Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Noncertified Pharmacist–Led Antimicrobial Stewardship Programs in a Medium-Sized Hospital Without an Infectious Disease Specialist: A Retrospective Pre–Post Study

Abstract Background Few studies have reported the outcomes of antimicrobial stewardship programs (ASPs) implemented without infectious disease (ID) physician or pharmacist specialists. We implemented interventions that included providing antimicrobial optimization recommendations through a pharmacist-led team using prospective audit and feedback. This study evaluated different types of interventions and their impact on the outcomes of ASPs in a medium-sized hospital without ID specialists. Methods This retrospective pre–post study included adult inpatients treated with intravenous antimicrobials between April 2016 and March 2020. Outcome (eg, length of hospital stay [LOS], drug cost) and process measures (eg, type of intervention, length of therapy) were compared between 2 time periods: pre-ASP (April 2016–March 2018) and post-ASP (April 2018–March 2020). Results We included 5419 and 5634 patients in the pre- and post-ASP periods, respectively. The most common types of interventions were adjusting length of therapy (49.5%), additional laboratory tests (27.1%), antimicrobial change (16.2%), and dosage of antimicrobial (7.1%). After ASP implementation, LOS significantly decreased (14.8 vs 13.8 days, P < .01), along with the length of therapy, empirical use of antipseudomonal and anti–methicillin-resistant Staphylococcus aureus drugs, and number of days to de-escalation. No significant differences were noted in 30-day mortality, 30-day readmission, or de-escalation rates. On average, the antimicrobial cost per hospitalization decreased from US$173.03 to US$120.66. Conclusions Pharmacist-led ASP interventions that focus on the length of therapy have the potential to reduce LOS in hospitals without ID specialists. Overall, this study showed that ASPs can be effectively implemented in medium-sized hospitals without ID specialists.
Estimating Electric Vehicle Driving Range with Machine Learning

: In the past years, we have witnessed an increase on the use of electric vehicles (EV), which are now widely accepted as reliable and eco-friendly means of transportation. When choosing an EV, usually one of the key parameters of choice for the consumer is its driving range (DR) capability. The DR depends on many factors that should be addressed when predicting its value. In some cases, the existing heuristic techniques for DR estimation provide values with large variation, which may cause driver anxiety. In this paper, we explore the use of machine learning (ML) techniques to estimate the DR. From publicly available data, we build a dataset with EV data suitable to estimate the DR. Then, we resort to regression techniques on models learned on the dataset, evaluated with standard metrics. The experimental results show that regression techniques perform adequate and smooth estimation of the DR value on both short and long trips, avoiding the need to use the previous heuristic techniques, thus minimizing the drivers anxiety and allowing better trip planning.
The impact of multivariate quasi-flat zones on the morphological description of hyperspectral images

Quasi-fl at zones are powerful morphological image simplification tools capable of producing unique image partitions at multiple scales. In this paper we investigate their impact on the morphological description of hyperspectral images and, specifically, whether they can improve classification performance when used for preprocessing. In particular, we employ them in order to group spatially adjacent pixels according to spectral criteria, prior to the computation of extended morphological profiles. Moreover, we explore both marginal quasi-flat zones and recent multivariate extensions using well-known vector orderings. The method studied is tested with multiple hyperspectral images, where it leads consistently to improvements in classification performance.
Image Quality Assessment in the Context of the Brazilian Electoral System

: The Brazilian electoral system uses an electronic voting machine to increase the voting reliability. This voting machine goes through a series of security procedures, and the one that uses the most human resources is the integrity test. The proposed solution to optimize these resources is using a robotic arm and computer vision methods to replace the eight persons needed to carry out the test currently. However, there is a problem with the LCD screen in the poll worker’s terminal.There is no backlight on the LCD screen, this may cause visual pollution on images captured by the camera, depending on the ambient lighting and camera position. In this way, this paper proposes two methods to make it easier to choose the best images to be used in the extraction of information process: OCR and blur analysis. We analyzed 27 images with three ambient lighting configurations then compared our results with three no-reference image quality evaluators and research on human perception of image quality. The OCR analysis matched very closely the human perception and the other evaluators.
Lightweight Audio-Based Human Activity Classification Using Transfer Learning

: This paper employs the acoustic modality to address the human activity recognition (HAR) problem. The cornerstone of the proposed solution is the YAMNet deep neural network, the embeddings of which comprise the input to a fully-connected linear layer trained for HAR. Importantly, the dataset is publicly available and includes the following human activities: preparing coffee , frying egg , no activity , showering , using microwave , washing dishes , washing hands , and washing teeth . The speciﬁc set of activities is representative of a standard home environment facilitating a wide range of applications. The performance offered by the proposed transfer learning-based framework surpasses the state of the art, while being able to be executed on mobile devices, such as smartphones, tablets, etc. In fact, the obtained model has been exported and thoroughly tested for real-time HAR on a smartphone device with the input being the audio captured from its microphone.
MULTI-MEV ION PRODUCTION FROM HIGH-INTENSITY LASER INTERACTIONS WITH UNDERDENSE PLASMAS

Experiments were performed using high-power laser pulses (greater than 50 TW) focused into underdense helium, neon, or deuterium plasmas $({n}_{e}\ensuremath{\sim}5\ifmmode\times\else\texttimes\fi{}{10}^{19}{\mathrm{cm}}^{\ensuremath{-}3})$. Ions having energies greater than 300 keV were measured to be produced primarily at 90\ifmmode^\circ\else\textdegree\fi{} to the axis of laser propagation. Ion energies greater than 6 MeV were recorded from interactions with neon. Images of the ion emission were also obtained, and it is possible that spatially resolved measurements of the ion energy spectrum can provide a method to estimate the intensity of the focused radiation in the interaction region.
THE ABILITY OF matK AND trnL-trnL-trnF INTERGENIC SPACER TO DISCERN CERTAIN SPECIES ACCESSIONS OF THE FAMILIES SOLANACEAE AND FABACEAE

Indonesia, as a mega-biodiversity country, has many kinds of tropical fruits. Solanaceae and Fabaceae families contain plant species that produce fruits and vegetables for human food, such as ‘rimbang’ (Solanum torvum), ‘terung asam’ (S. ferox), ‘kabau’ (Archidendron microcarpum), ‘jengkol’ (A. pauciflorum), and ‘petai’ (Parkia speciosa). This study aimed to determine the ability of DNA barcodes (matK and trnL-trnL-trnF intergenic spacer [IGS]) in distinguishing some of those tropical fruit plants. Plant materials came from Riau Province, Indonesia. Methods developed include total DNA extraction from fresh leaves and PCR using the primer pairs to amplify matK and trnL-trnL-trnF IGS. The PCR products underwent sequencing, with the DNA sequences analyzed using MEGA6. In this study, the obtained matK and trnL-trnL-trnF IGS sequences showed lengths ranging from 800 bp to 892 bp and 872 bp to 1113 bp, respectively. The matK and trnL-trnL-trnF IGS sequences of S. ferox, A. microcarpum, and A. pauciflorum provided the first sequences reported in those species. Several nucleotide variations in matK sequences emerged among the studied accessions caused by substitution mutation, including transversion and transition. No indel (insertion-deletion) mutation in the studied matK sequences occurred. Indel can also cause variations in trnL-trnL-trnF IGS sequences among the studied accessions aside from substitution mutation. For species S. torvum, S. ferox, and P. speciosa, there came about some decisive nucleotides based on the matK sequences. Moreover, for species S. torvum, S. ferox, A. microcarpum, A. pauciflorum, and P. speciose, there occurred few critical nucleotides based on trnL-trnL-trnF IGS. Also, there was a critical nucleotide for large-fruited ‘jengkol’ (one of the populations in A. pauciflorum) based on trnL-trnL-trnF IGS. However, both DNA barcodes could not differentiate the two types of S. ferox, i.e., ‘terung asam’ and ‘terung bulu. ’ Both DNA barcodes showed to differentiate some species members of both families Solanaceae and Fabaceae into separate clusters. Moreover, compared with matK, the trnL-trnL-trnF IGS indicated uniqueness in discriminating populations within species.
Sewer-AI: Sustainable Automated Analysis of Real-World Sewer Videos Using DNNs

: Automated maintenance of sewer networks using computer vision techniques has gained prominence in the vision-research community. In this work, we handle sewer inspection videos with severe challenges. These obstacles hinder direct application of state-of-the-art neural networks in finding a solution. Thus, we perform an exhaustive study on the performance of highly successful neural architectures on our challenging sewer-video-dataset. For complete understanding we analyze their performance in different modes. We propose training strategies for effectively handling the different challenges and obtain balanced accuracy, F1 and F2 scores of more than 90% for 17 out of the 25 defect categories. Furthermore, for developing resource efficient, sustainable versions of the models we study the trade-off between performance and parameter pruning. We show that the drop in average performance of the networks is within 1% with more than 90% weight pruning. We test our models on the state-of-the-art Sewer-ML-dataset and obtained 100% true positive rate for 8 out of 18 defect categories in the Sewer-ML-dataset.
Visual Question Answering Analysis: Datasets, Methods, and Image Featurization Techniques

: Holistic scene understanding is a long-standing objective of core tenets of Artificial Intelligence (AI). Multi-modal tasks that aim to synergize capabilities spanning multiple domains, such as visual-linguistic capabilities, into intelligent systems are thus a desideratum for the next step in AI. Visual Question Answering (VQA) systems that integrate Computer Vision and Natural Language Processing tasks into the task of answering natural language questions about an image represent one such domain. There is a need to explore Deep Learning techniques that can help to improve such systems beyond the language biases of real-world priors that presently hinder them from serving as a veritable touchstone for holistic scene understanding. Furthermore, the effectiveness of Transformer architecture for the image featurization pipeline of VQA systems remains untested. Hence, an exhaustive study on the performance of various model architectures with varied training conditions on VQA datasets like VizWiz and VQA v2 is imperative to further this area of research. This study explores architectures that utilize image and question co-attention for the task of VQA and several CNN architectures, including ResNet, VGG, EfficientNet, and DenseNet. Vision Transformer architecture is also explored for image featurization, and a myriad of loss functions such as cross-entropy, focal loss, and UniLoss are employed for training the models. Finally, the trained model is deployed using Flask, and a GUI for the same has been implemented that lets users input an image and accompanying questions about the image to generate an answer in response.
High energy density plasmas generated by radial foil explosions

High energy density (HED) plasmas, produced by diverse techniques, such as lasers or pulsed power generators, can help scientists to better understand extreme states of matter as well as astrophysical phenomena. While fast Z-pinches are the most common approach to generating HED plasmas in the pulsed power community, radial foil configurations can yield plasma pressures on the order of 0.5 Mbar on a 1 MA, 100 ns current rise time generator, similar to wire array configurations producing Z-pinches. In this experimental setup, a thin metallic foil stretched onto a circular anode connects to a very small ‘pin’ cathode at the center. Radial currents flow in the foil then down the pin cathode, thereby generating an axi-symmetric toroidal magnetic field under the foil. Initial experimental results (Gourdain et al 2010 Phys. Plasmas 17 012706) showed that the foil current interacts with this field and the resulting J × B force lifts the foil upward. Very rapidly the foil plasma turns into a bubble-shaped cavity above the central pin, and that bubble expands at 300 km s−1, until instabilities destroy the axial symmetry, bursting the bubble open. This paper complements these initial results by using time-integrated x-ray pin-hole cameras and a focusing spectrometer with spatial resolution. In addition to laser interferometry, these new data help to provide a better estimate of the plasma electron density inside the bubble, above 1020 cm−3, and of the electron temperature, between 300 and 400 eV inside the central plasma column. We also discovered the presence of ‘bright’ spots in the plasma, with densities larger than 5 × 1021 electrons cm−3 and temperatures above 1 keV. Finally laser interferometry gives a precise mapping of the initial plasma jet and bubble areal densities.
Estimation of stock market index volatility using the GARCH model: Causality between stock indices

This paper aims to model the volatility of returns for selected stock indices and examine the causal relationships between the markets using the historical daily prices of the Financial Times Stock Exchange (FTSE), Bursa Malaysia Kuala Lumpur Composite Index (KLCI), the Indonesia Stock Exchange Index (LQ45), and the Stock Exchange of Thailand (SET) from January 2008 to November 2019. The study employs univariate GARCH models that are prominent in capturing the volatility clustering of financial instruments in association with the Box–Jenkins methodology for better estimation. Generally, the ARMA-GARCH model is used to capture the volatility series, while the Granger causality test examines the causal directions between the markets. The findings revealed leverage effects on the markets, with the outperformance of the EGARCH in analyzing the empirical properties of stock returns. An initial test that yielded positive correlations suggests the existence of co-movement between the derived volatility series. The study concluded bidirectional causal relationships between the selected markets, and based on the resulting relationships, it is proposed that supervision of markets among the ASEAN members could be advantageous in predicting the corresponding market performance.
Automatic Spine Segmentation in CT Scans

: The segmentation of the spine can be an essential step in computer-aided diagnosis. Current methods aiming to handle this problem generally employ an explicit model of some type. However, to create an adequately robust model, a high amount of properly labeled diverse data is required. This is not always accessible. In this research, we suggest an explicit model-free algorithm for spine segmentation. Our approach utilizes expert algorithms that are built on medical expert knowledge to create a spine segmentation from thoracic CT scans. Our system achieves an IoU (intersection over union) value of 0.7103 ± 0.051 (mean ± std) and a DSC (Dice similarity coefficient) of 0.8295 ± 0.0343 on a subset of the CTSpine1K dataset.
1D-SalsaSAN: Semantic Segmentation of LiDAR Point Cloud with Self-Attention

: Semantic segmentation on the three-dimensional (3D) point-cloud data acquired from omnidirectional light detection and ranging (LiDAR) identiﬁes static objects, such as roads, and dynamic objects such as vehicles and pedestrians. This enables us to recognize the environment in all directions around a vehicle, which is necessary for autonomous driving. Processing such data requires a huge amount of computation. Therefore, methods have been proposed for converting 3D point-cloud data into pseudo-images and executing semantic segmentation to increase the processing speed. With these methods, a large amount of point-cloud data are lost when converting 3D point-cloud data to pseudo-images, which tends to decrease the identiﬁcation accuracy of small objects such as pedestrians and trafﬁc signs with a small number of pixels. We propose a semantic segmentation method that involves projection using Scan-Unfolding and a 1D self-attention block that is on the basis of the self-attention block. As a result of an evaluation using SemanticKITTI, we conﬁrmed that the proposed method improves the accuracy of semantic segmentation, contributes to the improvement of small-object identiﬁcation accuracy, and is sufﬁcient regarding processing speed. We also showed that the proposed method is fast enough for real-time processing.
Finding Similar non-Collapsed Faces to Collapsed Faces Using Deep Learning Face Recognition

: Face recognition is the ability to recognize a person’s face in a digital image. Common uses of face recognition include identity verification, automatically organizing raw photo libraries by person, tracking a specific person, counting unique people and finding people with similar appearances. However, there is no systematic and accurate study for finding a similar non-collapsed face to a given collapsed face. In this paper we focus on the use case of finding people with similar appearances that will help us to find a similar face without a collapse to a collapsed face for dental reconstruction. We used Python’s Open-CV for age and gender classification and face recognition for finding similar faces. Our results provide a set of similar images that can be used for reconstructing the collapsed faces for creating dentures. Thus with the help of a similar non-collapsed face, we can reconstruct a collapsed face for designing effective dentures.
Welcome to the Jungle: A Conceptual Comparison of Reinforcement Learning Algorithms

: Reinforcement Learning (RL) has continuously risen in popularity in recent years. Consequently, multiple RL algorithms and extensions have been developed for various use cases. This makes RL applicable to a wide range of problems today. When searching for suitable RL algorithms to speciﬁc problems, the options are overwhelming. Identifying the advantages and disadvantages of methods is difﬁcult, as sources use conﬂicting terminology, imply improvements to alternative algorithms without mathematical or empirical proof, or provide incomplete information. As a result, there is the chance for engineers and researchers to miss alternatives or perfect-ﬁt algorithms for their speciﬁc problems. In this paper, we identify and explain essential RL properties. Our discussion of different RL concepts allows to select, optimize, and compare RL algorithms and their extensions, as well as reason about their performance.
Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification

: This study proposes a multi-scale attention assembler network (MSAA-Net) for multi-scale pathological image classiﬁcation. The proposed method discovers crucial features by observing each scale and ﬁnding essential scales used for classiﬁcation. To realize this characteristic, we introduce a two-stage feature aggregation mechanism, which ﬁrst assigns the attention weights to useful local regions for each scale and then assigns the attention weights to the scale. The mechanism observes a pathological image from each scale perspective and adaptively determines the essential scale to classify from the observation results. To train the MSAA-Net, we adopt multiple instance learning (MIL), a learning approach for predicting a label corresponding to multiple images. The labeling effort reduces because the MIL trains the classiﬁcation model using diagnoses for whole slide-level images obtained by daily diagnoses of pathologists instead of detailed annotations of the images. We conducted classiﬁcation using two pathological image datasets to evaluate the proposed method. The results indicate that the proposed method outperforms state-of-the-art multi-scale-based methods.
A fragility curve based on the transformation of a random variable

ABSTRACT There are two failure criteria in seismic probabilistic risk assessment of a nuclear power plant to define component failure caused by an earthquake. The first is in terms of peak ground acceleration and peak ground acceleration capacity. The second is in terms of seismic response and component capacity. These criteria are closely related, but they are not equivalent. One can derive a different fragility curve from the second criterion. First, this study analytically shows the relation of these failure criteria, pointing out that a fragility curve may not represent the uncertainty of a failure probability derived from the second criterion. In addition, we propose a probability density function of a failure probability based on the second criterion using the transformation of a random variable method applied to probability density functions of seismic response and component capacity. Then, we derive a new fragility curve based on the derived probability density function. Also, we show that the derived probability density function links to a likelihood function based on a mean fragility curve used in the literature. Finally, we discuss the non-identifiability of uncertainties of response and capacity, and we propose a Bayesian model utilizing a local response to overcome the non-identifiability.
Propagation of Rayleigh Surface Waves in Microstretch Thermoelastic Continua Under Inviscid Fluid Loadings

The present article is aimed at an investigation of the propagation of generalized Rayleigh surface waves in a homogeneous, isotropic, microstretch thermoelastic solid half-space underlying an inviscid liquid half-space or layer of finite thickness, in the context of classical (coupled) and non-classical (generalized) theories of thermoelasticity. The secular equations in close form and isolated mathematical conditions are derived for generalized Rayleigh waves in the considered composite structure after obtaining general wave solutions of the model. The fluid overlying the solid half-space has been successfully modeled as thermal load in addition to normal (hydrostatic pressure) one. Some special cases of dispersion equations have also been deduced and discussed. The analytic expressions for the amplitudes of displacement, microstretch, microrotation and temperature change at the interfacial surface during the Rayleigh wave propagation are also derived. The results have been deduced and compared with the relevant publications available in the literature at the appropriate stages of this work. Finally, the analytical developments have been illustrated numerically for aluminum–epoxy-like material half-space under the action of inviscid liquid (water) half-space or layer of finite thickness. The computer simulated results in respect of phase velocity, attenuation coefficient, specific loss factor of energy dissipation and relative frequency shift due to fluid loadings are presented graphically in normalized form to observe their distinctions from those in the context of the well established theory of coupled thermoelasticity.