Miracle-dz/UniFAR-FTUs huggingface dataset:
The Value of Free Water: Analyzing South Africa's Free Basic Water Policy

This paper analyzes South Africa's Free Basic Water Policy, under which households receive a free water allowance equal to the World Health Organization's recommended minimum. I estimate residential water demand, evaluate the welfare effects of free water, and provide optimal price schedules derived from a social planner's problem. I use a data set of monthly metered billing data for 60,000 households for 2002–2009 from a particularly disadvantaged suburb of Pretoria, with rich price variation across 20 different nonlinear tariff schedules. I find that the free allowance acts as a lump‐sum subsidy, without large effects on water consumption. However, it is possible to reallocate the current subsidy to form an optimal tariff without a free allowance, which would increase welfare while leaving the water provider's profit unchanged. This optimal tariff would also reduce the number of households consuming low quantities of water, a desirable policy goal according to the WHO.
Study on resonant tunneling diode

We designed and fabricated an AlAs/GaAs/InGaAs/GaAs/AlAs Resonant Tunneling Diode (RTD) with two types of epitaxial structures grown by Molecular Beam Epitaxy (MBE). The devices have shown excellent I-V characteristics with peak-valley current ratio (PVCR) 5:1 at room temperature and a high peak current density. The DC and AC characterizations are reported and analyzed.
Two-Sided Learning and Moral Hazard

I study a class of continuous-time games in which one long-run agent and a population of small players learn about a hidden state from a public signal that is subject to Brownian shocks. The long-run agent can influence the small players’ beliefs by affecting the publicly observable signal or by affecting the hidden state itself, in both cases in an additively separable way. The impact of the small players’ beliefs on the long-run agent’s flow payoff is nonlinear. At a general level, I show that the long-run agent’s equilibrium incentives to distort public beliefs are characterized by ordinary differential equations (ODEs). These ODEs capture how the long-run agent’s incentives are determined by the size of marginal flow payoffs, by costsmoothing motives and by the impact of his actions on the small players’ expectations about equilibrium play. I also obtain closed-form solutions for onand off-equilibrium incentives in a subclass of games with Gaussian learning and quadratic preferences. Within this linearquadratic framework, I develop applications to monetary policy in a context of unobserved components of inflation, as well as to procurement in a context of contractors’ unobserved efficiency of providing goods.
Distinctive Subcellular Akt‐1 Responses to Shear Stress in Endothelial Cells

Endothelial cells undergo a rapid cell–cell junction inclination following exposure to atheroprotective unidirectional flow. In contrast, atherosclerotic lesions correlate with a heterogeneous distribution of the junctional wall inclination in cells exposed to time‐varying, reversing, and oscillatory flow as well as to low mean shear stress. However, the underlying biochemical events by which endothelial cells distinctively respond to unidirectional versus flow reversal remain unclear. Here, we show that the subcellular distribution of flow‐induced Akt‐1 phosphorylation in endothelial cells lining the mouse aorta varies depending on local hemodynamics. Activated Akt‐1 accumulated in perinuclear areas of cells in regions predisposed to disturbed flow but were localized at the cell–cell junction in regions of high unidirectional laminar shear stress. In flow‐adapted human endothelial cells, reversal in flow direction was associated within minutes with a subcellular concentration of phosphorylated Akt‐1 at the upstream edge of cells. Interestingly, oscillatory flow (with a zero mean shear stress) failed to activate Akt‐1, whereas a unidirectional pulsatile flow of similar amplitude induced an increase in Akt‐1 phosphorylation. Finally, silencing of the G protein αq/11 subunit abrogated both flow‐induced Akt‐1 and GSK‐3β activation. Together, these results characterize the existence of a Gαq/11‐mediated Akt‐1 signaling pathway that is dynamically responsive to flow direction, thereby offering a novel approach to regulating EC dysfunctions in regions subjected to flow reversal. J. Cell. Biochem. 115: 121–129, 2014. © 2013 Wiley Periodicals, Inc.
High degree of kinesiophobia after lumbar disc herniation surgery

Background and purpose Several studies have investigated outcomes after disc surgery. However, the occurrence of kinesiophobia has not been investigated previously in patients after disc herniation surgery. In this cross-sectional study, we investigated kinesiophobia in patients who had been treated surgically for lumbar disc herniation, and we related the results to established outcome measures. Patients and methods 10–34 months after surgery, questionnaires were sent to 97 patients who had undergone standardized open discectomy. Outcome measures included Tampa scale for kinesiophobia (TSK); Oswestry disability index (ODI); European quality of life in 5 dimensions (EQ-5D); visual analog scale (VAS) for leg and back pain, work disability, and patient satisfaction; Zung self-rating depression scale (ZDS); pain catastrophizing scale (PCS); and a self-efficacy scale (SES). Results 36 of 80 patients reported having kinesiophobia. There were statistically significant differences in ODI, EQ-5D, VAS leg and back pain, ZDS, PCS, and SES between patients with and without kinesiophobia. Interpretation Half of the patients suffered from kinesiophobia 10–34 months after surgery for disc herniation. These patients were more disabled, had more pain, more catastrophizing thoughts, more symptoms of depression, lower self-efficacy, and poorer health-related quality of life than patients without kinesiophobia.
Standards for Reporting Implementation Studies (StaRI): explanation and elaboration document

Objectives Implementation studies are often poorly reported and indexed, reducing their potential to inform the provision of healthcare services. The Standards for Reporting Implementation Studies (StaRI) initiative aims to develop guidelines for transparent and accurate reporting of implementation studies. Methods An international working group developed the StaRI guideline informed by a systematic literature review and e-Delphi prioritisation exercise. Following a face-to-face meeting, the checklist was developed iteratively by email discussion and critical review by international experts. Results The 27 items of the checklist are applicable to the broad range of study designs employed in implementation science. A key concept is the dual strands, represented as 2 columns in the checklist, describing, on the one hand, the implementation strategy and, on the other, the clinical, healthcare or public health intervention being implemented. This explanation and elaboration document details each of the items, explains the rationale and provides examples of good reporting practice. Conclusions Previously published reporting statements have been instrumental in improving reporting standards; adoption by journals and authors may achieve a similar improvement in the reporting of implementation strategies that will facilitate translation of effective interventions into routine practice.
A Validated Densitometric Method for Analysis of Atorvastatin Calcium and Metoprolol Tartarate as Bulk Drugs and In Combined Capsule Dosage Forms

A simple, accurate and precise high-performance thin-layer chromatographic method has been developed for the estimation of Atorvastatin Calcium and Metoprolol Tartarate simultaneously from a capsule dosage form. The method employed Silica gel 60F 254s precoated plates as stationary phase and a mixture of Chloroform: Methanol: Glacial acetic acid (dil.) :: (9:1.5:0.2 ml %v/v) as mobile phase. Densitometric scanning was performed at 220 nm using Camag TLC scanner 3. The method was linear in the drug concentrations′ range of 500 to 2500 ng/spot for Atorvastatin Calcium, also for Metoprolol Tartarate with correlation coefficient of 0.984 for Atorvastatin Calcium and 0.995 for Metoprolol Tartarate respectively. The retention factor for Atorvastatin Calcium was 0.45 ± 0.04 and for Metoprolol Tartarate was 0.25 ± 0.02. The method was validated as per ICH (International Conference on Harmonisation) Guidelines, proving its utility in estimation of Atorvastatin Calcium and Metoprolol Tartarate in combined dosage form.
Isolated systolic hypertension : prognostic information provided by pulse pressure.

Increased arterial stiffness results in increased characteristic impedance of the aorta and increased pulse wave velocity, which increases systolic and pulse pressures. An association between increased pulse pressure and adverse cardiovascular events has been found in normotensive and hypertensive patient populations. Increased pulse pressure has also been associated with thickening of the carotid intima and media. However, the relationship between pulse pressure and stroke has not previously been evaluated. In this study, we examined the hypothesis that pulse pressure is an independent predictor of stroke in elderly patients with systolic hypertension entered in the Systolic Hypertension in the Elderly Program. Differences in baseline characteristics were examined by tertiles of pulse pressure. The independent prognostic value of pulse pressure and mean arterial pressure for predicting either stroke or total mortality was assessed with Cox proportional hazards models that included pulse pressure, mean arterial pressure, and other variables that were significant on univariate analysis. This analysis demonstrated an 11% increase in stroke risk and a 16% increase in risk of all-cause mortality for each 10-mm Hg increase in pulse pressure. Each 10-mm Hg increase in mean arterial pressure was independently associated with a 20% increase in the risk of stroke and a 14% increase in the risk of all-cause mortality. These data provide strong evidence of an association of increased conduit vessel stiffness, as indicated by increased pulse pressure, with stroke and total mortality, independent of the effects of mean arterial pressure, in elderly patients with isolated systolic hypertension.
Realistic Expectations: The Management of Severe Alzheimer Disease

Alzheimer disease (AD) is a progressive neurodegenerative disorder characterized by a loss of memory and cognition, a decline in ability to perform activities of daily living, changes in personality and behavior, and an increase in resource utilization and medical care. The natural course of AD can be viewed as a gradual loss of independence divided into several stages (mild, moderate, and severe) that ultimately leaves the patient under the supervision of a caregiver. Acetylcholinesterase inhibitors are the most widely accepted and proven approach to the treatment of mild to moderate AD. However, management of patients with severe AD poses challenges to physicians because there are only limited treatment options for these patients. This article provides overviews of the natural history, current therapy, and diagnostic scales of AD, focusing on management of patients with severe AD. In addition, a brief summary of the existing clinical trials conducted in severe AD is presented.
Performance geometry capture for spatially varying relighting

In image-based relighting, novel illumination on a subject is synthesized based on images acquired in different basis lighting conditions. Most commonly, the basis images of the subject are taken under a variety of different directional lighting conditions. A linear combination of the color channels of the different lighting directions is formed to produce the rendering under novel illumination. When the lighting directions are densely distributed throughout the sphere, any distant lighting environment can be simulated accurately.
An approach for matching communication patterns in parallel applications

Interprocessor communication is an important factor in determining the performance scalability of parallel systems. The communication requirements of a parallel application can be quantified to understand its communication pattern and communication pattern similarities among applications can be determined. This is essential for the efficient mapping of applications on parallel systems and leads to better interprocessor communication implementation among others. This paper proposes a methodology to compare the communication pattern of distributed-memory programs. Communication correlation coefficient quantifies the degree of similarity between two applications based on the communication metrics selected to characterize the applications. To capture the network topology requirements, we extract the communication graph of each applications and quantities this similarity. We apply this methodology to four applications in the NAS parallel benchmark suite and evaluate the communication patterns by studying the effects of varying problem size and the number of logical processes (LPs).
Electron Transport in Single-Walled Carbon Nanotubes

Single-walled carbon nanotubes (SWNTs) are emerging as an important new class of electronic materials. Both metallic and semiconducting SWNTs have electrical properties that rival or exceed the best metals or semiconductors known. In this article, we review recent transport and scanning probe experiments that investigate the electrical properties of SWNTs.We address the fundamental scattering mechanisms in SWNTs, both in linear response and at high bias.We also discuss the nature and properties of contacts to SWNTs. Finally, we discuss device performance issues and potential applications in electronics and sensing.
High-Throughput and Area-Efficient MIMO Symbol Detection Based on Modified Dijkstra's Search

The multiple-input-multiple-output (MIMO) technique is being actively adopted in recent wireless communication standards to enhance data rate. Though channel capacity is increased by adopting multiple spatial streams, the computational complexity needed to eliminate the interference hinders the implementation of a practical system. In this paper, we propose a modified Dijkstra's algorithm and a precalculation technique to improve the throughput by allowing overlapped processing. For the maximum-likelihood (ML) detection, in addition, we propose a simple approximation of L2-norm calculation to reduce the computational complexity without degrading the error performance noticeably. A MIMO symbol detector based on the proposed algorithm is implemented in a 0.18- μm CMOS process, targeting 4 × 4 16-QAM. It occupies about 0.5 mm2 with 25.1 K equivalent gates and shows a throughput of over 300 Mb/s.
An overview of metrics-based approaches to support software components reusability assessment

Objective: To present an overview on the current state of the art concerning metrics-based quality evaluation of software components and component assemblies. Method: Comparison of several approaches available in the literature, using a framework comprising several aspects, such as scope, intent, definition technique, and maturity. Results: The identification of common shortcomings of current approaches, such as ambiguity in definition, lack of adequacy of the specifying formalisms and insufficient validation of current quality models and metrics for software components. Conclusions: Quality evaluation of components and component-based infrastructures presents new challenges to the Experimental Software Engineering community.
Parthenolide promotes the ubiquitination of MDM2 and activates p53 cellular functions

MDM2 belongs to a class of ring-finger domain–containing ubiquitin ligases that mediate the proteasomal degradation of numerous proteins, including themselves. Arguably, the most important substrate of MDM2 is p53, which controls cell cycle progression and apoptosis. MDM2 and p53 are parts of a feedback regulatory loop whose perturbations are often present in cancer and are targets for anticancer drug development. We found that the natural product, small-molecule anti-inflammatory agent parthenolide (PN), which is actively being investigated as a potential therapeutic for many human cancers, induces ubiquitination of MDM2 in treated cells, resulting in the activation of p53 and other MDM2-regulated tumor-suppressor proteins. Using cells with functional gene deletions and small interfering RNA knockdown studies, we found that these effects required the DNA damage transducer ataxia telangiectasia mutated. The effects of PN on tumor suppressor activation were comparable with that of nutlin-3a, a recently developed small molecule that was designed to interfere with the interaction between MDM2 and p53 but does not promote MDM2 ubiquitination. Our study illustrates an alternative approach for controlling MDM2 and p53 activities and identifies an additional critically important cancer pathway affected by PN. [Mol Cancer Ther 2009;8(3):552–62]
Numerical Analysis of Non-Constant Discounting With an Application to Renewable Resource Management

The possibility of non-constant discounting is important in environmental and resource management problems where current decisions affect welfare in the far-distant future, as with climate change. The difficulty of analyzing models with non-constant discounting limits their application. We describe and provide software to implement an algorithm to numerically obtain a Markov Perfect Equilibrium for an optimal control problem with non-constant discounting. The software is available online. We illustrate the approach by studying welfare and observational equivalence for a particular renewable resource management problem.
Variational formulation of problems involving fractional order differential operators

In this work, we consider boundary value problems involving either Caputo or Riemann-Liouville fractional derivatives of order α ∈ (1, 2) on the unit interval (0, 1). These fractional derivatives lead to nonsymmetric boundary value problems, which are investigated from a variational point of view. The variational problem for the Riemann-Liouville case is coercive on the space Hα/2 0 (0, 1) but the solutions are less regular, whereas that for the Caputo case involves different test and trial spaces. The numerical analysis of these problems requires the so-called shift theorems which show that the solutions of the variational problem are more regular. The regularity pickup enables one to establish convergence rates of the finite element approximations. The analytical theory is then applied to the Sturm-Liouville problem involving a fractional derivative in the leading term. Finally, extensive numerical results are presented to illustrate the error estimates for the source problem and eigenvalue problem.
Modeling of graphene–polymer interfacial mechanical behavior using molecular dynamics

Carbon nanotube (CNT) polymer-matrix composites exhibit promising properties as structural materials for which appropriate constitutive models are sought, to predict their macroscale behavior. The reliability of determining the homogenized response of such materials depends upon the ability to accurately capture the interfacial behavior between the nanotubes and the polymer matrix. In this work, molecular dynamics simulations, using the Consistent Valence Force Field (CVFF) to describe the atomistic interactions, are used to study nanoscale load transfer between polyethylene and a graphene sheet, a model system chosen to characterize the force-separation behavior between CNTs and the polymer matrix. Separation studies are conducted for both opening as well as sliding modes and cohesive zone parameters such as peak traction and energy of separation are evaluated for each mode. Studies are also carried out to investigate the effect of tension and compression on sliding mode separation. Size dependence studies are conducted utilizing different sizes of the computational domain and different boundary conditions, to obtain the representative volume element and connect to continuum level properties. These results set the stage for continuum length-scale micromechanical models which may be used in determining the overall material response, incorporating interfacial phenomena.
Dynamic Mechanisms of Cell Rigidity Sensing: Insights from a Computational Model of Actomyosin Networks

Cells modulate themselves in response to the surrounding environment like substrate elasticity, exhibiting structural reorganization driven by the contractility of cytoskeleton. The cytoskeleton is the scaffolding structure of eukaryotic cells, playing a central role in many mechanical and biological functions. It is composed of a network of actins, actin cross-linking proteins (ACPs), and molecular motors. The motors generate contractile forces by sliding couples of actin filaments in a polar fashion, and the contractile response of the cytoskeleton network is known to be modulated also by external stimuli, such as substrate stiffness. This implies an important role of actomyosin contractility in the cell mechano-sensing. However, how cells sense matrix stiffness via the contractility remains an open question. Here, we present a 3-D Brownian dynamics computational model of a cross-linked actin network including the dynamics of molecular motors and ACPs. The mechano-sensing properties of this active network are investigated by evaluating contraction and stress in response to different substrate stiffness. Results demonstrate two mechanisms that act to limit internal stress: (i) In stiff substrates, motors walk until they exert their maximum force, leading to a plateau stress that is independent of substrate stiffness, whereas (ii) in soft substrates, motors walk until they become blocked by other motors or ACPs, leading to submaximal stress levels. Therefore, this study provides new insights into the role of molecular motors in the contraction and rigidity sensing of cells.
Discrete Variable Generation for Improved Neural Network Classification

Neural networks are widely used for classification as they achieve good predictive accuracy. When the class labels are determined by complex interactions of the input variables, neural networks can be expected to provide better predictions than methods that test on the values of one variable at a time such as univariate decision tree classifiers. On the other hand, when no or relatively simple interaction between variables determines the class membership, the neural network may over fit the data and the input-to-output relationship in the data is represented by a function that is more complex than it should be. In this paper, we propose adding discretized values of the continuous variables in the data as input when training the neural networks. Finding out whether the discretized values or the original continuous values of the variables are useful is achieved by pruning. By having only the relevant inputs left in the pruned networks, we are able to extract classification rules from these networks that are accurate, concise and interpretable.
Exploring Fisher vector and deep networks for action spotting

This paper describes our method and attempt on track 2 at the ChaLearn Looking at People (LAP) challenge 2015. Our approach utilizes Fisher vector and iDT features for action spotting, and improve its performance from two aspects: (i) We take account of interaction labels into the training process; (ii) By visualizing our results on validation set, we find that our previous method [10] is weak in detecting action class 2, and improve it by introducing multiple thresholds. Moreover, we exploit deep neural networks to extract both appearance and motion representation for this task. However, our current deep network fails to yield better performance than our Fisher vector based approach and may need further exploration. For this reason, we submit the results obtained by our Fisher vector approach which achieves a Jaccard Index of 0.5385 and ranks the 1st place in track 2.