Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorrect aseptic techniques in medicine preparation and recommendations for safer practices: a systematic review

Objective Many patient deaths have been reported because of administration of contaminated intravenous medicines due to incorrect aseptic techniques. Our aim was to review the literature for (1) incorrect practices in aseptic drug preparation and administration and (2) recommendations for safer practices in hospitals. Methods A systematic literature search was conducted in PubMed covering 2007–2015. Studies were included if they concerned aseptic medicine preparation and administration in hospitals by different healthcare professionals, assessed incorrect practices and made recommendations for safer aseptic preparation and administration. Results 26 studies were included of which 19 were original articles. 12 of the studies concerned description of incorrect practices that led to contamination. The studies reported 11 incorrect practices that increased the risk of contamination of parenteral medicines. The most reported incorrect practices were multiple use of phials and syringes (2/12 studies) and lack of overall disinfection during the aseptic preparation and administration (3/12 studies). 22 practices were recommended to avoid contamination, which were classified into six categories: equipment and medicines (7); disinfection (6); working environment (3); storing (3); catheter care (2) and quality of prepared medicines (1). The results indicate that pharmacists prepared syringes with less contamination than nurses because of the pharmacist's aseptic skills and environmental aspects in pharmacy units. Conclusions The review discusses many appropriate and enhanced practices in aseptic drug preparation and administration. As the change for the better in contamination rates of administered medicines seems to be challenging to achieve in hospitals, better and possibly international procedures for safe parenteral practices need to be developed.
Apparent source width and the church organ

The Concert Organ and the Cathedral are the two major extremes of their disciplines: Musical Acoustics and Architectural Acoustics. With that, they represent some of the most difficult problems in both domains, violating many of the assumptions typically used. The interest of the current work is preliminary examination of the effectiveness and interpretation of the Apparent Source Width (ASW) in evaluating the acoustic of both the church and also of the instrument itself. The ASW is becoming more commonly used in concert Hall acoustics as one of the descriptions of spaciousness of the hall. The measure of ASW is based on the Inter-Aural Cross-Correlation (IACC) using a binaural recording.
Dielectric behavior of La 1x Ca x MnO 3 ( 0 . 4 ≤ x ≤ 0 . 5 )

In this work the dielectric behavior of semiconducting La-manganites in an intermediate Ca-doping regime is studied. Ceramic samples were prepared by conventional solid-state reaction, using elemental oxides as starting reactants. A chemical, structural and microstructural characterization of the prepared manganites was performed by iodometric titrations, X-ray diffraction and scanning electron microscopy, respectively. The magnetic susceptibility and electrical resistivity as a function of temperature were also measured. The complex dielectric permittivity of the samples was determined as a function of frequency and temperature. Very high values of dielectric constant in a wide frequency and temperature range were observed for all the synthesized samples. Moreover, an increase of the dielectric constant at temperatures close to chargeorder or metal-insulator transition is reported.
Predicting Pushing Action Effects on Spatial Object Relations by Learning Internal Prediction Models

Understanding the effects of actions is essential for planning and executing robot tasks. By imagining possible action consequences, a robot can choose specific action parameters to achieve desired goal states. We present an approach for parametrizing pushing actions based on learning internal prediction models. These pushing actions must fulfill constraints given by a high-level planner, e. g. , after the push the brown box must be to the right of the orange box. In this work, we represent the perceived scenes as object-centric graphs and learn an internal model, which predicts object pose changes due to pushing actions. We train this internal model on a large synthetic data set, which was generated in simulation, and record a smaller data set on the real robot for evaluation. For a given scene and goal state, the robot generates a set of possible pushing action candidates by sampling the parameter space and then evaluating the candidates by internal simulation, i. e. , by comparing the predicted effect resulting from the internal model with the desired effect provided by the high-level planner. In the evaluation, we show that our model achieves high prediction accuracy in scenes with a varying number of objects and, in contrast to state-of-the-art approaches, is able to generalize to scenes with more objects than seen during training. In experiments on the humanoid robot ARMAR-6, we validate the transfer from simulation and show that the learned internal model can be used to manipulate scenes into desired states effectively.
Data Warehouse Design to Support Social Media Analysis in a Big Data Environment 

The volume of generated and stored data from social media has increased in the last decade. Therefore, analyzing and understanding this kind of data can offer relevant information in different contexts and can assist researchers and companies in the decision-making process. However, the data are scattered in a large volume, come from different sources, with different formats and are rapidly created. Such facts make the knowledge extraction difficult, turning it in a complex and high costly process. The scientific contribution of this paper is the development of a social media data integration model based on a data warehouse to reduce the computational costs related to data analysis, as well as support the application of techniques to discover useful knowledge. Differently from the literature, we focus on both social media Facebook and Twitter. Also, we contribute with the proposition of a model for the acquisition, transformation and loading data, which can enable the extraction of useful knowledge in a context where the human capability of understanding is exceeded. The results showed that the proposed data warehouse improves the quality of data mining algorithms compared to related works, while being able to reduce the execution time.
Ja n 20 20 Gravitational Vortex Mass in a Superfluid

We consider superfluid hydrodynamics of two-dimensional Bose–Einstein condensates. Interpreting the curvature of the macroscopic condensate wavefunction as an effective gravity in such a superfluid universe, we argue for a superfluid equivalence principle—that the gravitational mass of a quantised vortex should be equal to the inertial vortex mass. In this model, gravity and electromagnetism have the same origin and are emergent properties of the superfluid universe, which itself emerges from the underlying collective structure of more elementary particles, such as atoms. The Bose–Einstein condensate is identified as the elusive dark matter of the superfluid universe with vortices and phonons, respectively, corresponding to massive charged particles and massless photons. Implications of this cosmological picture of superfluids to the physics of dense vortex matter are considered.
ACM Transactions on Embedded Computing Systems

To protect multicores from soft-error perturbations, research has explored various resiliency schemes that provide high soft-error coverage. However, these schemes incur high performance and energy overheads. We observe that not all soft-error perturbations affect program correctness, and some soft-errors only affect program accuracy, i.e., the program completes with certain acceptable deviations from error free outcome. Thus, it is practical to improve processor efficiency by trading off resiliency overheads with program accuracy. This article proposes the idea of declarative resilience that selectively applies strong resiliency schemes for code regions that are crucial for program correctness (crucial code) and lightweight resiliency for code regions that are susceptible to program accuracy deviations as a result of soft-errors (non-crucial code). At the application level, crucial and non-crucial code is identified based on its impact on the program outcome. A cross-layer architecture enables efficient resilience along with holistic soft-error coverage. Only program accuracy is compromised in the worst-case scenario of a soft-error strike during non-crucial code execution. For a set of machine-learning and graph analytic benchmarks, declarative resilience reduces performance overhead over a state-of-the-art system that applies strong resiliency for all program code regions from ∼ 1 . 43 × to ∼ 1 . 2 × . CCS
Debiased offline evaluation of recommender systems: a weighted-sampling approach

Offline evaluation of recommender systems mostly relies on historical data, which is often biased by many confounders. In such data, user-item interactions are Missing Not At Random (MNAR). Measures of recommender system performance on MNAR test data are unlikely to be reliable indicators of real-world performance unless something is done to mitigate the bias. One way that researchers try to obtain less biased offline evaluation is by designing new supposedly unbiased performance estimators for use on MNAR test data. We investigate an alternative solution, a sampling approach. The general idea is to use a sampling strategy on MNAR data to generate an intervened test set with less bias --- one in which interactions are Missing At Random (MAR) or, at least, one that is more MAR-like. An example of this is SKEW, a sampling strategy that aims to adjust for the confounding effect that an item's popularity has on its likelihood of being observed. In this paper, we propose a novel formulation for the sampling approach. We compare our solution to SKEW and to two baselines which perform a random intervention on MNAR data (and hence are equivalent to no intervention in practice). We empirically validate for the first time the effectiveness of SKEW and we show our approach to be a better estimator of the performance one would obtain on (unbiased) MAR test data. Our strategy benefits from high generality properties (e.g. it can also be employed for training a recommender) and low overheads (e.g. it does not require any learning).
An empirical Overview on the Implementation of Big Results Now Initiative in Tanzania and its Efficacy on Academic Performance in Secondary Schools

In Tanzania, education is conceived as a strategic agent for mindset transformation and for the creation of a welleducated nation, sufficiently equipped with the knowledge needed to competently and competitively solve the development challenges facing the nation. Based on this conviction, the government adapted the Big Results Now Initiative (BRN) from the Malaysian government in 2013 so as to realize its educational goals. The Big Results Now Initiative was aimed at improving students’ academic performance by fast-tracking the quality of secondary school education. It focused on delivering defined goals at a predetermined timeline; that is, from 2013 to 2016. This paper provides an empirical overview on the implementation of Big Results Now initiative in Tanzania and its efficacy on students’ academic performance in secondary schools. Big Results Now readiness dimensions and rationale is first presented alongside the nexus between implementation and academic performance in secondary schools. The paper concludes that the implementation of Big Results Now Initiative in Tanzania has had some noticeable effects on the academic performance in secondary schools. However, a lot still need to be done in as far as bridging the existing discrepancy between implementation and quality academic performance. It is recommended that the implementation of important educational initiatives like BRN should follow a bottom-up approachfor a sense of ownership and for a desirable attention to detail. Furthermore, the government should not wholly depend on donors to finance the implementation of educational policies and/or initiative but rather engage the local citizenry.
The role of non ‐ rigid cervical collar in pain relief and functional restoration after whiplash injury : a systematic review and a pooled analysis of randomized controlled trials

We read with interest the systematic review by Ricciardi et al. [1] on the effect of non-rigid cervical collar (nRCC) in the rehabilitation of whiplash injury. The conclusion of their review favors an active “non-immobilization” approach [1], in line with most literature on the topic. However, after reading the review, we do have some methodological concerns that could have significant impact on the interpretation of the findings. The authors aim to investigate whether wearing a nRCC improves pain and cervical range of motion (ROM) more than not wearing an nRCC [1]. However, the review included four papers based on two RCTs [2–5), which either equipped all participants, including the “non-immobilization” group, with a nRCC or allowed for it to be used in the first days after the injury. As all participants had access to and potentially used a nRCC, these studies are not appropriate to use to investigate the research aim of the systematic review. In addition, the included study by Kongsted et al. [6] clearly stated that they had used a “semirigid Philadelphia neck collar” and not a nRCC. This study is therefore not appropriate for the specific review aim either. For the pooled data on the pain VAS scores, two papers [2, 3] included in the review seem to be based on the same patient population, although they are reported as two separate studies for the time interval “VAS t < 3 m” [1]. Furthermore, Ricciardi et al. [1] display VAS data as means and SD in a table in the review while the exact same values are presented as mean and SEM in the original papers from Mealy et al. [7] and Borchgrevink et al. [8]. Similarly, the ROM data from Mealy et al. [8] are also presented in the review [1] as mean and SD while the original paper has reported these values as mean and SEM. Regarding ROM, the authors define this as “. the degree of mean lateral flexion of the neck” [1] in line with what is reported in the paper by McKinney et al. [4]. However, Mealy et al. [7] report “Total cervical movement-that is, flexion, extension, right and left lateral flexion, and right and left rotation-was calculated, giving a numerical score” which is not mentioned in the review [1]. Taken together, these methodological concerns of the review by Ricciardi et al. [1] may have a direct impact on the interpretation of the findings.
Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV With Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their air-ground communications. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV’s controllable mobility to design its navigation/trajectory to avoid the cellular BSs’ coverage holes while accomplishing their missions. To this end, we formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, which, however, cannot be solved by the standard optimization techniques due to the lack of an accurate and tractable end-to-end communication model in practice. To overcome this difficulty, we propose a new solution approach based on the technique of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling double deep Q network (dueling DDQN) with multi-step learning, we first propose a UAV navigation algorithm based on direct RL, where the signal measurement at the UAV is used to directly train the action-value function of the navigation policy. To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV’s signal measurement is used not only for training the DQN directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.
Facing problems in the determination of stellar temperatures and gravities: Galactic globular clusters

We analysed red giant branch stars in 16 Galactic globular clusters, computing their atmospheric parameters both from the photometry and from excitation and ionisation balances. The spectroscopic parameters are lower than the photometric ones and this discrepancy increases with decreasing metallicity, reaching differences of ~350 K in effective temperature and ~1 dex in surface gravity at [Fe/H] ~ –2.5 dex. We demonstrate that the spectroscopic parameters are inconsistent with the position of the stars in the colour-magnitude diagram, providing overly low temperatures and gravities, and predicting that the stars are up to about 2.5 magnitudes brighter than the observed magnitudes. The parameter discrepancy is likely due to inadequacies in the adopted physics; in particular the assumption of a one-dimensional geometry could be the origin of the observed slope between iron abundances and excitation potential that leads to low temperatures. However, the current modelling of 3D/NLTE radiative transfer for giant stars seems to be unable to totally erase this slope. We conclude that the spectroscopic parameters are incorrect for metallicity lower than –1.5 dex and that photometric temperatures and gravities should be adopted for these red giant stars. We provide a simple relation to correct the spectroscopic temperatures in order to put them onto a photometric scale.
Audio inpainting with generative adversarial network

We study the ability of Wasserstein Generative Adversarial Network (WGAN) to generate missing audio content which is, in context, (statistically similar) to the sound and the neighboring borders. We deal with the challenge of audio inpainting long range gaps (500 ms) using WGAN models. We improved the quality of the inpainting part using a new proposed WGAN architecture that uses a short-range and a long-range neighboring borders compared to the classical WGAN model. The performance was compared with two different audio instruments (piano and guitar) and on virtuoso pianists together with a string orchestra. The objective difference grading (ODG) was used to evaluate the performance of both architectures. The proposed model outperforms the classical WGAN model and improves the reconstruction of high-frequency content. Further, we got better results for instruments where the frequency spectrum is mainly in the lower range where small noises are less annoying for human ear and the inpainting part is more perceptible. Finally, we could show that better test results for audio dataset were reached where a particular instrument is accompanist by other instruments if we train the network only on this particular instrument neglecting the other instruments.
Comment on "Quantum key agreement protocol"

The first two party Quantum Key Agreement (QKA) protocol, based on quantum teleportation, was proposed by Zhou et al. (Electronics Letters 40.18 (2004): 1149-1150). In this protocol, to obtain the key bit string, one of the parties use a device to obtain inner product of two quantum states, one being unknown, and the other one performs Bell measurement. However, in this article, we show that it is not possible to obtain a device that would output the inner product of two qubits even when only one of the qubit is unknown. This is so because existence of such device would imply perfectly distinguishing among four different states in a two-dimensional vector space. This is not permissible in quantum mechanics. Furthermore, we argue that existence of such a device would also imply violation of the ``No Signalling Theorem" as well. Finally, we also comment that this protocol is not a valid key agreement protocol at all.
Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods

Abstract Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This article considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package miselect. Simulations demonstrate that the “stacked” approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials for this article are available online.
Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes

In the United States, 25% or greater than 200 billion dollars of hospital spending accounts for administrative costs that involve services for medical coding and billing. With the increasing number of patient records, manual assignment of the codes performed is overwhelming, time-consuming and error-prone, causing billing errors. Natural language processing can automate the extraction of codes/labels from unstructured clinical notes, which can aid human coders to save time, increase productivity, and verify medical coding errors. Our objective is to identify appropriate diagnosis and procedure codes from clinical notes by performing multi-label classification. We used de-identified data of critical care patients from the MIMIC-III database and subset the data to select the ten (top-10) and fifty (top-50) most common diagnoses and procedures, which covers 47.45% and 74.12% of all admissions respectively. We implemented state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) to fine-tune the language model on 80% of the data and validated on the remaining 20%. The model achieved an overall accuracy of 87.08%, an F1 score of 85.82%, and an AUC of 91.76% for top-10 codes. For the top-50 codes, our model achieved an overall accuracy of 93.76%, an F1 score of 92.24%, and AUC of 91%. When compared to previously published research, our model outperforms in predicting codes from the clinical text. We discuss approaches to generalize the knowledge discovery process of our MIMIC-BERT to other clinical notes. This can help human coders to save time, prevent backlogs, and additional costs due to coding errors.