Data acquisition in process industries usually takes place at each sampling. The disadvantage is that a considerable amount of data without new information about the state of the process is continuously transmitted and processed. This negatively affects the communication system and computational power, which is more critical nowadays given the number of variables measured, even in seconds. One solution concerns the event-driven paradigm, in which only relevant data according to a pre-defined criterion is forwarded for further processing. This work investigated the event-based threshold and delta methods in the context of fault detection. The data transmission rate was also analyzed. The well-know Tennessee Eastman problem (TEP) was used as a case study. The fault detection system was based on PCA (principal component analysis), which is widely used for this purpose in this benchmark. The results were compared with the commonly used time-based approach, for a fixed false alarm rate. The threshold rule provided similar results, but with much less data. For the delta rule, significant MDR (missed detection rate) gains of up to 74% were obtained for five of the six hard-to-detect faults, and of up to 69%, for two of the three very hard-to-detect faults. MDR values very close to zero were reached for two of the three intermediate detection faults and two of the hard-to-detect faults. The detection time was also evaluated. In this regard, considerably lower values were obtained for all intermediate detection faults, three of the hard-to-detect faults and all very hard-to-detect faults. In short, the delta method was able to improve fault detection performance, especially for hard-to-detect faults, with a considerably lower data transmission rate, around 20% on average. Event-driven data acquisition can be very attractive for process industries.

Magnetic nanoparticles (MNPs) are widely used to facilitate magnetic particle imaging (MPI) which has the potential to become the leading diagnostic instrument for biomedical imaging. This comparative study assesses the effects of changing iron content and excitation frequency on point-spread function (PSF) representing the effect of magnetization reversal. PSF is quantified by features of interest for MPI: i.e. gradient amplitude and full-width-at-half-maximum (FWHM). A superparamagnetic quantifier (SPaQ) is used to assess differential magnetic susceptibility of two commercially available MNPs: Synomag ® -D50 and Synomag ® -D70. For both MNPs, the signal output depends on increase in drive field frequency and amount of iron oxide, which might be hampering the sensitivity of MPI systems that perform on higher frequencies. Nevertheless, there is a clear potential of Synomag ® -D for a stable MPI resolution, especially in case of 70 nm version, that is independent of either drive field frequency or amount of iron oxide.

—Longitudinal research is fundamentally important for understanding sustainable behavior change related to human health. This is particularly true for chronic illnesses, such as dementia and mental illness, where treatment is often measured in months and years over the long-term. For research on human-robot interaction (HRI) geared towards addressing such health-related issues, this presents a challenge in that many HRI studies (particularly in lab settings) are performed in a matter of minutes or at most hours. Given that reality, this suggests a need for innovative approaches to longitudinal research in HRI for such health-focused scenarios. Here, we argue that such methodological approaches to longitudinal HRI research should be grounded in existing models of behavior change (e.g. transtheoretical model) and leveraged via novel technology-enabled real-time sampling strategies for user activities outside of lab settings (e.g. ecological momentary assessment, EMA), all geared toward supporting data-driven methods for connecting robot behavior and human behavior over the long-term in in-the-wild settings.

In this letter, we study the trade-off between exploration and exploitation for linear quadratic adaptive control. This trade-off can be expressed as a function of the exploration and exploitation costs, called cumulative regret. It has been shown over the years that the optimal asymptotic rate of the cumulative regret is in many instances $\mathcal {O}(\sqrt {T})$ . In particular, this rate can be obtained by adding a white noise external excitation, with a variance decaying as $\mathcal {O}(1/\sqrt {T})$ . As the amount of excitation is pre-determined, such approaches can be viewed as open loop control of the external excitation. In this contribution, we approach the problem of designing the external excitation from a feedback perspective leveraging the well known benefits of feedback control for decreasing sensitivity to external disturbances and system-model mismatch, as compared to open loop strategies. We base the feedback on the Fisher information matrix which is a measure of the accuracy of the model. Specifically, the amplitude of the exploration signal is seen as the control input while the minimum eigenvalue of the Fisher matrix is the variable to be controlled. We call such exploration strategies Fisher Feedback Exploration (F2E). We propose one explicit F2E design, called Inverse Fisher Feedback Exploration (IF2E), and argue that this design guarantees the optimal asymptotic rate for the cumulative regret. We provide theoretical support for IF2E and in a numerical example we illustrate benefits of IF2E and compare it with the open loop approach as well as a method based on Thompson sampling.

Figure 1. Our method obtains a high-quality 3D reconstruction from an RGB-D input sequence by training a multi-layer perceptron. In comparison to state-of-the-art methods like BundleFusion [3] or the theoretical NeRF [9] with additional depth constraints, our approach results in cleaner and more complete reconstructions. As can be seen, the pose optimization of our approach is key to resolving misalignment artifacts.

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: red algae Lemanea rigida and Paralemanea torulosa, mycorrhizal fungi Amanita simulans and Terfezia pseudoleptoderma, parasitic fungus Microbotryum vinosum, saprotrophic fungus Sarcoscypha jurana, stonewort Chara tenuispina, mosses Brachytheciastrum collinum and Meesia longiseta, monocots Dactylorhiza romana and Neotinea maculata and dicots Adenophora liliifolia, Ambrosia artemisiifolia and Tanacetum corymbosum subsp. cinereum are given within SE Europe and adjacent regions.

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: red algae Lemanea fucina and Paralemanea annulata, parasitic fungus Anthracoidea pratensis, saprotrophic fungi Cyathus olla, Massaria campestris, and Xylaria sicula, stonewort Chara canescens, liverworts Gymnomitrion commutatum and Porella baueri, moss Acaulon triquetrum, monocots Anacamptis laxiflora, Cephalanthera damasonium, and Himantoglossum robertianum and dicot Jacobaea othonnae are given within SE Europe and adjacent regions.