Miracle-dz/UniFAR-FTUs huggingface dataset:
Early flexible endoscopic evaluation of swallowing after mechanical thrombectomy in stroke patients

Abstract Objective The aims of the study were to (1) characterize the findings of flexible endoscopic evaluation of swallowing (FEES) in stroke patients undergoing mechanical thrombectomy (MT); (2) analyse the screening performance of the Standardized Swallowing Assessment (SSA); and (3) study the impact of FEES‐defined dysphagia on 3‐month outcomes. Methods This single‐centre study was based on a local registry of consecutive acute ischaemic stroke patients undergoing MT during a 1‐year period. Patients received FEES within 5 days of admission regardless of the result of dysphagia screening. We compared baseline demographic and clinical characteristics of patients with and without FEES‐defined dysphagia. We collected 3‐month modified Rankin Scale (mRS) and individual index values of the European Quality of Life 5 Dimensions (EQ‐5D‐iv). Using univariable and multivariable regression analyses we predicted 3‐month outcomes for presence of dysphagia and for FEES‐defined dysphagia severity. Results We included 137 patients with a median age of 74 years, 43.1% were female, median NIHSS was 12 and successful recanalization was achieved in 92.7%. Stroke‐associated pneumonia occurred in 8% of patients. FEES‐defined dysphagia occurred in 81% of patients. Sensitivity of the SSA as a dysphagia screening was 67%. Presence of dysphagia and increasing severity of dysphagia were independently associated with increasing 3‐month mRS score. Increasing dysphagia severity dysphagia was independently associated with lower EQ‐5D‐iv. Interpretation Early FEES‐defined dysphagia occurs in four in every five patients undergoing MT. SSA has a suboptimal dysphagia screening performance. Presence of dysphagia and increasing dysphagia severity predict worse functional outcome and worse health‐related quality‐of‐life.
Cultivable Microbial Diversity in Domestic Bentonites and Their Hydrolytic Enzyme Production

We have isolated and identified 72 bacterial strains from four bentonite samples collected at the mining areas located in Gyeongsangbuk-do, Republic of Korea, and measured their hydrolytic enzyme (α-amylase, protease, and cellulase) activities to identify the isolates with industrial-use potential. Most of the isolates belonged to the Bacillaceae, with minor portions being from the Paenibacillaceae, Micrococcaceae, and Bacillales Family XII at the family level. Of the strains isolated, 33 had extracellular α-amylase activity, 30 strains produced cellulase, and 35 strains produced protease. Strain MBLB1268, having the highest α-amylase activity, was identified as Bacillus siamensis (0.38 ± 0.06 U/ml). Bacillus tequilensis MBLB1223, isolated from Byi33-b, showed the highest cellulase activity (0.26 ± 0.04 U/ml), whereas Bacillus wiedmannii MBLB1197, isolated from Zdb130-b, exhibited the highest protease activity (54.99 ± 0.78 U/ml). These findings show that diverse bacteria of the Bacillaceae family adhere to and exist in bentonite and are potential sources of industrially useful hydrolytic enzymes.
Fitting of statistical distributions to wind speed data in Malaysia

-This paper investigates the probability distribution of wind speed data recorded in Faculty of Engineering, University Kebangsaan Malaysia. The wind speed data represented in the form of frequency curves show the shape of a potential model. The two-parameter Weibull distribution and lognormal distribution are adopted in this study to fit the wind speed data. The scale and shape parameters were estimated by using maximum likelihood method. The goodness-of-fit tests based on the empirical distribution function (EDF) are conducted to show that the distribution adequately fits the data. It is found from the hypothesis test that, although the two distributions are all suitable for describing the probability distribution of wind speed data, the two-parameter Weibull distribution is more appropriate than the lognormal distribution.
La prescripción de ejercicio físico en pacientes cardíacos: características y estructura del programa

La rehabilitacion cardiaca ha demostrado ser uno de los elementos, en terminos prospectivos, mas eficaces para mejorar la capacidad fisica del paciente cardiaco, disminuir los factores de riesgo, mejorar la calidad de vida, reducir la dosis o numero de medicamentos despues de un evento isquemico y facilitar la incorporacion mas temprana a la actividad laboral en mejores condiciones. Entre los componentes que integran la rehabilitacion cardiaca: educacion, asesoramiento nutricional, asistencia psicologica, orientacion vocacional y/o laboral y ejercicio, este ultimo, adquiere la mayor importancia por ser el componente que de forma mas temprana y efectiva aumenta la capacidad fisica del paciente, proporcionandole seguridad e independencia en sus actividades personales y profesionales, y un mayor grado de responsabilidad y participacion en su entorno social y familiar. En esta revision se presentan las pautas recomendadas por las organizaciones profesionales relacionadas con la rehabilitacion cardiaca mas prestigiosas y confiables del mundo medico, que proponemos sean consideradas en la planificacion, organizacion y ejecucion de los programas de rehabilitacion en nuestro pais, adaptandolas a las condiciones existentes de cada centro donde se realice. Cardiac rehabilitation has proven to be prospectively an effective element in heart disease patients to endurance functional capacity, diminish risk factors, improve quality of life, reduce dosing and number of medications after an acute event and facilitate an early incorporation to daily labor activities in the best of conditions. Of the elements that compose cardiac rehabilitation one can mention; education, nutritional counseling, psychological assistance, vocational orientation and exercise, the latter being of great significance as responsible of an early and effective improvement of physical capacity that leads to self insurance and independence for personal and professional duties with a greater degree of responsibility and participation in social and family environments. In the following review, guidelines from the most important and trusted professional organizations in the field of cardiac rehabilitation are presented along with a proposal for their use in the planning, organizing and execution of cardiac rehabilitation programs in our country, adjusting them to the existing conditions where to be implemented.
Augmented-Reality Waste Accumulation Visualizations

The negative impact humans have on the environment is partly caused by thoughtless consumption leading to unnecessary waste. A likely contributing factor is the relative invisibility of waste: waste produced by individuals is either out of their sight or quickly taken away. Nevertheless, waste disposal systems sometimes break down, creating natural information displays of waste production that can have educational value. We take inspiration from such natural displays and introduce a class of situated visualizations we call augmented-reality waste accumulation visualizations or ARwavs, which are literal representations of waste data embedded in users’ familiar environment. We implemented examples of ARwavs and demonstrated them in feedback sessions with experts in pro-environmental behavior, and during a large tech exhibition event. We discuss general design considerations for ARwavs. Finally, we conducted a study with 20 participants suggesting that ARwavs yield stronger emotional responses than non-immersive waste accumulation visualizations and plain numbers.
Method-Level Bug Prediction: Problems and Promises

Fixing software bugs can be colossally expensive, especially if they are discovered in the later phases of the software development life cycle. As such, bug prediction has been a classic problem for the research community. As of now, the Google Scholar site generates ∼ 113,000 hits if searched with the “bug prediction” phrase. Despite this staggering effort by the research community, bug prediction research is criticized for not being decisively adopted in practice. A significant problem of the existing research is the granularity level (i.e., class/file level) at which bug prediction is historically studied. Practitioners find it difficult and time-consuming to locate bugs at the class/file level granularity. Consequently, method-level bug prediction has become popular in the last decade. We ask, are these method-level bug prediction models ready for industry use? Unfortunately, the answer is no. The reported high accuracies of these models dwindle significantly if we evaluate them in different realistic time-sensitive contexts. It may seem hopeless at first, but encouragingly, we show that future method-level bug prediction can be improved significantly. In general, we show how to reliably evaluate future method-level bug prediction models, and how to improve them by focusing on four different improvement avenues: building noise-free bug data, addressing concept drift, selecting similar training projects, and developing a mixture of models. Our findings are based on three publicly available method-level bug datasets, and a newly built bug dataset of 774,051 Java methods originating from 49 open-source software projects.
Undesirable Biases in NLP: Addressing Challenges of Measurement

As Large Language Models and Natural Language Processing (NLP) technology rapidly develop and spread into daily life, it becomes crucial to anticipate how their use could harm people. One problem that has received a lot of attention in recent years is that this technology has displayed harmful biases, from generating derogatory stereotypes to producing disparate outcomes for different social groups. Although a lot of effort has been invested in assessing and mitigating these biases, our methods of measuring the biases of NLP models have serious problems and it is often unclear what they actually measure. In this paper, we provide an interdisciplinary approach to discussing the issue of NLP model bias by adopting the lens of psychometrics — a field specialized in the measurement of concepts like bias that are not directly observable. In particular, we will explore two central notions from psychometrics, the construct validity and the reliability of measurement tools, and discuss how they can be applied in the context of measuring model bias. Our goal is to provide NLP practitioners with methodological tools for designing better bias measures, and to inspire them more generally to explore tools from psychometrics when working on bias measurement tools. This article appears in the AI & Society track.
Constructing BDI Plans from Optimal POMDP Policies, with an Application to AgentSpeak Programming

Based on the analysis of the hybrid approach BDI-MDP found in the literature, this paper introduces the algorithm policyToBDIplan that builds AgentSpeak plans for BDI agents that obey optimal POMDP policies, presenting an example of an application, using Jason. POMDP policies are mapped into BDI agent plans, following an intention, considering that plans extracted from a POMDP optimal policy are the ones adopted by the BDI agent that selects the plan with the greatest utility, and an optimal strategy reconsideration.
The Impact of Digitalization on Student Academic Performance in Higher Education Case of Jönköping International Business School

As in any other sector, the Covid-19 outbreak has caused many changes in education, and there is a reasonable expectation for this intervention to have a significant impact on the students and their performance. The purpose of this study was to analyse the effects of the digital semester, imposed on students due to Covid-19 outbreak, on student academic success. Using a quasi-experimental methodological approach called dif-in-dif analysis, three empirical models were constructed to analyse if there is an overall effect when comparing our control and treatment groups, as well as if there were any group-specific differences when it comes to the performance across genders and educational levels. The study found a significant negative effect of the digital semester on student academic success, suggesting that students performed significantly worse after the Covid-19 outbreak caused the University to step away from face-to-face teaching and adapt to remote studies. Furthermore, it was found that gender-specific differences do not affect the academic performance of our treatment group; however, female students performed worse when the digital semester was implemented compared to the control group who had both the classes and exams face-to-face. Lastly, Master stud ents were found to perform significantly worse compared to Bachelor students’ when the Covid-19 outbreak caused the education to transfer to the digital environment.
Respiratory management of the preterm newborn in the delivery room

: The survival of preterm infants has improved signiﬁcantly during the past several decades. However, bronchopulmonary dysplasia remains a major morbidity. Preterm infants have both structural and functional lung immaturity compared with term infants, making them more likely to require resuscitation and more vulnerable to developing bronchopulmonary dysplasia. Interventions in the delivery room may affect short-term and long-term outcomes for preterm infants. The paradigm of resuscitation of preterm infants has been changing over the past decade from being interventional and invasive to be observational and gentle. Recent developments in respiratory management of preterm infants in the delivery room include oxygen supplementation and monitoring, alveolar recruitment techniques, noninvasive ventilation, new surfactant preparations, and new techniques for administration of surfactant. Providing nasal continuous positive airway pressure (CPAP) rather than intubating has been identiﬁed as a potentially better practice. Experimental studies have demonstrated that early application of nasal CPAP is protective for the preterm lung and brain compared with mechanical ventilation. Several observational studies have suggested that early nasal CPAP and avoiding intubation leads to reduced oxygen requirements, intubation rates, duration of mechanical ventilation, and may decrease rates of bronchopulmonary dysplasia. Multicenter, randomized controlled trials support the use of nasal CPAP as a primary strategy in preterm babies with respiratory distress syndrome. This approach leads to a reduction in the number of infants who are intubated and given surfactant without an impact on bronchopulmonary dysplasia rates. On the other hand, half of the infants enrolled in these studies failed nasal CPAP treatment. New techniques for surfactant administration include INSURE (“intubate give surfactant and extubate”), administration through a laryngeal mask airway, nebulized surfactant administration, and minimally invasive surfactant therapy. Abbreviations: Supplemental Therapeutic Oxygen for Prethreshold Retinopathy of Prematurity; BOOST, Benefits of Oxygen Saturation Targeting; SUPPORT, Surfactant, positive pressure, and pulse oximetry randomized; SpO 2 , pulse oximetry percent saturation.
Using Machine Learning to Describe How Players Impact the Game in the MLB

Although common across all major sports in today’s day and age, Major League Baseball (MLB) has the most storied history of using statistical analysis to better understand the game, with an entire discipline known as sabermetrics dedicated to the craft. At the end of the day, all of the methods of analysis across sports try to describe some aspect of how an individual player or team impacts the game. In general, an aspect of the game humans deem “important” is discretized , and players or teams are evaluated and described based on the number of times they induce said discrete events, or in some cases keep it from happening. In doing so, the meaning of each discrete event is set in stone - a single is a single regardless of how it happens - and players are described based on the rate at which these events occur while they are in the game. We contend it would be more advantageous to work in the reverse order - learn the meaning of in-game events based on the impact that they have on the game and context in which they occur, then describe how players impact the game with this new understanding in mind. To this end, we draw on recent work in Natural Language Processing (NLP) and Computer Vision (CV) and propose a novel method of describing player form in the MLB - how players impact the game over a short period of time. Concretely, a player’s form is described by a 64-element vector. We show how these vectors can be used to describe players over the short-and long-term, and contain signal useful for predicting the outcome of games.
Frege Systems for Quantiﬁed Boolean Logic

We deﬁne and investigate Frege systems for quantiﬁed Boolean formulas (QBF). For these new proof systems we develop a lower bound technique that directly lifts circuit lower bounds for a circuit class C to the QBF Frege system operating with lines from C . Such a direct transfer from circuit to proof complexity lower bounds has often been postulated for propositional systems, but had not been formally established in such generality for any proof systems prior to this work. This leads to strong lower bounds for restricted versions of QBF Frege, in particular an exponential lower bound for QBF Frege systems operating with AC 0 [ p ] circuits. In contrast, any non-trivial lower bound for propositional AC 0 [ p ]- Frege constitutes a major open problem. Improving these lower bounds to unrestricted QBF Frege tightly corresponds to the major problems in circuit complexity and propositional proof complexity. In particular, proving a lower bound for QBF Frege systems operating with arbitrary P / poly circuits is equivalent to either showing a lower bound for P / poly or for propositional extended Frege (which operates with P / poly circuits). We also compare our new QBF Frege systems to standard sequent calculi for QBF and establish a correspondence to intuitionistic bounded arithmetic.
Foot Fingerprints

A footstep-based identi(cid:12)cation system is proposed in this article. The usage of average acceleration sensors on existing nodes for wireless sensor networks is all that is required, allowing for an easy implementation, without resorting to hardware development. Furthermore, wireless sensor networks permit that this application can take advantage of several techniques such as redundancy, scalability, matching, etc. to increase its robustness. The approach we follow in this paper is based on the strength of the steps and the time between steps for detecting people by their footsteps. The system goes through a learning phase, where patterns for di(cid:11)erent users can be recorded. With this information, during regular operation the system infers if the subject is one of the known users or an intruder.
VMR 2 L: Virtual Machines Rescheduling Using Reinforcement Learning in Data Centers

Modern industry-scale data centers receive thousands of virtual machine (VM) requests per minute. Due to the continual creation and release of VMs, many small resource fragments are scattered across physical machines (PMs). To handle these fragments, data centers periodically reschedule some VMs to alternative PMs. Despite the increasing importance of VM rescheduling as data centers grow in size, the problem remains understudied. We first show that, unlike most combinatorial optimization tasks, the inference time of VM rescheduling algorithms significantly influences their performance, causing many existing methods to scale poorly. Therefore, we develop a reinforcement learning system for VM rescheduling, VMR 2 L, which incorporates a set of customized techniques, such as a two-stage framework that accommodates diverse constraints and workload conditions, as well as an effective feature extraction module. Our experiments on an industry-scale data center show that VMR 2 L can achieve a performance comparable to the optimal solution, but with a running time of seconds. 2
Evidence for Moho‐lower crustal transition depth diking and rifting of the Sierra Nevada microplate

Lithospheric rifting most often initiates in continental extensional settings where “breaking of a plate” may or may not progress to sea floor spreading. Generally, the strength of the lithosphere is greater than the tectonic forces required for rupture (i.e., the “tectonic force paradox”), and it has been proposed that rifting requires basaltic magmatism (e.g., dike emplacement) to reduce the strength and cause failure, except for the case of a thin lithosphere (<30 km thick). Here we isolate two very similar and unprecedented observations of Moho‐lower crustal transition dike or fluid injection earthquake swarms under southern Sierra Valley (SV: 2011–2012) and North Lake Tahoe (LT: 2003–2004), California. These planar distributions of seismicity can be interpreted to define the end points, and cover ~25% of the length, of an implied ~56 km long structure, each striking N45°W and dipping ~50°NE. A single event at ~30 km depth that locates on the implied dipping feature between the two swarms is further evidence for a single Moho‐transition depth structure. We propose that basaltic or fluid emplacement at or near Moho depths weakens the upper mantle lid, facilitating lithospheric rupture of the Sierra Microplate. Similar to the LT sequence, the SV event is also associated with increased upper crustal seismicity. An 27 October 2011, Mw 4.7 earthquake occurred directly above the deep SV sequence at the base of the upper crustal seismogenic zone (~15 km depth).
Review of the Algorithm Selection

The algorithm selection problem consists of choosing, from a predefined set, the best algorithm to run in a given instance of a an optimization problem. Typically, the objective is either to reduce the time to obtain a solution and/or to maximize the quality of this solution. The algorithm selection problem is a rich area that uses many ideas from other fields. In this paper, we make a review of the most significant contributions in this area and try to extrapolate future tendencies. We make an attempt at classifying this sample of works, to give a better perspective of the methods proposed so far. With this objective, we try to unify the terms more commonly used and propose a new nomenclature in some areas. We believe that the proposed taxonomy gives a better perspective of the field and that it will help contextualize future research.
Transformation of soil properties under the impact of oil pollution

A brief review of investigations into the problem of oil pollution of soils and the development of a set of standards to characterize the degree of soil contamination with oil and oil products is given. It is argued that this problem is especially acute for Russia, because the absence of official standards complicates the activity of environmental protection organizations. Quantitative indices of the degree of oil contamination of soils in European countries are listed. The effect of oil pollution on soil properties is studied by the example of a gray forest soil, leached chernozem, and podzolized chernozem. It is shown that oil pollution increases substantially the organic carbon content in the soil, which is due to the carbon of oil products. Under the impact of oil pollution, the relative content of the second fraction of humic acids (the fraction presumably bound with calcium) decreases, as well as the content of the first (free) fraction of fulvic acids. At the same time, the portion of non-hydrolyzable residue (the humin fraction) increases significantly. In general, soil humus becomes enriched in humic acids, whereas the degree of humification of soil organic matter decreases. Oil contamination results in a considerable reduction of spectral reflectance coefficients of the soils; the slope of the soil spectral curve becomes less steep.