The irreversibility line Bm(T) of monocrystalline superconducting Bi2Sr2CaCu208 has been measured in magnetic fields H parallel to the c axis of the crystal. For low inductions B near Tcy we find a parabolic temperature dependence, Bm^ BQ{TC/T~\)^, while for larger inductions, Bm grows exponentially with T~^. We argue that the two regimes reflect the three-and the quasi-two-dimensional character of the respective vortex fluctuations. In both regimes, a Lindemann-type melting criterion yields quantitative expressions for BmiT) which reproduce the experimental data very well.PACS numbers: 74.60. Ge, 74.72.Hs Numerous experiments probing the mixed state of cuprate superconductors have established the presence of a boundary in the magnetic phase diagram, which separates a magnetically irreversible, zero-resistance state from a reversible state with dissipative electrical transport properties [1][2][3][4][5]. This boundary has been suggested to be due to either depinning [2,6], to a vortex-glass formation [7], or to flux-lattice melting [8]. It has been recently suggested that the temperature dependence of the irreversibility field Hirr(T) obeys a scaling relation universal for all high-T^ superconductors [5]. Although such a scaling may hold within one class of compounds (e.g., Pr-substituted or oxygen-depleted 9]), it is unlikely that it is also valid for more anisotropic compounds such as Bi2Sr2CaCu208 [10]. There, a crossover from essentially three-dimensional to quasitwo-dimensional vortex fluctuations is predicted to occur at ^cr^40oA^7^ [10,11], where s is the interlayer distance, Y^Xc/Xab is the anisotropy parameter, and 0o is the magnetic flux quantum. At inductions B<^Bcr, the vortices are expected to form an ensemble of vortex strings with 3D-like fluctuations. For B^Bcr, the interaction between vortex objects within one particular layer is stronger than the coupling between these 2D objects belonging to adjacent layers. This leads to a quasi-2D behavior of the thermal vortex fluctuations. Our experimental results on the irreversibility boundary BmiT) presented below strongly suggest that such a dimensional crossover occurs indeed in Bi2Sr2CaCu208.The experiments were performed on a high-quality single crystal of Bi2Sr2CaCu208, prepared from the melt in a temperature gradient. In an external dc field of ^=4 Oe, diamagnetism was observed to sharply develop below rc~89.7 K. After demagnetization corrections, the field-cooling susceptibility is 87% of -1/4TT. For such corrections, the demagnetization factor TV of the crystal had been previously determined by measuring the apparent zero-field cooling susceptibility ;t at r = 6 K in small external fields, //< 1 Oe. Assuming complete magnetic screening, i.e., ;^eff^ ~" l/4;r, we obtained TV =0.965, which is consistent with a corresponding esti-mate from an ellipsoid approximation for the shape of the crystal. The irreversibility boundary T\rv^B) was measured using a quasistatic technique described in Ref. [4]. The method is based on the measurement of the redu...

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Bioelectrochemical systems (BES) are promising technologies to convert organic compounds in wastewater to electrical energy through a series of complex physical-chemical, biological and electrochemical processes. Representative BES such as microbial fuel cells (MFCs) have been studied and advanced for energy recovery. Substantial experimental and modeling efforts have been made for investigating the processes involved in electricity generation toward the improvement of the BES performance for practical applications. However, there are many parameters that will potentially affect these processes, thereby making the optimization of system performance hard to be achieved. Mathematical models, including engineering models and statistical models, are powerful tools to help understand the interactions among the parameters in BES and perform optimization of BES configuration/operation. This review paper aims to introduce and discuss the recent developments of BES modeling from engineering and statistical aspects, including analysis on the model structure, description of application cases and sensitivity analysis of various parameters. It is expected to serves as a compass for integrating the engineering and statistical modeling strategies to improve model accuracy for BES development.

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We propose to use segment graph convolutional and recurrent neural networks (Seg-GCRNs), which use only word embedding and sentence syntactic dependencies, to classify relations from clinical notes without manual feature engineering. In this study, the relations between 2 medical concepts are classified by simultaneously learning representations of text segments in the context of sentence syntactic dependency: preceding, concept1, middle, concept2, and succeeding segments. Seg-GCRN was systematically evaluated on the i2b2/VA relation classification challenge datasets. Experiments show that Seg-GCRN attains state-of-the-art micro-averaged F-measure for all 3 relation categories: 0.692 for classifying medical treatment–problem relations, 0.827 for medical test–problem relations, and 0.741 for medical problem–medical problem relations. Comparison with the previous state-of-the-art segment convolutional neural network (Seg-CNN) suggests that adding syntactic dependency information helps refine medical word embedding and improves concept relation classification without manual feature engineering. Seg-GCRN can be trained efficiently for the i2b2/VA dataset on a GPU platform.

A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs. Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive ManufacturingJingran Li GENERAL AUDIENCE ABSTRACT This paper aims to achieve the layer to layer temperature monitoring and consequently predict the temperature distribution for any new freeform geometry. An engineering statistical synergistic model is proposed to integrate the pure statistical methods and finite element modeling (FEM), which is physically meaningful as well as accurate for temperature prediction. Besides, this proposed synergistic model contains geometry information, which can be applied to any freeform geometry. This paper serves to enable a holistic cyber physical systems-based approach for the additive manufacturing (AM) not only restricted in fused deposition modeling (FDM) process but also can be extended to powder-based process like laser engineered net shaping (LENS) and selective laser sintering (SLS). This paper as well as the scheduled future works will make it affordable for customized AM including customized geometries and materials, which will greatly accelerate the transition from rapid prototyping to rapid manufacturing. This article demonstrates a first evaluation of engineering statistical synergistic model in AM technology, which gives a perspective on future researches about online quality monitoring and control of AM based data fusion principles. iv ACKNOWLEDGEMENTS

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