Miracle-dz/UniFAR-FTUs huggingface dataset:
Primary PCI in STEMI: The Role of Thromboaspiration

Acute myocardial infarction is a potentially life-threatening disease that even in the current device-oriented era remains a challenge for the interventional cardiologist. The underlying pathological mechanism has been shown to be atherosclerotic plaque rupture or erosion with subsequent thrombus formation, which leads to coronary artery occlusion and myocardial necrosis. In order to restore blood flow in the artery and prevent the detrimental effect of myocardial muscle loss, two strategic approaches have been shown to be mandatory. First of all it is pivotal to open the occluded artery and secondly it is imperative to prevent further fibrin deposition and platelet activation and aggregation. The latter is mainly achieved by giving oral antiplatelet therapy with aspirin and a P2Y12 receptor inhibitor and administering an intravenous antithrombotic agent, such as heparin or bivalirudin. However, thrombus removal from the infarct related artery and reperfusion of the damaged myocardium has been proven to be a more complex issue. This is due to the fact that, even though the hypothesis of thrombotic material extraction which could result in better clinical outcome seems appealing and logical, the available studies have shown surprisingly conflicting results. Several mechanisms have been proposed to explain reperfusion failure during primary percutaneous coronary interventions (pPCI), despite the use of dedicated devices that remove thrombotic debris, leading to microvascular obstruction and myocardial injury.
A simple descriptor for magnetic classification of 2D MXene materials

Classification of the magnetic state is an essential step to investigate two-dimensional magnetic materials. Combining high-throughput calculations and machine-learning methods, we have classified the magnetic states of 23 825 MXenes in the aNANt database. A simple descriptor, obtained by averaging the product of the element feature, connectivity, and Coulomb matrix, was found to improve the performance of the machine-learning models. Using this descriptor on 4153 data produced using first-principles calculations, predictive machine-learning models were developed and 1432 MXene with a high saturation magnetization were predicted. The proposed descriptor is useful for the magnetic classification of other materials, and the identified magnetic MXene materials can be used as an important reference for further study.
ISOLATION, MOLECULAR CHARACTERIZATION, AND ANTIMICROBIAL ACTIVITY OF THE MEMBERS OF THE FAMILY BACILLACEAE OBTAINED FROM SOIL SAMPLES

The soil is a very rich environment in terms of microorganism diversity and the microorganisms in the soil are the source for many secondary metabolites. Bacteria in the family Bacillaceae are commonly found in the soil and can maintain their vitality for many years because of their spore production. At the same time, the species in this family can produce secondary metabolites which have different functions. In this study, Bacillus or Bacillus-like bacteria were isolated from soil samples in wheat fields and they were identified based on their morphological and molecular features using 16S rRNA gene sequencing. In addition, the antimicrobial activities of the isolates were investigated against various important human pathogens. A total of twenty bacteria were isolated and all of them were identified as Bacillus sp. , except for ES-18 which is Lysinibacillus xylanilyticus. Six isolates showed antibacterial activity at different levels and the most effective isolate, ES-18, showed antimicrobial activity against Bacillus cereus, B. megaterium, B. subtilis, Stapyhlococcus aureus, Enterococcus faecalis and Listeria monocytogenes. MIC values of the isolate ES-18 were also determined against the related bacteria and the highest dilution factor was determined to be 2-1. It was also determined that all effective dilutions were bacteriostatic. This is the first study showing antimicrobial properties of L. xylanilyticus and the results obtained from this study might be important for the discovery of new antimicrobial compounds.
Integration of reflection seismic data into the documentation during the construction of the Brenner Base Tunnel

The 64 km long Brenner Base Tunnel will be the longest railway tunnel in the world when complete. As part of this mega project, an exploratory tunnel is currently under construction with a tunnel boring machine (∅ 8 m). Even though a geological model along the tunnel route was generated beforehand from the results of geological field mapping and deep drilling campaigns from the surface, some uncertainties persist due to the high overburden of up to 1,300 m. In particular, several fault systems intersect the tunnel route, but their position and orientation is uncertain. The excavation work is being documented with geological and geotechnical data, continuously updating the current geological model. Continuous seismic prediction measurements are integrated into this revision process. The aim is the detection, location and characterization of (sub‐)perpendicular to (sub‐)parallel fault zones ahead and to the side of the tunnel, based on the principle of body wave propagation. In a case study of a seismic campaign, the smooth integration of the acquisition into the regular advance is presented and the obtained 3D seismic models are compared to the geological documentation of the site geologists. It will be shown how the seismic analysis gives an added value for the advance and how it benefits the geological forecast for the forthcoming main tunnels.
The SLI-SC Mathematical Model of African Swine Fever Transmission among Swine Farms: The Effect of Contaminated Human Vector

In this paper, a mathematical model for African swine fever is modified by considering the swine farm with the contaminated human vector that is able to infect and spread the disease among swine farms. In the developed model, we have divided the swine farm density into three related groups, namely the susceptible swine farm compartment, latent swine farm compartment, and infectious swine farm compartment. On the other hand, the human vector population density has been separated into two classes, namely the susceptible human vector compartment and the infectious human vector compartment. After that, we use this model and a quarantine strategy to analyze the spread of the infection. In addition, the basic reproduction number R0 is determined by using the next-generation matrix, which can analyze the stability of the model. Finally, the numerical simulations of the proposed model are illustrated to confirm the results from theorems. The results showed that the transmission coefficient values per unit of time per individual between the human vector and the swine farm resulted in the spread of African swine fever.
Quality of life in older adults with heart failure: assessment with a specific instrument

Objective: To assess the quality of life in elderly patients with heart failure. Methods: Cross-sectional study conducted in two university hospitals. Interviews were conducted with 170 elderly patients with heart failure in an outpatient setting. To assess the quality of life, the Minnesota Living With Heart Failure Questionnaire was used. Results: The results showed a minor impact of illness on the emotional dimension of quality of life. However, indicators related to the physical dimension, including fatigue and breathlessness, showed a greater impact on quality of life for elderly patients. The internal consistency of the instrument-specific to quality of life was high, indicating satisfactory reliability (α> 0.80). Conclusion: This study enabled the assessment of quality of life of elderly patients living with heart failure, demonstrating that the physical dimension was the most compromised. It is essential to implement strategies to improve physical functioning in these patients.
Fabrication and properties of graphene oxide and reduced graphene oxide reinforced Poly(Vinyl alcohol) nanocomposite films for packaging applications

Petroleum-based polymers have a wide range of uses in the packaging industry, including films, food packaging, rigid containers, foamed containers, medical products, etc. This study focuses on improving the properties of polyvinyl alcohol (PVA) based material systems, one of the most popular water-soluble biopolymers, to reduce the waste of petroleum-based plastics, which are widely used. Polyvinyl alcohol (PVA) nanocomposite films containing low concentrations (0.05–1%) of graphene oxide (GO) and reduced graphene oxide (rGO) were produced via the solution casting method. When electrical properties of nanocomposite films were evaluated, the addition of 1 wt% GO and rGO resulted in a 36% and 45% decrease in the volumetric resistance of the films as well as a decrease in surface resistance of 24.5% and 34.9%, respectively. The maximum tensile strength of 1% GO and rGO-reinforced PVA nanocomposites was found to be ∼59 MPa and ∼68 MPa with an increase of 20% and 38% compared to neat PVA films, respectively. The average Young’s modulus of 1% GO and rGO-reinforced PVA nanocomposites also increased from 2561 MPa to 3515 MPa and 4219 MPa with an increase of 37% and 65%, respectively. As a result, the electrical conductivity, Young’s modulus, maximum tensile strength, thermal properties, and optical absorption of nanocomposite films increased by adding GO and rGO to PVA. The results show that the produced nanocomposite film may be a promising material for packaging applications.
A context-aware improved POR protocol for Delay Tolerant networks

ABSTRACT The intermittent network connectivity and unawareness of global network knowledge are remarkable challenges when designing efficient Position-based Opportunistic Routing (POR) for Delay Tolerant Networks (DTNs). The best progress set selection of POR effectively handles the intermittent connectivity issue and improves the reliable performance of DTNs. Hence, the optimal progress set selection and catching capacity decide the routing reliability of DTN-POR. This paper proposes a context-aware DTN-POR protocol STAP to ensure reliable DTN routing with minimum overhead STAP design is divided into three folds: application diversity and network failures, context-aware best progress set selection, and enhanced caching management. The application diversity and network failure switch the nodes in harsh environments to DTN mode for transmitting the data packets successfully to the destination. Selecting context-aware relay nodes ensures highly successful data transmissions in the absence of end-to-end connectivity. The probability estimation maximizes the data reachability by considering unique node and application-level context attributes in the best progressive set. Finally, the enhanced catching management strategy limits the number of data copies with effective catch invalidation, resulting in minimum overhead. The proposed STAP accomplishes better results in different scenarios and improves the packet delivery ratio with a minimum duplication rate.
Parallel Classification Method of Shapelet for Large-scale Time Series

—Time series shapelet are subsequences of time series that can maximally represent a class, the quality of shapelet set is the key of time series classification algorithms based on shapelet, which have high accuracy and good interpretability mostly. However, due to the large number of shapelet candidate sets that need to be traversed in the process of calculating shapelet, its drawback of high time complexity makes it difficult to use traditional methods in large-scale data sets. Therefore, in order to be able to improve shapelet algorithm on the time complexity of large-scale data set and reservation of calculation accuracy meanwhile, in this paper, a new method was suggested to change the traditional shapelet algorithm with parallel computing, through the combination of clustering and sampling method, making the large time series data set into several small samples. Then get their candidate set of shapelet by parallel computing, finally through the merge algorithm candidates were calculated according to the original data set into the most discrimination shapelet collections. Fifteen large scale UCR data sets are selected in the experiment to verify the algorithm. Through comparison experiments, it can be shown that this method can greatly reduce the training time on most time series data sets, and effectively improve the classification accuracy of the time series classification algorithm based on shapelet.
Impact of numerical solution approach of a plant hydrodynamic model on vegetation dynamics

Numerous plant hydrodynamic models have started to be implemented in vegetation dynamics models, reflecting the central role of plant hydraulic traits in driving water, energy and carbon cycle, as well as plant adaptation to climate change. Different numerical approximations 10 of the governing equations of the hydrodynamic models have been documented, but the numerical accuracy of these models and its subsequent effects on the simulated vegetation function and dynamics have rarely been evaluated. Using different numerical solution methods (including implicit and explicit approaches) and vertical discrete grid resolutions, we evaluated the numerical performance of a plant hydrodynamic module in the Functionally Assembled 15 Terrestrial Ecosystem Simulator (FATES-HYDRO) based on single point and global simulations. Our simulation results showed that when near-surface vertical grid spacing is coarsened (grid size > 10 cm), the model significantly overestimates above ground biomass (AGB) in most of the temperate forest locations, and underestimates AGB in the boreal forest locations, as compared to a simulation with finer vertical grid spacing. Grid coarsening has a 20 small effect on AGB in the tropical zones of Asia and South America. In particular, coarse surface grid resolution should not be used when there are large and prolonged water content difference among soil layers at depths due to long dry season duration and/or well-drained soil, or when soil evaporation is a dominant fraction of evapotranspiration. Similarly, coarse surface grid resolution should not be used when there is lithologic discontinuity along the soil depth. 25 This information is useful for uncertainty quantification, sensitivity analysis, or training surrogate models to design the simulations when computational cost limits the use of ensemble simulations. https://doi.org/10.5194/gmd-2022-105 Preprint. Discussion started: 3 June 2022 c © Author(s) 2022. CC BY 4.0 License.
A Fine Motor Skill Classifying Framework to Support Children's Self-Regulation Skills and School Readiness

Children’s self-regulation skills predict their school-readiness and social behaviors, and assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. Assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. To assess children’s fine motor skills, current educators are assessing those skills by either determining their shape drawing correctness or measuring their drawing time durations through paper-based assessments. However, the methods involve human experts manually assessing children’s fine motor skills, which are time consuming and prone to human error and bias. As there are many children that use sketchbased applications on mobile and tablet devices, computer-based fine motor skill assessment has high potential to solve the limitations of the paper-based assessments. Furthermore, sketch recognition technology is able to offer more detailed, accurate, and immediate drawing skill information than the paper-based assessments such as drawing time or curvature difference. While a number of educational sketch applications exist for teaching children how to sketch, they are lacking the ability to assess children’s fine motor skills and have not proved the validity of the traditional methods onto tablet-environments. We introduce our fine motor skill classifying framework based on children’s digital drawings on tablet-computers. The framework contains two fine motor skill classifiers and a sketch-based educational interface (EasySketch). The fine motor skill classifiers contain: (1) KimCHI: the classifier that determines children’s fine motor skills
Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities

As for other forms of AI, speech recognition has recently been examined with respect to performance disparities across different user cohorts. One approach to achieve fairness in speech recognition is to (1) identify speaker cohorts that suffer from subpar performance and (2) apply fairness mitigation measures targeting the cohorts discovered. In this paper, we report on initial findings with both discovery and mitigation of performance disparities using data from a product-scale AI assistant speech recognition system. We compare cohort discovery based on geographic and demographic information to a more scalable method that groups speakers without human labels, using speaker embedding technology. For fairness mitigation, we find that oversampling of underrepresented cohorts, as well as modeling speaker cohort membership by additional input variables, reduces the gap between top- and bottom-performing cohorts, without deteriorating overall recognition accuracy.
Cardinals Beyond Choice and the HOD-Dichotomy

In the 2019 paper “Large Cardinals Beyond Choice” [1], Bagaria, Koellner and Woodin apply the large cardinal techniques and results developed from Woodin’s work on the HOD-Dichotomy to determine the structural resemblance of HOD to V . Whereas standard inner model theory attempts to ﬁnd suitable inner models for large cardinals, this new program is aimed at exploring very large cardinals that “break” the resemblance of HOD to V . This paper attempts to explain in full detail the tools and arguments required for that body of work.
Hybrid Recommendation Engine using Feature-Combination

— An abundant amount of information is created and distributed over electronic media. Recommendation systems use various filters to extract required information. In every aspect of our life, recommendation engine is used to make such a task easier. We proposed a hybrid filtering engine for books data by using the combination of filters. We compared the proposed approach with existing approaches using algorithms, and found that our approach outperformed existing methods in terms of accuracy and robustness. Hybrid system combines the two or more filters to gain better accuracy and performance. The proposed recommendation engine is able to make a large number of recommendations simultaneously, and fit the partial dataset to the appropriate model to have better performance. The engine is able to determine user’s personality traits by utilizing the book’s features. Three types of scenarios are covered here. If user is new then recommendations are made depending upon user interests, second is recommendations based on past purchase history and the last is recommendation by using different algorithms and cosine similarity.
Empirical Study of Randomized Algorithms for Low-rank Approximation

In 2011, Halko, Martinsson, and Tropp published a survey presenting the theory behind and performance of fast randomized algorithms for low-rank matrix decomposition. The current paper discusses an empirical study to evaluate their work, including checking error bounds and exploring possibility to strengthen the bounds, testing failure probabilities, and finding factors that empirically influence the quality of algorithm but are not mentioned in their paper
National Water Model for Drought Monitoring: A Preliminary Evaluation

As one key innovation in the NOAA hydrological modelling, National Water Model (NWM) was recently upgraded to v2.0 in June 2019. The NWM could provide not only the streamflow prediction for hydrological guidance, but also the real-time high-resolution land state analysis and assimilation. Based on the NWM v2.0 retrospective analysis from 1993 to 2018, we evaluated NWM soil moisture (SM) and evapotranspiration (ET) for the drought monitor application. The Soil Moisture Percentile (SMP) from NWM is compared with the official US drought monitor (USDM) map in major drought events. The drought categories Dx based on NWM, is quantitatively compared with similar drought monitor from the North American Land Data Assimilation System Phase 2 (NLDAS2) ensemble. A long time-series soil moisture monitor from Climate Prediction Center (CPC) leaky bucket model is also compared against NWM, to distinguish the importance of the long temporal record versus high spatial resolution for drought monitor. The Evaporative Stress Index (ESI) based on ET estimation from NWM is also assessed for the rapid drought development, i.e. flash drought, to evaluate evapotranspiration for the drought development. The preliminary results indicated the NWM could well capture the major droughts during 2000 to 2018 and 2019 Southeast flash drought, showing great potential in the future application for drought monitor.
A Collaborative Mobile Sensing Framework using Mobile CloudComputing

The ubiquity of mobile devices, tablets, wearables and other smart devices, with ever-more capable embedded sensors, has accelerated the use of mobile sensing applications. These applications harness the processed sensor data to offer a more context-aware and personalized user-experience for purposes including healthcare, entertainment, environmental monitoring and promoting socialization. However, the resource limitation of mobile devices creates a significant bottleneck for the realization of such applications. In this scenario, Mobile Cloud Computing (MCC) is a promising solution that allows the offload of raw or pre-processed sensed data to the application logic hosted in the cloud. This research utilizes the scalability and processing capabilities of MCC to present an intelligent framework that readily supports collaborative sensing and provides efficient collection of the sensed data, whilst conserving the energy of the mobile devices and meeting the performance constraints of applications. The benefits of this collaborative sensing framework include the capability to satisfy multiple mobile applications with optimal sensing using minimal number of mobile devices, context-driven reporting of sensed data to the application cloud, and achieving all of this in an energy efficient manner.
Disaster Response Aided by Tweet Classification with a Domain Adaptation Approach

Social media platforms such as Twitter provide valuable information for aiding disaster response during emergency events. Machine learning could be used to identify such information. However, supervised learning algorithms rely on labelled data, which is not readily available for an emerging target disaster. While labelled data might be available for a prior source disaster, supervised classifiers learned only from the source disaster may not perform well on the target disaster, as each event has unique characteristics (e.g., type, location, and culture) and may cause different social media responses. To address this limitation, we propose to use a domain adaptation approach, which learns classifiers from unlabelled target data, in addition to source labelled data. Our approach uses the Naive Bayes classifier, together with an iterative Self-Training strategy. Experimental results on the task of identifying tweets relevant to a disaster of interest show that the domain adaptation classifiers are better as compared to the supervised classifiers learned only from labelled source data.