Multiple Source Systems Research Articles

Fault detection and isolation in system of multiple sources of energy using hierarchical Bayesian belief networks

Fault detection and isolation in system of multiple sources of energy using hierarchical Bayesian belief networks

Validation of a Theoretical Model for Laser Welding Thermal Field by Multi-Physics Numerical Simulation

Theoretical models based on solutions of the conduction heat transfer equation have been widely proposed to calculate the thermal fields generated during laser welding, revealing simplification benefits and limitations in the accuracy of the results. In previous papers, the authors have introduced a parameterized analytical model based on the configuration of a virtual system of multiple mobile heat sources that simulates the effects of an actual keyhole welding mode by setting the system parameters so as to fit the calculated contours of the fusion zone in the weld cross-section of the experimental one. Even though a basic validation was already carried out by experimental detection, in order to further strengthen the model validity, this article deals with an extensive comparison between the results obtained by a multi-physics numerical simulation, performed by a commercial CFD software, and a theoretical one. The two different approaches were applied to the laser beam welding of butt-positioned AISI 304L steel plates. The investigation was focused on the effects of the keyhole on the main morphological features of the melt pool and fusion zone, and on the thermal fields obtained by the two models. The intrinsic differences between the two approaches, and how they are reflected in the corresponding results, were discussed. Satisfactory results were obtained by comparing the thermal fields, with a substantial convergence of the results, so as to validate the analytical model, assess the accuracy of its results, and define its application limits.

Energy management strategies of hybrid electric vehicles: A comparative review

AbstractWith increasing environmental problems, including air pollution, rising greenhouse gases and global warming, the need to use clean energy resources seems essential. Therefore, the usage of clean and renewable energy sources is suggested as a suitable solution to overcome the mentioned concerns. The transportation section contributes to a huge percentage of energy consumption. Hybrid electric vehicles (HEVs), by combining several energy resources, are considered as a crucial solution to decrease fossil fuel consumption and improve the environmental challenges. The existence of an alternative energy resource and the internal combustion engine together provides optimal power distribution among them to maximise power usage and minimise fuel consumption. Energy management strategy (EMS) is an essential challenge in HEV's design procedure to deal with the power distribution in multiple power source systems to improve the performance of the HEVs. A review of various EMSs for HEVs, followed by an analysis of each type, including its benefits and drawbacks, is presented by the authors. In addition to this, the major challenges in EMSs for HEVs are described and a comprehensive review is presented for strategies addressing these issues.

A High-Resolution Analytical Thermal Modeling Method of Capacitor Bank Considering Thermal Coupling and Different Cooling Modes

Thermal stress is of crucial importance to capacitor reliability. However, for a capacitor bank, on spatial scale, the complex heat transfer modes are not clearly illustrated; and on time scale, the ESR aging is neither fully discussed in current evaluation methods. These could lead to a glaring error in the prediction of temperature distribution and lifetime estimation. Therefore, this paper proposed an analytical thermal modeling method with high-resolution for the capacitor bank, considering the thermal coupling effect between individual capacitors, as well as different cooling conditions and the heat variation caused by ESR aging. Firstly, the improved thermal state-space modeling method is proposed to universally describe a multiple heat source system. Then, based on heat transfer and fluid mechanic theories, the characterization method of thermal coupling effect in different cooling modes is discussed. Furthermore, the ESR aging model is applied in the electro-thermal analysis of capacitor bank, aiming to improve the accuracy of thermal calculation in long time scale. Finally, to alleviate the uneven temperature distribution in an in-line designed capacitor bank, a staggered design method is proposed followed by a case study, where a nine-capacitor bank is presented to validate the corresponding modeling method and optimization.

Predicting Individual Patient Platelet Demand in a Large Tertiary Care Hospital Using Machine Learning

Introduction: An increasing shortage of donor blood is expected, considering the demographic change in Germany. Due to the short shelf life and varying daily fluctuations in consumption, the storage of platelet concentrates (PCs) becomes challenging. This emphasizes the need for reliable prediction of needed PCs for the blood bank inventories. Therefore, the objective of this study was to evaluate multimodal data from multiple source systems within a hospital to predict the number of platelet transfusions in 3 days on a per-patient level. Methods: Data were collected from 25,190 (42% female and 58% male) patients between 2017 and 2021. For each patient, the number of received PCs, platelet count blood tests, drugs causing thrombocytopenia, acute platelet diseases, procedures, age, gender, and the period of a patient’s hospital stay were collected. Two models were trained on samples using a sliding window of 7 days as input and a day 3 target. The model predicts whether a patient will be transfused 3 days in the future. The model was trained with an excessive hyperparameter search using patient-level repeated 5-fold cross-validation to optimize the average macro F2-score. Results: The trained models were tested on 5,022 unique patients. The best-performing model has a specificity of 0.99, a sensitivity of 0.37, an area under the precision-recall curve score of 0.45, an MCC score of 0.43, and an F1-score of 0.43. However, the model does not generalize well for cases when the need for a platelet transfusion is recognized. Conclusion: A patient AI-based platelet forecast could improve logistics management and reduce blood product waste. In this study, we build the first model to predict patient individual platelet demand. To the best of our knowledge, we are the first to introduce this approach. Our model predicts the need for platelet units for 3 days in the future. While sensitivity underperforms, specificity performs reliably. The model may be of clinical use as a pretest for potential patients needing a platelet transfusion within the next 3 days. As sensitivity needs to be improved, further studies should introduce deep learning and wider patient characterization to the methodological multimodal, multisource data approach. Furthermore, a hospital-wide consumption of PCs could be derived from individual predictions.

An Orthogonal Experimental Study on the Preparation of Cr Coatings on Long-Size Zr Alloy Tubes by Arc Ion Plating.

Cr-coated Zr alloys are widely considered the most promising accident-tolerant fuel (ATF) cladding materials for engineering applications in the near term. In this work, Cr coatings were prepared on the surfaces of 1400 mm long N36 cladding tubes using an industrial multiple arc source system. Orthogonal analyses were conducted to demonstrate the significance level of various process parameters influencing the characteristics of coatings (surface roughness, defects, crystal orientation, grain structure, etc.). The results show that the arc current mainly affects the coating deposition rate and the droplet particles on the surface or inside the coatings; however, the crystal preferred orientation and grain structure are more significantly influenced by the gas pressure and negative bias voltage, respectively. Then, the underlying mechanisms are carefully discussed. At last, a set of systemic methods to control the quality and microstructures of Cr coatings are summarized.

Deciphering organic matter distribution by source-specific biomarkers in the shallow Taiwan Strait from a source-to-sink perspective

Sedimentary organic matter (OM) in coastal systems is inherently diverse, often with multiple particulate sources and transport histories. The Taiwan Strait (TS) is a typical shallow conduit region, linking the East and South China Seas. Strong ocean currents, coastal upwellings, distal large rivers, and proximal small mountainous rivers all influence the distribution of OM in the TS. We investigated the covarying patterns in the distribution of gain size classes of sand, silt, and clay; terrestrial-sourced biomarkers (n-C27+29+31 alkanes, n-C26+28+30 fatty acids (FAs), and n-C28+30+32 alkanols); marine-sourced biomarkers (phytoplankton-derived alkenones, brassicasterol, dinosterol, and zooplankton-derived cholesterol) in sea floor sediment; indicator satellite-derived primary production (Chl-a); and water-mass indicator (sea surface temperature, SST). We used an empirical orthogonal/eigen function (EOF) analysis to distinguish the influence of four hypothetical sources that entered the TS through the north, south, west, and east boundaries. Results show that input sources from the south-bound ZMCC (Zhejiang-Fujian Coastal Current) and north-bound SCSWC (South China Sea Warm Current) had the dominant influence on the OM distributions buried in the TS. Input sources via river plumes on lateral boundaries and upwellings in the TS were the secondary factors that affected the sedimentary OM distribution. Within this source-to-sink system of multiple sources and transport processes, silt and clay were the major carriers of the OM signals. Terrestrial biomarkers and primary production (Chl-a) were associated with the two major current systems and river plumes along the edge of TS. Marine biomarkers were associated with upwellings in the interior of the TS. Our finding points out that the physical systems of ocean currents, river plumes, and upwelling not only determine the distributions of biomarkers in the TS but also determine the diversity of OM in the TS.

The implementation of a data learning series focused on clinical development teams in a contract research organization

Effective management of a clinical trial requires having real time access to information that provides useful insights into trial progress and that lends itself to collaborative decision making.  Data visualizations using data from multiple source systems employed during the conduct of a clinical trial have become an essential tool in the recent past as support for collaborative decision making by project teams. Having the ability to access, analyze, read, work with, and present data to support an argument are important skills that ensure data visualizations fulfill their purpose in clinical trial management. There is an expectation that members of the clinical trial team either possess or develop the data literacy skill sets necessary to collaborate on the successful execution of a clinical drug development trial. Here we describe the development of a Data Learning Series program targeted to increase the data literacy skills within a Contract Research Organization in support of the digital evolution of the drug development industry.

Word's Predictability Can Modulate Semantic Preview Effect in High-Constraint Sentences.

The processing of words in sentence reading is influenced by both information from sentential context (the effect of predictability) and information from previewing upcoming words (the preview effect), but how both effects interact during online reading is not clear. In this study, we tested the interaction of predictability effect and the preview effect in predicting reading processing. In the experiment, sentence constraint was controlled using all high-constraint sentences as materials. We manipulated both the predictability of the target word in the sentence and the semantic relationship between the preview word and the target word as predictors of the semantic preview effect. The results showed that the semantic preview effect was present only when the target word had low-predictability in the sentence but was not observed when the target word had high-predictability in the sentence. The results suggest that contextual information in reading can modulate the pre-activation of words and thus influence whether the preview word has a priming effect. The results of this study provide further evidence that reading comprehension involves an interactive system of processing multiple sources of information at multiple levels.

Building a Harmonized Datamart by Integrating Cross-Institutional Systems of Clinical, Outcome, and Genomic Data: The Pediatric Patient Informatics Platform (PPIP).

Siloed electronic medical data limits utility and accessibility. At the Dana-Farber/Boston Children's Cancer and Blood Disorders Center, cross-institutional data were inconsistent and difficult to access. To unify data for clinical operations, administration, and research, we developed the Pediatric Patient Informatics Platform (PPIP), an integrated datamart harmonizing multiple source systems across two institutions into a common technology. Starting in 2009, user requirements were gathered and data sources were prioritized. Project teams, including biostatisticians, database developers, and an external contractor, were formed. Read-access to source systems was established. The 3-layer PPIP architecture was developed: STAGING, a near-exact copy of source data; INTEGRATION, where data were reorganized into domains; and, CONSUMPTION, where data were optimized for rapid retrieval. The diverse systems were integrated into a common IBM Netezza technology. Data filters were defined to accurately capture the Center's patients, and derived data items were created for harmonization across sources. An interactive online query tool, PPIP360, was developed using Microstrategy Analytics. Driven by scientific objectives, the PPIP datamart was created, including 33,674 patients, 2,983 protocols, and 3.6 million patient visits from 14 source databases, 164 source tables, and 2,622 source data items. The PPIP360 has 605 data items and 33 metrics across 11 reports and dashboards. Dana-Farber and Boston Children's established a legal data-sharing agreement. The PPIP has supported hundreds of faculty, staff, and projects, including planning clinical trials and informing strategic planning. The PPIP has successfully harmonized and integrated diagnostic, demographic, laboratory, treatment, clinical outcome, pathology, transplant, meta-protocol, and -omics data, for efficient, daily operational and research activities at Dana-Farber/Boston Children's Cancer and Blood Disorders Center, and future external sharing.

Actuator Development Step by Step: Pellet Particle Flux Control for Single- and Multiple-Source Systems

Fuel injection by means of solid cryogenic pellets is expected to provide a sound and efficient tool. Hence, the installation of a pellet launching system will be a necessity. Yet, pellets are considered as a serviceable actuator for integrated supplementary functions as, e.g., fast and efficient delivery of seeding gas, or in case needed, the pacing of edge-localized modes. Consequently, a control scheme has to be developed that is capable of mastering the simultaneous actuations covering different tasks. Our scheme relies on pellet launching by a centrifuge accelerator, providing the option for precisely predictable pellet injection sequences. In order to develop a suitable actuator control scheme, as a first step the central part was brought into service at ASDEX Upgrade. It proved operational for feedback particle flux control of a single pellet source. In a subsequent step, it is now upgraded to enable multitasked control of the JT-60SA multipellet source currently under construction. In its finally designated configuration, this control scheme provides a potential solution for a reactor-grade system.

Towards smart farming: Systems, frameworks and exploitation of multiple sources

Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status.This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture.

Transient response comparison of virtual oscillator controlled and droop controlled three‐phase inverters under load changes

This study compares two inverter control methods, virtual oscillator control (VOC) and droop control, in terms of time responses of a three-phase single-inverter system under a load step change. The analytical comparison is aided by small-signal modelling. Two small-signal VOC models are derived from the average VOC dynamics by approximating average power differently; two small-signal droop control models using distinct filters are also assessed. By letting the two controllers have similar droop characteristics in steady state, participation analyses are performed to predict transient responses of the systems. The analyses are verified by simulations and experiments. This study presents a meticulous comparison between VOC and droop control in a three-phase system. It is demonstrated that two types of linearized models produce similar transient responses in the output voltage and current amplitudes. VOC has a faster transient response in instantaneous frequency while the droop controller can have a transient response as fast as the VOC in the terminal voltage amplitude. This reveals that the VOC and droop controller can be designed to yield similar responses under a load disturbance. Comparing them in a stand-alone system aids in understanding the control loop as a step towards understanding transient behavior in multiple source systems.

Combination of a Big Data Analytics Resource System With an Artificial Intelligence Algorithm to Identify Clinically Actionable Radiation Dose Thresholds for Dysphagia in Head and Neck Patients

PurposeWe combined clinical practice changes, standardizations, and technology to automate aggregation, integration, and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). We then developed novel artificial intelligence algorithms, coupled with the BDARS, to identify structure dose volume histograms (DVH) metrics associated with dysphagia. Methods and MaterialsFrom the BDARS harmonized data of ≥22,000 patients, we identified 132 patients recently treated for head and neck cancer who also demonstrated dysphagia scores that worsened from base line to a maximum grade ≥2. We developed a method that used both physical and biologically corrected (α/β = 2.5) DVH curves to test both absolute and percentage volume based DVH metrics. Combining a statistical categorization algorithm with machine learning (SCA-ML) provided more extensive detailing of response threshold evidence than either approach alone. A sensitivity guided, minimum input, machine learning (ML) model was iteratively constructed to identify the key structure DVH metric thresholds. ResultsSeven swallowing structures producing 738 candidate DVH metrics were ranked for association with dysphagia using SCA-ML scoring. Structures included superior pharyngeal constrictor (SPC), inferior pharyngeal constrictor (IPC), larynx, and esophagus. Bilateral parotid and submandibular gland (SG) structures were categorized by relative mean dose (eg, SG_high, SG_low) as a dose versus tumor centric analog to contra and ipsilateral designations. Structure DVH metrics with high SCA-ML scores included the following: SPC: D20% (equivalent dose [EQD2] Gy) ≥47.7; SPC: D25% (Gy) ≥50.4; IPC: D35% (Gy) ≥61.7; parotid_low: D60% (Gy) ≥13.2; and SG_high: D35% (Gy) ≥61.7. Larynx: D25% (Gy) ≥21.2 and SG_low: D45% ≥28.2 had high SCA-ML scores but were segmented on less than 90% of plans. A model based on SPC: D20% (EQD2 Gy) alone had sensitivity and area under the curve of 0.88 ± 0.13 and 0.74 ± 0.17, respectively. ConclusionsThis study provides practical demonstration of combining big data with artificial intelligence to increase volume of evidence in clinical learning paradigms.

Effect of phase change materials on heat dissipation of a multiple heat source system

Abstract This paper experimentally investigates heat dissipation of a heat pipe with phase change materials (PCMs) cooling in a multiple heat source system. Two heat sources are fixed at one end of the heat pipe. Considering that a heat sink cannot dissipate all the heat generated by two heat sources, various PCMs are used due to a large latent heat. Different materials in a container are wrapped outside of the middle heat pipe to take away the heat from the evaporation section. The experimental tests obtain temperature data of heat source, evaporation section, and energy storage characteristics of PCMs are also determined under constant and dynamic values of the heat source powers. It is found that under this multiple heat source system structure, the phase change material RT35 maintains temperature variations of the evaporation section at a lower temperature and shortens the required time to reach the equilibrium temperature under a heating power of 20 W.

Extracting and Transforming Heterogeneous Data from XML files for Big Data

Digital technology is fast changing in the recent years and with this change, the number of data systems, sources, and formats has also increased exponentially. So the process of extracting data from these multiple source systems and transforming it to suit for various analytics processes is gaining importance at an alarming rate. In order to handle Big Data, the process of transformation is quite challenging, as data generation is a continuous process. In this paper, we extract data from various heterogeneous sources from the web and try to transform it into a form which is vastly used in data warehousing so that it caters to the analytical needs of the machine learning community.

System Level Modelling of Batteries in Spacecraft Power System in Multiple Source and Multiple Load Configuration

AbstractIn present day spacecraft applications, power system uses solar arrays as the primary source of power generation and rechargeable Lithium ion batteries as secondary source of power. In normal orbits, during sunlit period, solar arrays support the power requirement from all the subsystems and payloads along with the charge power requirement of batteries. During eclipse period, entire load should be supported by batteries. In addition to the eclipse support, starting from launch pad to till deployment of solar panels, all loads will be on batteries. Contingency loads should also be taken care by batteries. So, battery sizing is very critical part in designing the power system of spacecraft. In multiple source and multiple load configuration where two or more batteries are connected to same Spacecraft power bus, current sharing between the batteries is expected, which needs to be considered for optimized sizing and designing of batteries. The scenario becomes complex with additional constraints namely some loads can come on one battery and not on others or vice versa. Even in a configuration where potential difference exists between batteries, current sharing will start as soon as this difference vanishes. A system level simulation model is developed, using network equations, to analyze in‐circuit performance of batteries in multiple source and multiple load power system. The simulation model uses actual current sharing and predicts State of Charge (SOC) of each battery from which Open Circuit Voltage (OCV) is derived. This OCV of batteries is used periodically for finding actual current sharing which runs in a loop for analyzing battery performance in multisource system. Currently a configuration in which two batteries are connected to two buses, which are paralleled and having different loads, is simulated. Optimized battery sizing, actual current sharing and SOC of each battery are derived. This model can also be extended to a system where more than two batteries are present. Two different approaches are used for simulation and results are compared. The developed generic model has features to use in multiple battery configuration.

PNS404 USING DATA WAREHOUSES TO OPTIMIZE HEALTHCARE DECISION MAKING

In order to support rational decisions in the Austrian health care system, decision makers require real-time information of high quality in a user-friendly form. Commonly, the sources of information are distributed across various old-fashioned database systems which are approaching their technical limits in terms of storage and processing efficiency. To address future challenges and achieve consolidation of different source systems, a modern concept of data warehouse (DWH) architecture is being implemented. The DWH architecture follows the paradigms of Data Vault. Firstly, data from multiple source systems are loaded daily into the DWH to keep track of all changes. Then, a full history of data changes is established and stored in a flexible linked set of normalized tables. Subsequently, data are transformed into information by applying business rules. Finally, the information is put into context and presented in dashbords at different levels of granularity and from various perspectives. Successful DWH-applications in the context of rehabilitation are as follows: 1) Rehabilitation planning: A Rehab Map that provides insights about existing and proposed rehabilitation facilities. The benefit of the map lies in collecting information from three different data sources: rehabilitation, geospatial, and population data. 2) Rehabilitation Utilization Monitoring: An interactive dashboard for assessing ongoing rehabilitations. Results can be evaluated e.g. by scrutinizing specific aspects such as diagnosis, age or facilities. The information is provided by the DWH automatically on a monthly basis.3) Long-term Rehabilitation Effectiveness Monitoring: A platform for effectively auditing the success of completed rehabilitation measures. This makes use of the capacity of the DWH to supply a historization of relevant information both before and after the rehabilitation treatment. DWH has been integrated in order to overcome limitations of old-fashioned systems. This in turn allows management better to plan, monitor, and evaluate their decisions with improved speed, efficiency and information quality.

Combining a Big Data Analytics Resource System with Artificial Intelligence Algorithms to Identify Clinically Actionable Radiation Dose Thresholds for Dysphagia in Head and Neck Patients

We combined clinical practice changes and standardizations with informatics technologies to automate aggregation, integration and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). This system supports the development of artificial intelligence (AI) algorithms for clinically actionable analytics to guide decision frameworks. Our purpose was to combine the BDARS with AI to identify additional default planning constraints from our historic data to further reduce dysphagia risk. The BDARS harmonized data for over 22,000 patients, from which we identified 135 of 496 recent patients treated for head and neck cancer demonstrating physician graded dysphagia toxicity scores worsening from baseline to a maximum ≥ 2. Our method used both physical and biologically corrected (α/β =2.5) DVH curves from the BDARS to study both absolute and percentage volume-based DVH metrics for evidence for predicting dysphagia. The method combined a statistical categorization algorithm (SCA) with machine learning (ML) to identify DVH metrics with stronger mutual evidence and more extensive detailing of evidence than either approach alone. SCA-ML ranking was used to cull the large set of DVH metric candidates to a reduced set for input to a predictive ML model (PMLM), reducing risk of overfitting. The PMLM was iteratively constructed, minimizing the number of DVH metric inputs according to their ability to improve the model sensitivity. Seven swallowing structures producing 738 candidate DVH metrics were examined with the SCA-ML. Structures included superior constrictor muscle (SCM), inferior constrictor muscle (ICM), larynx (L), esophagus (E), ipsilateral and contra lateral parotids (IP, CP) and submandibular glands (ISG, CSG). Structures not contoured on at least 90% of the plans were noted for evidence but excluded from the predictive model. High SCA-ML scores were identified for SCM: D20%[EQD2Gy] ≤47.7, D25%[Gy]≤50.4, ISG: D35%[Gy]≤61.7 and E: D2cc [Gy]≤22.6.ICM:D55%[EQD2Gy]≤10.8 had a low SCA-ML score. High SCA-ML scores were obtained for L: D25%[Gy] ≤21.2 and CSG: D45%[Gy]≤28.8 but were excluded in the model due to lacking contours. Sensitivity (0.88 ±0.13) and AUC (0.74 ± 0.17) of the baseline PMLM was not significantly (p>0.05) different from the PMLM using SCM: D20%[EQD2Gy] and ISG: D35%[Gy], with SCM: D20%[EQD2Gy] being the dominant factor. Using the physical dose metric SCM: D25%[Gy] instead of the bio-corrected alternative did not significantly degrade the model. The SCA-ML based AI approach combined with the BDARS identified SCM:D25% [Gy] ≤50.4 as an additional mid priority metric, that together with historic constraints ICM:Mean [Gy] ≤20, L:Mean [Gy] ≤20, and SCM:Mean [Gy] ≤50 could improve outcomes. This study provided practical demonstration of how big data evidence combined with AI could advance iterative clinical learning paradigms.

Reflections on Custom Critique and on Functional Equivalents in the Work of Jean d’Aspremont

Abstract The present article engages with the custom critique and functional equivalents in the work of Jean d’Aspremont. It reflects on the emphasis given to the distinction between ascertainment and content-determination. If one engages in the politics of law-ascertainment, or the politics of sources and their evaluation, it is submitted here to adopt a contextualized perspective that includes content-determination as well. The article will conclude by pointing out three examples that merit ongoing attention in order to evaluate the distribution of normativity within a system of multiple sources.