Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative interpretation of atmospheric carbon records over the last glacial termination

The glacial/interglacial rise in atmospheric pCO2 is one of the best known changes in paleoclimate research, yet the cause for it is still unknown. Forcing the coupled ocean‐atmosphere‐biosphere box model of the global carbon cycle BICYCLE with proxy data over the last glacial termination, we are able to quantitatively reproduce transient variations in pCO2 and its isotopic signatures (δ13C, Δ14C) observed in natural climate archives. The sensitivity of the Box model of the Isotopic Carbon cYCLE (BICYCLE) to high or low latitudinal changes is comparable to other multibox models or more complex ocean carbon cycle models, respectively. The processes considered here ranked by their contribution to the glacial/interglacial rise in pCO2 in decreasing order are: the rise in Southern Ocean vertical mixing rates (>30 ppmv), decreases in alkalinity and carbon inventories (>30 ppmv), the reduction of the biological pump (∼20 ppmv), the rise in ocean temperatures (15–20 ppmv), the resumption of ocean circulation (15–20 ppmv), and coral reef growth (<5 ppmv). The regrowth of the terrestrial biosphere, sea level rise and the increase in gas exchange through reduced sea ice cover operate in the opposite direction, decreasing pCO2 during Termination I by ∼30 ppmv. According to our model the sequence of events during Termination I might have been the following: a reduction of aeolian iron fertilization in the Southern Ocean together with a breakdown in Southern Ocean stratification, the latter caused by rapid sea ice retreat, trigger the onset of the pCO2 increase. After these events the reduced North Atlantic Deep Water (NADW) formation during the Heinrich 1 event and the subsequent resumption of ocean circulation at the beginning of the Bølling‐Allerød warm interval are the main processes determining the atmospheric carbon records in the subsequent time period of Termination I. We further deduce that a complete shutdown of the NADW formation during the Younger Dryas was very unlikely. Changes in ocean temperature and the terrestrial carbon storage are the dominant processes explaining atmospheric δ13C after the Bølling‐Allerød warm interval.
Hydronium-Ion Batteries with Perylenetetracarboxylic Dianhydride Crystals as an Electrode.

We demonstrate for the first time that hydronium ions can be reversibly stored in an electrode of crystalline 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA). PTCDA exhibits a capacity of 85 mAh g-1 at 1 A g-1 after an initial conditioning process. Ex situ X-ray diffraction revealed reversible and significant structure dilation upon reduction of PTCDA in an acidic electrolyte, which can only be ascribed to hydronium-ion intercalation. The lattice expansion upon hydronium storage was theoretically explored by first-principles density functional theory (DFT) calculations, which confirmed the hydronium storage in PTCDA.
Energy-aware routing protocol for mobile sensor networks using learning automata algorithms

This paper proposes an energy-aware location-based routing protocol for mobile sensor networks that consist of frequently moving sensors. Our proposed protocol uses learning automata to select best routes that maximize delivery ratio and network lifetime. The protocol uses the location and remaining energy information of sensors to assign a cost function to each sensor node. Each node in network is equipped with a learning automaton which selects least-cost paths for each packet. Simulation results show that the proposed method achieves higher delivery ratio, lower routing overhead and lower energy consumption.
A pragmatic approach to area coverage in hybrid wireless sensor networks

Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. This paper seeks to address the problem of determining the current coverage achieved by the non-deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. We identify three key elements that are critical for ensuring effective area coverage in Hybrid WSN: (i) determining the boundary of the target region and evaluating the area coverage (ii) locating coverage holes and maneuvering mobile nodes to fill these voids, and (iii) maintaining the desired coverage over the entire operational lifetime of the network. We propose a comprehensive solution that addresses all of the aforementioned aspects of the area coverage, called MAPC (mobility assisted probabilistic coverage). MAPC is a distributed protocol that operates in three distinct phases. The first phase identifies the boundary nodes using the geometric right-hand rule. Next, the static nodes calculate the area coverage and identify coverage holes using a novel probabilistic coverage algorithm (PCA). PCA incorporates realistic sensing coverage model for range-based sensors. The second phase of MAPC is responsible for navigating the mobile nodes to plug the coverage holes. We propose a set of coverage and energy-aware variants of the basic virtual force algorithm (VFA). Finally, the third phase addresses the problem of coverage loss due to faulty and energy depleted nodes. We formulate this problem as an Integer Linear Program (ILP) and propose practical heuristic solutions that achieve similar performance as that of the optimal ILP solution. A guiding principle in our design process has been to ensure that the MAPC can be readily implemented in real-world applications. We implemented the boundary detection and PCA algorithm (i.e., Phase I) of the MAPC protocol on off-the-shelf sensor nodes and results show that the MAPC can successfully identify boundary nodes and accurately determine the area coverage in the presence of real radio irregularities observed during the experiments. Extensive simulations were carried out to evaluate the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage, while reducing the total energy consumption by up to 70% as compared with the basic VFA. Copyright © 2010 John Wiley & Sons, Ltd. The results in this paper have been presented in part at the Workshop on Wireless Local Networks (WLN, 2005), Mobile Ad-hoc and Sensor Systems (MASS, 2006), and Mobile Ad-hoc and Sensor Networks (MSN, 2007).
Exogenous Brain-Derived Neurotrophic Factor Rescues Synaptic Dysfunction in Mecp2-Null Mice

Postnatal deficits in brain-derived neurotrophic factor (BDNF) are thought to contribute to pathogenesis of Rett syndrome (RTT), a progressive neurodevelopmental disorder caused by mutations in the gene encoding methyl-CpG-binding protein 2 (MeCP2). In Mecp2-null mice, a model of RTT, BDNF deficits are most pronounced in structures important for autonomic and respiratory control, functions that are severely affected in RTT patients. However, relatively little is known about how these deficits affect neuronal function or how they may be linked to specific RTT endophenotypes. To approach these issues, we analyzed synaptic function in the brainstem nucleus tractus solitarius (nTS), the principal site for integration of primary visceral afferent inputs to central autonomic pathways and a region in which we found markedly reduced levels of BDNF in Mecp2 mutants. Our results demonstrate that the amplitude of spontaneous miniature and evoked EPSCs in nTS neurons is significantly increased in Mecp2-null mice and, accordingly, that mutant cells are more likely than wild- type cells to fire action potentials in response to primary afferent stimulation. These changes occur without any increase in intrinsic neuronal excitability and are unaffected by blockade of inhibitory GABA currents. However, this synaptopathy is associated with decreased BDNF availability in the primary afferent pathway and can be rescued by application of exogenous BDNF. On the basis of these findings, we hypothesize that altered sensory gating in nTS contributes to cardiorespiratory instability in RTT and that nTS is a site at which restoration of normal BDNF signaling could help reestablish normal homeostatic controls.
Inflammation Caused by Praziquantel Treatment Depends on the Location of the Taenia solium Cysticercus in Porcine Neurocysticercosis

Background Neurocysticercosis (NCC), infection of the central nervous system by Taenia solium cysticerci, is a pleomorphic disease. Inflammation around cysticerci is the major cause of disease but is variably present. One factor modulating the inflammatory responses may be the location and characteristics of the brain tissue adjacent to cysticerci. We analyzed and compared the inflammatory responses to cysticerci located in the parenchyma to those in the meninges or cysticerci partially in contact with both the parenchyma and the meninges (corticomeningeal). Methodology/Principal Findings Histological specimens of brain cysticerci (n = 196) from 11 pigs naturally infected with Taenia solium cysticerci were used. Four pigs were sacrificed after 2 days and four after 5 days of a single dose of praziquantel; 3 pigs did not receive treatment. All pigs were intravenously injected with Evans Blue to assess disruption of the blood-brain barrier. The degree of inflammation was estimated by use of a histological score (ISC) based on the extent of the inflammation in the pericystic areas as assessed in an image composed of several photomicrographs taken at 40X amplification. Parenchymal cysticerci provoked a significantly greater level of pericystic inflammation (higher ISC) after antiparasitic treatment compared to meningeal and corticomeningeal cysticerci. ISC of meningeal cysticerci was not significantly affected by treatment. In corticomeningeal cysticerci, the increase in ISC score was correlated to the extent of the cysticercus adjacent to the brain parenchyma. Disruption of the blood-brain barrier was associated with treatment only in parenchymal tissue. Significance Inflammatory response to cysticerci located in the meninges was significantly decreased compared to parenchymal cysticerci. The suboptimal inflammatory response to cysticidal drugs may be the reason subarachnoid NCC is generally refractory to treatment compared to parenchymal NCC.
Content-based routing in highly dynamic mobile ad hoc networks

The decoupling and asynchrony properties of the content‐based publish‐subscribe paradigm makes it very appealing for dynamic wireless networks, like those that often occur in pervasive computing scenarios. Unfortunately, most of the currently available content‐based publish‐subscribe middleware do not fit the requirements of such extreme scenarios, in which the network is subject to very frequent topological reconfigurations due to mobility of nodes. In this paper we propose a protocol for content‐based message dissemination tailored to Mobile Ad Hoc Networks (MANETs) showing frequent topological changes. Message routing occurs without the support of any network‐wide dispatching infrastructure thus eliminating the need of maintaining such infrastructure on top of a physical network continuously changing its topology. The paper reports an extensive simulation study that confirms the suitability of the proposed approach along with a stochastic analysis of the central mechanism adopted by the protocol.
Statistical Structure Analysis in MRI Brain Tumor Segmentation

Automated MRI (Magnetic Resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this paper, a statistical structure analysis based tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. Firstly, 3 kinds of features including intensity-based, symmetry-based and texture-based are extracted from structural elements. Then a classification technique using AdaBoost that learns by selecting the most discriminative features is proposed to classify the structural elements into normal tissues and abnormal tissues. Experimental results on 140 tumor-contained brain MR images achieve an average accuracy of 96.82% on tumor segmentation.
Regularisation Using Spatiotemporal Independence and Predictability

Independent component analysis (ICA) of an image sequence extracts a set of statistically independent images, and deenes a corresponding set of unconstrained dual time courses. However, the extra degrees of freedom implicit in these time courses can lead to physically improbable solutions. Accordingly, we introduce two methods for regularising ICA: smoothed independent component analysis (smICA), and spatiotemporal ICA (stICA). smICA is based on the observation that temporal signals tend to vary smoothly, and stICA is based on the observation that images and time courses tend to be statistically independent. A key feature of these methods is that they are based on generic physical properties , and may therefore be widely applicable. The methods are demonstrated on synthetic fMRI images.
Linear programing formulations and algorithms for radiotherapy treatment planning

Optimization has become an important tool in treatment planning for cancer radiation therapy. It may be used to determine beam weights, beam directions, and appropriate use of beam modifiers such as wedges and blocks, with the aim of delivering a required dose to the tumor, while sparing nearby critical structures and normal tissue. Linear programing formulations are a core computation in many approaches to treatment planning, because of the abundance of highly developed linear programing software. However, these formulations of treatment planning often require a surprisingly large amount of time to solve—more than might be anticipated given the dimensions of the problems. Moreover, the choices of formulation, algorithm, and pivot rule that perform best from a computational viewpoint are sometimes not obvious, and the software's default choices are sometimes poor. This article considers several linear programing formulations of treatment planning problem and tests several variants of simplex and interior-point methods for solving them. Conclusions are drawn about the most effective formulations and variants.
Model-based sparse component analysis for reverberant speech localization

In this paper, the problem of multiple speaker localization via speech separation based on model-based sparse recovery is studies. We compare and contrast computational sparse optimization methods incorporating harmonicity and block structures as well as autoregressive dependencies underlying spectrographic representation of speech signals. The results demonstrate the effectiveness of block sparse Bayesian learning framework incorporating autoregressive correlations to achieve a highly accurate localization performance. Furthermore, significant improvement is obtained using ad-hoc microphones for data acquisition set-up compared to the compact microphone array.
Efficient Assembly of H(div) and H(curl) Conforming Finite Elements

In this paper, we discuss how to efficiently evaluate and assemble general finite element variational forms on H(div) and H(curl). The proposed strategy relies on a decomposition of the element tensor into a precomputable reference tensor and a mesh-dependent geometry tensor. Two key points must then be considered: the appropriate mapping of basis functions from a reference element, and the orientation of geometrical entities. To address these issues, we extend here a previously presented representation theorem for affinely mapped elements to Piola-mapped elements. We also discuss a simple numbering strategy that removes the need to contend with directions of facet normals and tangents. The result is an automated, efficient, and easy-to-use implementation that allows a user to specify finite element variational forms on H(div) and H(curl) in close to mathematical notation.
Type-based amortized resource analysis with integers and arrays*

Abstract Proving bounds on the resource consumption of a program by statically analyzing its source code is an important and well-studied problem. Automatic approaches for numeric programs with side effects usually apply abstract interpretation-based invariant generation to derive bounds on loops and recursion depths of function calls. This article presents an alternative approach to resource-bound analysis for numeric and heap-manipulating programs that uses type-based amortized resource analysis. As a first step towards the analysis of imperative code, the technique is developed for a first-order ML-like language with unsigned integers and arrays. The analysis automatically derives bounds that are multivariate polynomials in the numbers and the lengths of the arrays in the input. Experiments with example programs demonstrate two main advantages of amortized analysis over current abstract interpretation–based techniques. For one thing, amortized analysis can handle programs with non-linear intermediate values like f((n + m)2). For another thing, amortized analysis is compositional and works naturally for compound programs like f(g(x)).
Project Management Mechanism: Mediates the Influence of Business Strategy on Corporate Performance

According to the strategy-implementation theory based on project management, strategy could be implemented by project management mechanism. However, no empirical analysis has ever examined how project management mechanism acts as a mediator between business strategy and corporate performance. This study introduces three project management mechanisms such as project management office, program management, and gate review to be the tools to implement strategy, and then tests the significant of project management mechanisms as mediators between business strategy and enterprises performance. The results show that the mediate effects of three project management mechanisms between the low-cost strategy and corporate performance are significant, so is the mediate effect of project management office between the differentiation strategy and corporate performance. It is verified that the project management mechanisms, in varying degrees, can be ways to implement the strategy.
Intravitreal Administration of HA-1077, a ROCK Inhibitor, Improves Retinal Function in a Mouse Model of Huntington Disease

Huntington disease (HD) is an inherited neurodegenerative disease that affects multiple brain regions. It is caused by an expanded polyglutamine tract in huntingtin (Htt). The development of therapies for HD and other neurodegenerative diseases has been hampered by multiple factors, including the lack of clear therapeutic targets, and the cost and complexity of testing lead compounds in vivo. The R6/2 HD mouse model is widely used for pre-clinical trials because of its progressive and robust neural dysfunction, which includes retinal degeneration. Profilin-1 is a Htt binding protein that inhibits Htt aggregation. Its binding to Htt is regulated by the rho-associated kinase (ROCK), which phosphorylates profilin at Ser-137. ROCK is thus a therapeutic target in HD. The ROCK inhibitor Y-27632 reduces Htt toxicity in fly and mouse models. Here we characterized the progressive retinopathy of R6/2 mice between 6–19 weeks of age to determine an optimal treatment window. We then tested a clinically approved ROCK inhibitor, HA-1077, administered intravitreally via liposome-mediated drug delivery. HA-1077 increased photopic and flicker ERG response amplitudes in R6/2 mice, but not in wild-type littermate controls. By targeting ROCK with a new inhibitor, and testing its effects in a novel in vivo model, these results validate the in vivo efficacy of a therapeutic candidate, and establish the feasibility of using the retina as a readout for CNS function in models of neurodegenerative disease.
Uncertainty in perception and the Hierarchical Gaussian Filter

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder–Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient—but at the same time intuitive—framework for the resolution of perceptual uncertainty in behaving agents.
Using a trust network to improve top-N recommendation

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended for top-N recommendation, but this approach does not work accurately for cold start users that have rated only a very small number of items. In this paper we propose novel methods exploiting a trust network to improve the quality of top-N recommendation. The first method performs a random walk on the trust network, considering the similarity of users in its termination condition. The second method combines the collaborative filtering and trust-based approach. Our experimental evaluation on the Epinions dataset demonstrates that approaches using a trust network clearly outperform the collaborative filtering approach in terms of recall, in particular for cold start users.
PRAKTYKOPLEIERS SE PERSEPSIES VAN MAATSKAPLIKE¬WERK-PRAKTYKOPLEIDING

The Bachelor of Social Work (BSW) degree was developed to equip students with the knowledge and skills to address socio-economic issues from a developmental paradigm. F ield education reflects the reality of the profession and the context in which it is utilised. Hence, field education is viewed as an integral and valuable part of the education and preparation of students for practice. The research objective of this study was to undertake a qualitative exploratory and descriptive study of the perceptions of field instructors in terms of the current state of social work practice training. The data was collected through semi-structured interviews with field instructors