ observation method based on the seabed observation network for low mobility and high operation and maintenance costs, restricting the in‐depth understanding of the dynamic change process of the deep‐sea floor. To solve the above problems, an open and modular data acquisition control system was designed based on an embedded system and signal processing technology. In terms of the physical, chemical, geological, and ecosystem characteristics of the seafloor or near the seafloor boundary layer, various functional sensors and instrumentation were matched to form an independent underwater integrated measurement or experimental device, eventually realizing in situ multiparameter and long‐time series observations of the seafloor. The system data acquisition and control test were completed through laboratory experiments, which verified the feasibility of the system design. The research showed important theoretical and technical reference significance for the exploration and development of resources in the submarine boundary layer and the promotion of deep‐sea scientific research.