Microstructure and bonding behavior of fiber-mortar interface in fiber-reinforced concrete

The interfacial properties between fiber and matrix play a critical role in the overall mechanical responses of composite materials. In this paper, the glass fiber-mortar interfacial microstructure in fiber reinforced concrete (FRC) is visualized and characterized using X-ray microscopy. Additionally, three types of fiber-mortar interface (glass fiber, high modulus polyvinyl alcohol (PVA) fiber, and basalt fiber) are analyzed by scanning electron microscopy and energy dispersive X-ray spectroscopy. The results revealed a lot of microcracks along with the glass fiber-mortar interface; moreover, the hydration product of the glass/PVA/basalt fiber-mortar interface was much lower than that of the mortar matrix. Because microcracks or lower hydration product have such a negative effect on the interfacial bonding between fiber and mortar, the objective of this paper was to provide an analysis of this problem through extensive testing of their bonding properties. Specimens made of three types of fiber were tested along with three different mortar types under tensile stress and a combined stress state to investigate the interfacial bond properties between fiber and mortar. Results show that both of the tensile and shear bond strength of the interface were not only improved by stronger mortar matrix, but also significantly affected by fiber type. Furthermore, when the interface failed by slipping along the interfacial area, the interface showed an increasing shear bond strength with the increase of compressive stress. This was not the case when failure was due to the crushing of mortar. Finally, the FRC splitting tensile strength was tested to demonstrate the bonding mechanism effects on the FRC mechanical properties

Cortico-cerebellar interactions during goal-directed behavior

Preparatory activity is observed across multiple interconnected brain regions before goal-directed movement. Preparatory activity reflects discrete activity states representing specific future actions. It is unclear how this activity is mediated by multi-regional interactions. Recent evidence suggests that the cerebellum, classically associated with fine motor control, contributes to preparatory activity in the neocortex. We review recent advances and offer perspective on the function of cortico-cerebellar interactions during goal-directed behavior. We propose that the cerebellum learns to facilitate transitions between neocortical activity states. Transitions between activity states enable flexible and appropriately timed behavioral responses

Historical costume simulation

The aim of this study is to produce accurate reproductions of digital clothing from historical sources and to investigate the implications of developing it for online museum exhibits. In order to achieve this, the study is going through several stages. Firstly, the theoretical background of the main issues will be established through the review of various published papers on 3D apparel CAD, drape and digital curation. Next, using a 3D apparel CAD system, this study attempts the realistic visualization of the costumes based on the establishment of a valid simulation reference. This paper reports the pilot exercise carried out to scope the requirements for going forward

Deep Gaussian processes for regression using approximate expectation propagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. This paper develops a new approximate Bayesian learning scheme that enables DGPs to be applied to a range of medium to large scale regression problems for the first time. The new method uses an approximate Expectation Propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning. We evaluate the new method for non-linear regression on eleven real-world datasets, showing that it always outperforms GP regression and is almost always better than state-of-the-art deterministic and sampling-based approximate inference methods for Bayesian neural networks. As a by-product, this work provides a comprehensive analysis of six approximate Bayesian methods for training neural networks

Black-Box α-divergence minimization

Black-box alpha (BB-α) is a new approximate inference method based on the minimization of α-divergences. BB-α scales to large datasets because it can be implemented using stochastic gradient descent. BB-α can be applied to complex probabilistic models with little effort since it only requires as input the likelihood function and its gradients. These gradients can be easily obtained using automatic differentiation. By changing the divergence parameter α, the method is able to interpolate between variational Bayes (VB) (α → 0) and an algorithm similar to expectation propagation (EP) (α = 1). Experiments on probit regression and neural network regression and classification problems show that BB-a with non-standard settings of α, such as α = 0.5, usually produces better predictions than with α → 0 (VB) or α = 1 (EP).JMHL acknowledges support from the Rafael del Pino Foundation. YL thanks the Schlumberger Foundation Faculty for the Future fellowship on supporting her PhD study. MR acknowledges support from UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L016516/1 for the University of Cambridge Centre for Doctoral Training, the Cambridge Centre for Analysis. TDB thanks Google for funding his European Doctoral Fellowship. DHL acknowledge support from Plan National I+D+i, Grant TIN2013-42351-P and TIN2015- 70308-REDT, and from Comunidad de Madrid, Grant S2013/ICE-2845 CASI-CAM-CM. RET thanks EPSRC grant #EP/L000776/1 and #EP/M026957/1

Duodenal perforation due to a kink in a nasojejunal feeding tube in a patient with severe acute pancreatitis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Nasojejunal feeding tube placement can be achieved by fluoroscopic or endoscopic techniques. Significant complications due to nasojejunal feeding tube placement, such as hydrothorax, duodenal perforation and retroperitoneal emphysema, are very rare. We present a case of massive retroperitoneal emphysema and abscess because of duodenal perforation caused by a kink in a nasojejunal feeding tube.</p> <p>Case presentation</p> <p>A 34-year-old Chinese woman was admitted to our intensive care unit due to hypertriglyceridemia and severe acute pancreatitis. As she suffered from acute respiratory distress syndrome and required mechanical ventilation, a nasojejunal feeding tube was placed by transnasal endoscopic technique. The procedure took place at her bedside. Half a month later, she had a high fever and abdominal distension. An abdominal radiography was performed and showed that the nasojejunal feeding tube was kinking on the third portion of the duodenum and the tip of the nasojejunal feeding tube was inserted into the right retroperitoneum on the second portion of the duodenum.</p> <p>Conclusion</p> <p>When a nasojejunal feeding tube is placed through the transnasal endoscopic technique, an abdominal radiography should be used to confirm the tube's position and indicate if it is kinking or beyond the ligament of Treitz.</p

A possible method for non-Hermitian and non-PTPT-symmetric Hamiltonian systems

A possible method to investigate non-Hermitian Hamiltonians is suggested through finding a Hermitian operator $\eta_+$ and defining the annihilation and creation operators to be $\eta_+$-pseudo-Hermitian adjoint to each other. The operator $\eta_+$ represents the $\eta_+$-pseudo-Hermiticity of Hamiltonians. As an example, a non-Hermitian and non-$PT$-symmetric Hamiltonian with imaginary linear coordinate and linear momentum terms is constructed and analyzed in detail. The operator $\eta_+$ is found, based on which, a real spectrum and a positive-definite inner product, together with the probability explanation of wave functions, the orthogonality of eigenstates, and the unitarity of time evolution, are obtained for the non-Hermitian and non-$PT$-symmetric Hamiltonian. Moreover, this Hamiltonian turns out to be coupled when it is extended to the canonical noncommutative space with noncommutative spatial coordinate operators and noncommutative momentum operators as well. Our method is applicable to the coupled Hamiltonian. Then the first and second order noncommutative corrections of energy levels are calculated, and in particular the reality of energy spectra, the positive-definiteness of inner products, and the related properties (the probability explanation of wave functions, the orthogonality of eigenstates, and the unitarity of time evolution) are found not to be altered by the noncommutativity.Comment: 15 pages, no figures; v2: clarifications added; v3: 16 pages, 1 figure, clarifications made clearer; v4: 19 pages, the main context is completely rewritten; v5: 25 pages, title slightly changed, clarifications added, the final version to appear in PLOS ON

Quantifying the impact of climate change on drought regimes using the Standardised Precipitation Index

The study presents a methodology to characterise short- or long-term drought events, designed to aid understanding of how climate change may affect future risk. An indicator of drought magnitude, combining parameters of duration, spatial extent and intensity, is presented based on the Standardised Precipitation Index (SPI). The SPI is applied to observed (1955–2003) and projected (2003–2050) precipitation data from the Community Integrated Assessment System (CIAS). Potential consequences of climate change on drought regimes in Australia, Brazil, China, Ethiopia, India, Spain, Portugal and the USA are quantified. Uncertainty is assessed by emulating a range of global circulation models to project climate change. Further uncertainty is addressed through the use of a high-emission scenario and a low stabilisation scenario representing a stringent mitigation policy. Climate change was shown to have a larger effect on the duration and magnitude of long-term droughts, and Australia, Brazil, Spain, Portugal and the USA were highlighted as being particularly vulnerable to multi-year drought events, with the potential for drought magnitude to exceed historical experience. The study highlights the characteristics of drought which may be more sensitive under climate change. For example, on average, short-term droughts in the USA do not become more intense but are projected to increase in duration. Importantly, the stringent mitigation scenario had limited effect on drought regimes in the first half of the twenty-first century, showing that adaptation to drought risk will be vital in these regions

A semiparametric joint model for terminal trend of quality of life and survival in palliative care research

Copyright © 2017 John Wiley & Sons, Ltd. Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients’ diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented