Miracle-dz/UniFAR-FTUs huggingface dataset:
Microsphere Fabrication of Polycaprolactone via Electrospray: Effect of Different Parameters

: In controlled drug release systems, biodegradable and biocompatible polymer microspheres are good candidates as drug carrier due to their large surface area. Here, we used the electrospraying technique to fabricate polycaprolactone microspheres. By exploring the process parameters systematically, microspheres with different morphology were produced. The increase of feed rate increased the particle size and collapse of the particle, a feed rate of 0.2  L/min was found to produce narrow size distributed microspheres with the average size of ~4  m. Our study shows that electrospray is a promising technique to fabricate microspheres applied in controlled drug
Climatic drivers of dipterocarp mass-flowering in South-East Asia

Abstract Dipterocarpaceae, a dominant family of trees in South-East Asian tropical forests, are remarkable in that they exhibit supra-annual mass-flowering events. The flowering patterns are related to the El Niño Southern Oscillation, but the mechanism that precipitates mass-flowering is still debated. Here, we test if a cumulative-trigger model that tracks resource availability, specifically light, may better explain dipterocarp phenology than a direct-environmental-trigger mechanism. Using 11 flowering time series with an average length of 29 y and variety of candidate predictor variables (precipitation, cloud cover, minimum temperature and El Niño indices) we could not find a plausible direct-environmental-trigger (median AUCs across regions from 0.53 to 0.57 indicating near random predictions). The cumulative-trigger model based on El Niño indices showed better predictive results (AUC 0.67), which could further be improved by resetting the resource at known flowering events (AUC 0.76). Additional support for a cumulative-trigger model comes from the observation that regional differences in the time of year of peak flowering correspond to where El Niño effects are strongest. We conclude that cumulative resource tracking is an evolutionary plausible trigger mechanism that has other primary evolutionary advantages, such as predator satiation.
Constitutive Explanatory Relevance

In what sense are the activities and properties of components in a mechanism explanatorily relevant to the behavior of a mechanism as a whole? I articulate this problem, the problem of constitutive relevance, and I show that it must be solved if we are to understand mechanisms and mechanistic explanation. I argue against some putative solutions to the problem of constitutive relevance, and I sketch a positive account according to which relevance is analyzed in terms of relationships of mutual manipulability between the behavior of a mechanism as a whole and the properties and activities of its components. My account is a causal-mechanical account in the sense that it is a particular expression of the idea that constitutive explanation is a matter of showing how an explanandum phenomenon is situated within the causal structure of the world. It is thus offered as a rival to epistemic (argument-centered) and psychological accounts of interlevel explanation.
Weighted cooperative reinforcement learning-based energy-efficient autonomous resource selection strategy for underlay D2D communication

Underlay Device-to-Device (D2D) communication is a key technology responsible for high data rate, ultra-low latency with high spectral and energy efficiency in 5G cellular networks. But to achieve its full potential, optimal channel allocation and effective co-channel interference management must be accomplished. To address this challenge, we propose a multi-agent reinforcement learning based autonomous channel selection scheme for D2D communication. The proposed scheme, Weighted Cooperative Q -Learning based Resource Selection (WCopQLRS), allows a D2D pair to learn to select a channel from the available resources autonomously. Learning process of each D2D transmitter involves cooperation from neighboring D2D agents by exchanging their latest Q -values. An additional parameter called cooperation range is used to determine the neighboring pairs whose Q -values can be used for learning the optimal policy. The limited prior information prevents a linear increase in the dimensions of Q -value matrix of each learning agent when the number of D2D pairs within the cell is huge. Though WCopQL-RS involves additional information exchange among agents as compared to independent learning but also provides improved system throughput and convergence speed. It is shown through simulation results that WCopQL-RS outperforms other existing schemes in terms of average D2D user throughput, energy consumption and fairness value.
Redistribution in a political union: The case of the EU

A key function of centralized budgets in federal and political unions is to act as an equalizing mechanism to support economic and social cohesion. This is also the case with the European Union’s (EU) budget,which operates as a redistributive mechanism that counteracts the cross-national and cross-regional inequalities created by the single market. Despite the limits on cross-national redistribution imposed by a centrifugal system of representation, the net fiscal position of member states – what they pay to the EU budget minus what they receive from it – is very diverse and has changed quite remarkably over the last decades. In this paper, we investigate how and why the net fiscal position of each member state toward the rest of the EU changes over time. We develop a novel panel dataset (1979–2014) to study how key national and EU-level political and economic variables affect the EU redistributive dynamics. We find that redistribution via the EU budget primarily targets developments in inequality within EU member states, and that an increase in domestic unemployment may also improve the country’s fiscal balance. Moreover, we find that voting power in the Council is unrelated to a more positive fiscal balance. However, we find that governments with a centre-right profile are in general more successful in improving their redistributive position vis-à-vis the other member states. This may create a problem of budgetary ‘rent extraction’.
The relationship between objective measures of sleep and training load across different phases of the season in American collegiate football players

ABSTRACT Purpose: Despite the perceived importance of sleep for American collegiate football (ACF) players, particularly given the schooling and athletic expectations for these players, descriptions of the duration and quality of sleep are limited. Thus, this study investigated the relationship between objective measures of sleep and training load across different phases of the ACF season. Methods: 23 ACF players’ (21.1 ± 1.2 years; 108.0 ± 20.0 kg). sleep/wake behaviour and daily external training load were assessed using wristwatch actigraphy and accelerometry (PlayerLoadTM [PL]), respectively, for a minimum of 3 nights/days in each phase (Off-season, Camp [Pre-season], In-Season and School). The relationships between each sleep metric and both season phase and external training load were assessed using linear mixed models. Results: Overall, total sleep time was very likely shorter in Camp (−41 ± 13 min, effect size [ES 0.68 ± 0.36[), almost certainly shorter In-Season (−56 ± 14 min, ES 0.93 ± 0.39) and likely shorter in School (−28 ± 15 min, ES 0.46 ± 0.42) compared to the Off-season phase. There was almost certainly a difference in sleep latency during the school phase (ES = 4.67 ± 2.03). Conclusion: These data suggest sleep time is reduced during periods of intensified training in ACF players. Of further concern are the demands placed upon student-athletes during the School phase, where aspects of sleep can be compromised.
Making sense of making: critical issues in the integration of maker education into schools

ABSTRACT This article considers growing calls for the integration of so-called ‘maker technologies’ and associated ‘maker’ practices into schools and other formal education settings. Moving on from the largely celebratory literature in this area, the article seeks to further explore the tensions between the educational potential of maker technology and the realities of its use in school and classroom contexts. In particular, the article focuses on fundamental – but so far little acknowledged – tensions surrounding the social, cultural, political contexts of maker education, alongside the epistemological and pedagogical characteristics of maker technologies as tools for learning. It is concluded that maker education must not be seen as a ready fit with formal schooling. Instead, teachers and schools are likely to require sustained support to make the most of maker technologies within the demands and constraints of contemporary school contexts.
Motion Conflict Detection and Resolution in Visual-Inertial Localization Algorithm

In this dissertation, we have focused on conflicts that occur due to disagreeing motions in multi-modal localization algorithms. In-spite of the recent achievements in robust localization by means of multi-sensor fusion, these algorithms are not applicable to all environments. This is primarily attributed to the following fundamental assumptions: (i) the environment is predominantly stationary, (ii) only ego-motion of the sensor platform exists, and (iii) multiple sensors are always in agreement with each other regarding the observed motion. Recently, studies have shown how to relax the static environment assumption using outlier rejection techniques and dynamic object segmentation. Additionally, to handle non ego-motion, approaches that extend the localization algorithm to multi-body tracking have been studied. However, there has been no attention given to the conditions where multiple sensors contradict each other with regard to the motions observed. Vision based localization has become an attractive approach for both indoor and outdoor applications due to the large information bandwidth provided by images and reduced cost of the cameras used. In order to improve the robustness and overcome the limitations of vision, an Inertial Measurement Unit (IMU) may be used. Even though visual-inertial localization has better accuracy and improved robustness due to the complementary nature of camera and IMU sensor, they are affected by disagreements in motion observations. We term such dynamic situations as environments with motion conflict because these are caused when multiple different but self-consistent motions are observed by different sensors. Tightly coupled visual inertial fusion approaches that disregard such challenging situations exhibit drift that can lead to catastrophic errors. We have provided a probabilistic model for motion conflict. Additionally, a novel algorithm to detect and resolve motion conflicts is also presented. Our method to detect motion conflicts is based on per-frame positional estimate discrepancy and per-landmark reprojection errors. Motion conflicts were resolved by eliminating inconsistent IMU and landmark measurements. Finally, a Motion Conflict aware Visual Inertial Odometry (MC-VIO) algorithm that combined both detection and resolution of motion conflict was implemented. Both quantitative and qualitative evaluation of MC-VIO on visually and inertially challenging datasets were obtained. Experimental results indicated that MC-VIO algorithm reduced the absolute trajectory error by 70% and the relative pose error by 34% in scenes with motion conflict, in comparison to the reference VIO algorithm. Motion conflict detection and resolution enables the application of visual inertial localization algorithms to real dynamic environments. This paves the way for articulate object tracking in robotics. It may also find numerous applications in active long term augmented reality.
A CROSS-LAYER BASED SCALABLE CHANNEL SLOT RE-UTILIZATION TECHNIQUE FOR WIRELESS MESH NETWORK

Due to tremendous growth of the wireless based application services are increasing the demand for wireless communication techniques that use bandwidth more effectively. Channel slot reutilization in multi-radio wireless mesh networks is a very challenging problem. WMNs have been adopted as back haul to connect various networks such as Wi-Fi (802.11), WI-MAX (802.16e) etc. to the internet. The slot re-utilization technique proposed so far suffer due to high collision due to improper channel slot usage approximation error. To overcome this here the author propose the cross layer optimization technique by designing a device classification based channel slot re-utilization routing strategy which considers the channel slot and node information from various layers and use some of these parameters to approximate the risk involve in channel slot re-utilization in order to improve the QoS of the network. The simulation and analytical results show the effectiveness of our proposed approach in term of channel slot re-utilization efficiency and thus helps in reducing latency for data transmission and reduce channel slot collision.
Design of a phantom equivalent to measure bone-fluorine in a human's hand via delayed neutron activation analysis

Fluorine is an element that can be either beneficial or harmful, depending on the total amount accumulated in the teeth or bones. In our laboratory, we have developed a non-invasive technique for the in vivo measurement of fluoride in bone using neutron activation analysis and performed the first pilot human study. Fluoride in humans is quantified by comparing the γ-ray signal from a person to the γ-ray signal obtained from appropriate anthropomorphic calibration phantoms. An identified problem with existing fluoride phantoms is contamination with aluminum. Aluminum creates an interfering γ-ray signal which, although it can be subtracted out, increases the uncertainty in the measurement and worsens the detection limit. This paper outlines a series of studies undertaken to develop a better calibration phantom for fluorine measurement, which does not have aluminum contamination.
Adaptive Image Retrieval Based on Multi-Feature Fusion

With the rapid development of technology and the popularity of the Internet, a large number of image datasets have been produced in various industries. It is difficult to quickly search for a desired image in a large number of image datasets, so it is very meaningful to design an efficient image retrieval system. This paper proposes an adaptive weighting method based on information entropy. Firstly, the trust of a single feature is obtained according to the information entropy. Then the transfer matrix is constructed according to the trust of a single feature, and the weight of the single feature is obtained by iterative calculation according to the transfer matrix. This method has the following advantages: (1) The retrieval system combines the performance of multiple features and has higher accuracy than the retrieval of a single feature; (2) In the query process, the weight of the individual features is dynamically updated using the query image, so that the retrieval system takes full advantage of the single feature. The experimental result shows that the proposed method is better than the previous method. The Mean Average Precision of the search on the Holidays image dataset is 83.99%.
Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study

An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.
Vector Approximate Message Passing

The standard linear regression (SLR) problem is to recover a vector $\mathrm {x}^{0}$ from noisy linear observations $\mathrm {y}=\mathrm {Ax}^{0}+\mathrm {w}$ . The approximate message passing (AMP) algorithm proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i.i. d. sub-Gaussian matrices A, its per-iteration behavior is rigorously characterized by a scalar state-evolution whose fixed points, when unique, are Bayes optimal. The AMP algorithm, however, is fragile in that even small deviations from the i.i. d. sub-Gaussian model can cause the algorithm to diverge. This paper considers a “vector AMP” (VAMP) algorithm and shows that VAMP has a rigorous scalar state-evolution that holds under a much broader class of large random matrices A: those that are right-orthogonally invariant. After performing an initial singular value decomposition (SVD) of A, the per-iteration complexity of VAMP is similar to that of AMP. In addition, the fixed points of VAMP’s state evolution are consistent with the replica prediction of the minimum mean-squared error derived by Tulino, Caire, Verdú, and Shamai. Numerical experiments are used to confirm the effectiveness of VAMP and its consistency with state-evolution predictions.
Privacy preserving set intersection

and Paper Modern societies are increasingly dependent on (and sometimes afraid of) massive amounts and availability of digital information. There are numerous scenarios where sensitive data must be – even if reluctantly or suspiciously shared between entities (or institutions) without mutual trust. National privacy protection laws, however, forbid the processing of non-anonymized records between institutions and even within the same institution, thus making it difficult to carry out statistical studies. The goal of this paper is to explore viable techniques to carry out data linkage among institutions while preserving a desirable level of anonymity. Taking advantage of cryptographic techniques introduced since the 90s, we provide some examples of linkage of anonymized files, in an attempt to overcome the current privacy constraints. Administrative records including socio-economic and financial information, both for households and firms, have huge potential for statistical studies. In Italy, the Fiscal Code is a widely used identifier for individuals and firms, and this would theoretically provide wide scope for data matching, provided that the legislation on such matters was respected. In this paper, we propose some modifications on a protocol, based on hashing and cryptographic functions, to strongly distinguish among identifying and not-identifying data and carrying out the objective of performing privacy preserving analytics. The examined scenarios offer a strict supervision over who is in possession of which information. This allows us to prevent unauthorized linkage of data and to protect individuals anonymity. Here our goal is to explore viable techniques to carry out data linkage among institutions while preserving a desirable level of anonymity. We present some examples of empirical applications with the use of synthetic data.
Development of a fast simulation method for particle-laden fluid interfaces and selected applications to problems involving drops

Solid particles tend to adhere to fluid interfaces under the action of capillary force. This adsorption process is robust and has been exploited in lots of applications from stabilisation of emulsions to microfluidic fabrications. The resulting particleladen fluid interfaces can manifest solid-like behaviours. The modification of the surface tension and the emergence of surface shear elasticity of a particle-covered drops are attributed to the particle-induced surface stress. This stress represents at the continuum level the microscopic effect of particle-particle interactions. Understanding the link between the surface stress and the particle arrangement are crucial for creating novel soft materials in the future. A challenge remains when carrying out numerical simulations of particle-laden fluid interfaces: the large separation of scales makes the direct numerical simulations extraordinary expensive. Physical features present in the system come from both the liquid meniscus on the surface of each particle and the fluid interfaces containing thousands of particles. Motivated by the need for a fast simulation method to study problems involving particle-laden fluid interface, this thesis presents a new numerical formulation named Fast Interface Particle Interaction (FIPI) that can be used to simulate a large number of solid particles absorbed on fluid interfaces at a moderate computational cost. The outstanding performance of this new method is attributed to the fact that particle-level phenomena are modelled with analytical or semi-empirical expressions while hydrodynamics and fluid interface morphology at larger scales are fully resolved.
The changing role of organic nitrates in the removal and transport of NOx

Abstract. A better understanding of the chemistry of nitrogen oxides (NOx) is crucial to effectively reducing air pollution and predicting future air quality. The response of NOx lifetime to perturbations in emissions or in the climate system is set in large part by whether NOx loss occurs primarily by the direct formation of HNO3 or through the formation of alkyl and multifunctional nitrates (RONO2). Using 15 years of detailed in situ observations, we show that in the summer daytime continental boundary layer the relative importance of these two pathways can be well approximated by the relative likelihood that OH will react with NO2 or instead with a volatile organic compound (VOC). Over the past decades, changes in anthropogenic emissions of both NOx and VOCs have led to a significant increase in the overall importance of RONO2 chemistry to NOx loss. We find that this shift is associated with a decreased effectiveness of NOx emissions reductions on ozone production in polluted areas and increased transport of NOx from source to receptor regions. This change in chemistry, combined with changes in the spatial pattern of NOx emissions, is observed to be leading to a flatter distribution of NO2 across the United States, potentially transforming ozone air pollution from a local issue into a regional one.
Title : Genetic and environmental exposures constrain epigenetic drift over the human life course Running title : Genetics & environment constrain epigenetic drift

Material Supplemental http://genome.cshlp. org/content/suppl/2014/09/17/gr.176933. 114.DC1. html P<P Published online September 23, 2014 in advance of the print journal. Manuscript Accepted manuscript is likely to differ from the final, published version. Peer-reviewed and accepted for publication but not copyedited or typeset; accepted License Commons Creative . http://creativecommons.org/licenses/by-nc/4.0/ described at a Creative Commons License (Attribution-NonCommercial 4.0 International), as ). After six months, it is available under http://genome.cshlp. org/site/misc/terms.xhtml first six months after the full-issue publication date (see This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the