Miracle-dz/UniFAR-FTUs huggingface dataset:
Endocarditis in Liver Transplant Recipients: A Systematic Review

Infective Endocarditis (IE) is associated with significant mortality. Interestingly, IE in patients with liver transplantation has not been adequately described. The aim of this review was to systematically review all published cases of IE in liver transplant recipients and describe their epidemiology, microbiology, clinical characteristics, treatment and outcomes. A systematic review of PubMed, Scopus and Cochrane Library (through 2 January 2021) for studies providing epidemiological, clinical, microbiological, treatment data and outcomes of IE in liver transplant recipients was conducted. A total of 39 studies, containing data for 62 patients, were included in the analysis. The most common causative pathogens were gram-positive microorganisms in 69.4%, fungi in 25.8%, and gram-negative microorganisms in 9.7% of cases, while in 9.3% IE was culture-negative. The aortic valve was the most commonly infected valve followed by mitral, tricuspid and the pulmonary valve. Aminoglycosides, vancomycin and aminopenicillins were the most commonly used antimicrobials, and surgical management was performed in half of the cases. Clinical cure was noted in 57.4%, while overall mortality was 43.5%. To conclude, this systematic review thoroughly describes IE in liver transplant recipients and provides information on epidemiology, clinical presentation, treatment and outcomes.
Association of Glycated Albumin/Glycosylated Hemoglobin Ratio with Blood Glucose Fluctuation and Long-Term Blood Glucose Control in Patients with Type 2 Diabetes Mellitus

Objective: This study aimed to investigate the association of the glycated albumin (GA)/ glycosylated hemoglobin (HbA1c) ratio with the mean amplitude of glycemic excursion (MAGE) in type 2 diabetes mellitus (T2DM). Methods: A total of 102 patients with T2DM who were first treated in Jinjiang Hospital of Fujian Province were enrolled in this study. The patients’ general clinical data, including HbA1c, GA, fasting blood glucose, and fasting and peak C-peptide values upon diagnosis and after one year of follow-up, were collected, and their MAGE was calculated. Results: With the increase of the GA/HbA1c ratio at baseline, the patients’ fasting and peak C-peptide values decreased gradually from baseline to follow-up, while their MAGE, HbA1c, and fasting blood glucose increased gradually. A regression analysis demonstrated that the baseline MAGE was independently positively correlated with the GA/HbA1c ratio. A Cox regression analysis demonstrated that a baseline GA/HbA1c ratio of >2.78 was an independent risk factor for poor fasting blood glucose and HbA1c. Conclusion: The GA/HbA1c ratio is closely related to the MAGE and islet function in patients with T2DM.
Augmented Shopping Experience for Sustainable Consumption using the Internet of Things

Abstract Regardless of a possible rise in consumer expectations, research has also shown that the majority of consumers today still place value, product, and convenience in their purchasing decisions. They want to be sure that they found the right product at the right price and that it hasn't. We want to create a better store experience for hypermarket buyers with augmented reality. The design solution to find products, know their availability and the payment experience with a perfect combination of digital technology. Conventional stores have stacked aisles on either side of products with little or no information about the products. This makes shopping a slower and more confusing task. In such cases, customers must be satisfied with all the products available on the shelf. Does not give the customer the opportunity to choose between new and unknown products. The use of augmented reality in this context will improve Customer Experience Augmented Reality can be implemented anywhere with minor environmental improvements. If a supermarket identifier must be used for the location, these identifiers can be any picture. The customer can use their own device Smartphone to take advantage of this experience by simply installing an application on your phone. This app guides the customer through the store with extended notes next to each aisle. When the customer selects a line, the product location is displayed on the phone screen. When the product is pointed towards the camera, the customer can get all the information about the product.
Supplementary Material for Continual Learning Long Short Term Memory

In order to make our experimental results more convincing, we also investigate different orders in the addressed datasets where we test CL-LSTM , LWF and finetune on Exp1 in the reverse order (WR→SNIPS→ATIS). The results are shown in Table 1, while all methods have large performance drop, we found it is due to the forgetting on the largest WR dataset, probably longer training and parameter tuning can alleviate this problem. However, in this reverse order, our method still outperforms others.
Ultra High Energy Cosmic Rays & Super-heavy Dark Matter Observables and prospect of (non)detection

We reanalyse the prospects for upcoming Ultra-High Energy Cosmic Ray experiments in connection with the phenomenology of Super-heavy Dark Matter. We identify a set of observables well suited to reveal a possible anisotropy in the High Energy Cosmic Ray flux induced by the decays of these particles, and quantify their performance via Monte Carlo simulations that mimic the outcome of near-future and next-generation experiments. The spherical and circular dipoles are able to tell isotropic and anisotropic fluxes apart at a confidence level as large as 4σ or 5σ, depending on the Dark Matter profile. The forward-to-backward flux ratio yields a comparable result for relatively large opening angles of about 40 deg, but it is less performing once a very large number of events is considered. We also find that an actual experiment employing these observables and collecting 300 events at 60 EeV would have a 50% chance of excluding isotropy against Super-heavy Dark Matter at a significance of at least 3σ. ∗luca.marzola@ut.ee †federico.urban@kbfi.ee 1 ar X iv :1 61 1. 07 18 0v 1 [ as tr oph . H E ] 2 2 N ov 2 01 6
Advanced Flexible Skin-Like Pressure and Strain Sensors for Human Health Monitoring

Recently, owing to their excellent flexibility and adaptability, skin-like pressure and strain sensors integrated with the human body have the potential for great prospects in healthcare. This review mainly focuses on the representative advances of the flexible pressure and strain sensors for health monitoring in recent years. The review consists of five sections. Firstly, we give a brief introduction of flexible skin-like sensors and their primary demands, and we comprehensively outline the two categories of design strategies for flexible sensors. Secondly, combining the typical sensor structures and their applications in human body monitoring, we summarize the recent development of flexible pressure sensors based on perceptual mechanism, the sensing component, elastic substrate, sensitivity and detection range. Thirdly, the main structure principles and performance characteristic parameters of noteworthy flexible strain sensors are summed up, namely the sensing mechanism, sensitive element, substrate, gauge factor, stretchability, and representative applications for human monitoring. Furthermore, the representations of flexible sensors with the favorable biocompatibility and self-driven properties are introduced. Finally, in conclusion, besides continuously researching how to enhance the flexibility and sensitivity of flexible sensors, their biocompatibility, versatility and durability should also be given sufficient attention, especially for implantable bioelectronics. In addition, the discussion emphasizes the challenges and opportunities of the above highlighted characteristics of novel flexible skin-like sensors.
Learning Action Models from Plan Traces with Disordered Actions, Parallel Actions, Noisy States

There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in many real-world domains. Although there have many learning systems that help automatically learning domain models, most existing work assumes that the input traces are completely correct. A more realistic situation is that the plan traces are disordered and noisy, such as plan traces described by natu-ral language. In this paper we propose and evaluate an approach for doing this. Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces. We use a MAX-SAT framework for learning, where the constraints are derived from the given plan traces. Unlike traditional action models learners, the states in plan traces can be partially observable and noisy as well as the actions in plan traces can be disordered and parallel. We demonstrate the effectiveness of our approach through a systematic empirical evaluation with both IPC domains and the real-world dataset extracted from natural language documents.
Trends of Chitosan Based Delivery Systems in Neuroregeneration and Functional Recovery in Spinal Cord Injuries

Spinal cord injury (SCI) is one of the most complicated nervous system injuries with challenging treatment and recovery. Regenerative biomaterials such as chitosan are being reported for their wide use in filling the cavities, deliver curative drugs, and also provide adsorption sites for transplanted stem cells. Biomaterial scaffolds utilizing chitosan have shown certain therapeutic effects on spinal cord injury repair with some limitations. Chitosan-based delivery in stem cell transplantation is another strategy that has shown decent success. Stem cells can be directed to differentiate into neurons or glia in vitro. Stem cell-based therapy, biopolymer chitosan delivery strategies, and scaffold-based therapeutic strategies have been advancing as a combinatorial approach for spinal cord injury repair. In this review, we summarize the recent progress in the treatment strategies of SCI due to the use of bioactivity of chitosan-based drug delivery systems. An emphasis on the role of chitosan in neural regeneration has also been highlighted.
Microblog topic identification using Linked Open DataI

Much valuable information is embedded in social media posts (microposts) which are contributed by a great variety of persons about subjects that of interest to others. The automated utilization of this information is challenging due to the overwhelming quantity of posts and the distributed nature of the information related to subjects across several posts. Numerous approaches have been proposed to detect topics from collections of microposts, where the topics are represented by lists of terms such as words, phrases, or word embeddings. Such topics are used in tasks like classification and recommendations. The interpretation of topics is considered a separate task in such methods, albeit they are becoming increasingly human-interpretable. This work proposes an approach for identifying machine-interpretable topics of collective interest. We define topics as a set of related elements that are associated by having posted in the same contexts. To represent topics, we introduce an ontology specified according to theW3C recommended standards. The elements of the topics are identified via linking entities to resources published on Linked Open Data (LOD). Such representation enables processing topics to provide insights that go beyond what is explicitly expressed in the microposts. The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events. The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations. Manual evaluation of randomly selected 36 sets of topics yielded 81.0% and 93.3% for the precision and F1 scores respectively.
On Detecting Plant Diseases with Image Processing: A Survey

With the modernization of the agricultural industry, applications of image processing have a considerable impact on this industry. Image processing applications have been used in the early and fast detection of crop and plant diseases. The early detection of diseases enables the people concern to treat the infected crops or plants which results in ensuring good quality and yield of the crops and plants being raised by the farmers. This paper describes the recent studies, techniques, and contributions made by different researchers, to detect plant diseases affecting various plant parts. Current image processing trends and techniques like K-Means, Histogram Equalization, Otsu method, Adam Optimizer, etc. have been put forward in this survey paper to serve the purpose of identifying and detecting diseases and their pathogens. The study also discusses the various diseases and the corresponding image processing applications used by the researchers to identify the various diseases. The goal of this paper is to identify the current trends of the applications of image processing methods to recognize various plant diseases. Techniques like GLCM, GABOR filter, Local Binary Pattern have been used by researchers for the extraction of the features from the images. K-Means, Fuzzy logic, Otsu, etc. have been used for segmentation and clustering. Some of the survey papers include SIFT, Euclidean, and Autoencoders as classifiers.
Impaired spatial memory in mild cognitive impairment due to AD

Kuven K Moodley, Ludovico Minati, Valeria Elisa Contarino, Sara Prioni, Ruth Wood, Rebecca Cooper, Ludovico D’Incerti, FabrizioTagliavini, Dennis Chan Brighton and Sussex Medical School, Brighton, UK U.O. Direzione Scientifica, Fondazione IRCCS Istituto Neurologico “Carlo Besta”, Milano, Italy U.O Neuroradiologia, Fondazione IRCCS Istituto Neurologico “Carlo Besta”, Milano, Italy U.O. Neuropatologia, Fondazione IRCCS Istituto Neurologico“Carlo Besta”, Milano, Italy Brighton and Sussex University Hospitals NHS Trust, UK
STUDY ON AQUIFER PARAMETER IN BASALTIC ROCK WITH THE HELP OF PUMPING EXPERIMENT METHOD USING MODFLOW SOFTWARE IN PURNA RIVER BASIN

The occurrences of groundwater and movement of groundwater is major question to development and working process. The aspect of water run and supply are temporally related to hydrological issue, generally groundwater flow through aquifer but there is variation in rock composition and temperature due to the recharge and discharge of condition which are depend on aquifer flow, geological and hydrological information provide us influential information about the subsurface strata. Study of shallow earth provide major information necessary to answer these questions is not continuously, spring which discharge ground water table indicates study of subsurface in hydrological way. In presence condition detected on the basis of Transmissivity (T) storativity (S). There are many methods use by accurate parameter of aquifer like water analysis, hydrological parameter, pumping aquifer experiment. These parameters are projected execution experiment on aquifer well samples carried in the laboratory. Need to collect the analyzing data on borehole, open well, reservoir and its boundary. Keyword: Groundwater influential, well pumping experiment mudflow software.
THE EFFECT OF CAPITAL STRUCTURE ON THE PROFITABILITY OF NON-BANK FINANCIAL INSTITUTIONS’: AN EMPIRICAL EVIDENCE FROM THE GHANA STOCK EXCHANGE (GSE)

This research aimed to investigate the effects of capital structure on profitability of listed non-bank financial institutions (NBFIs) on the Ghana Stock Exchange (GSE). The research used panel data obtained from the fifteen (15) companies, for the period 2010-2017. In the study, short-term debt (STDTA, long-term debt (LTDTA), firm size (SZ) and liquidity (LQDTY) as a measure for capital structure while Return on Asset (ROA) measure profitability. Result: The study findings show that there is a negative and statistically significant relationship between short-term debt and profitability. However, long-term debt indicates a positive and significant association with the profitability of the firms. The findings of this empirical research are of higher significance for developing nations such as Ghana because it calls for the concentration of firm’s leadership and policymakers to pursue measures that decrease or increase debt dependence in order to achieve optimal capital structure.
Study on Radiological Inventory Assessment of K 1 Reactor Vessel

During decommissioning of nuclear power plant (NPP), it is generally understood that large amount of radioactive wastes are generated. The carefully planned waste management strategy, including packaging, transportation, and disposal, is one of the most important prerequisite for the decommissioning to prevent delay or impede of the process [1]. The accurate radiological inventory assessment comes first to construct the waste management plan. The radiological inventory assessment of activated component usually involves computer codes (Fig. 1.). The monte-carlo based computer code, MCNP, is used to understand the neutron behavior and evaluates the neutron flux and fluence over operation. The ORIGEN calculates the inventory of activated materials by neutron. It is generally accepted that accurate and detailed input of operation history, geometry of component, and materials composition should be provided to achieve accurate radiological inventory of the components.
Usability perception of visual programming language: A case study

Block programming languages are considered as facilitators of programming learning. Programming languages like Scratch are used by adolescents and children from different countries. Usability is an important aspect to facilitate interaction with interfaces, in this case, the interaction and use of the programming language. This work presents a usability perception study of Scratch, based on the System Usability Scale (SUS). The survey involved 96 teenagers and SUS was applied after the experience of using Scratch in the laboratory. The results indicate that Scratch's overall usability is a bit under satisfactory. They also highlight elements to improve.
Plant Leaf Disease prediction using Machine Learning

Agriculture is critical to India's economic growth. Farmers struggle to choose the right fruit and vegetable crop. Disease management by hand is a difficult job. The majority of diseases affect the plant's leaves or stems. As a result, agriculturists must develop effective techniques. To predict the disease, we combined two techniques in this paper. The proposed work's aim is to diagnose disease using image processing and clustering techniques on a photo of diseased plant leaves. Preprocessing begins with the input image. The RGB to L*a*b conversion of the input image of leaves is then performed. Following that, hierarchical clustering is used to segment leaf disease. The predominantly green color pixels are masked after segmentation depending on specific threshold values. Finally, the proposed method's precision was compared to that of other approaches. The suggested procedure was shown to be 92% accurate.
Environment for Translation Domain Adaptation and Continuous Improvement of English-Korean Machine Translation System

This paper presents an environment for rule-based English-Korean machine translation system, which supports the translation domain adaptation and the continuous translation quality improvement. For the purposes, corpus is essential, from which necessary information for translation will be acquired. The environment consists of a corpus construction part and a translation knowledge extraction part. The corpus construction part crawls news articles from some newspaper sites. The extraction part builds the translation knowledge such as newly-created words, compound words, collocation information, distributional word representations, and so on. For the translation domain adaption, the corpus for the domain should be built and the translation knowledge should be constructed from the corpus. For the continuous improvement, corpus needs to be continuously expanded and the translation knowledge should be enhanced from the expanded corpus. The proposed web-based environment is expected to facilitate the tasks of domain adaptation and translation system improvement.
Using Blended Project-Based Learning for Students’ Behavioral Intention to Use and Academic Achievement in Higher Education

This research focused on the potential of project-based learning and blended learning, which offer a distinctive cross-point in regard to building 21st-century skills in the classroom. Specifically, this research hypothesized that using the BPBL approach has an effect on perceived self-efficacy, perceived enjoyment, perceived usefulness, behavioral intention of using BPBL, and students’ academic achievement. To achieve the research goal, we employed a questionnaire as the main data collection method and dispensed it to 80 students, all of whom use the BPBL approach. The findings were obtained via a quantitative research method, structural equation modeling (SEM). We found a significant relationship between the BPBL approach and perceived self-efficacy, perceived enjoyment, perceived usefulness, behavioral intention of using BPBL, and students’ academic achievement. Therefore, we believe that the BPBL approach enhances students’ behavioral intention to use and academic achievement in a blended project-based learning approach and allows sharing knowledge, information, and discussions. Thus, it is recommended that students use the BPBL approach for educational purposes, and they should also be encouraged to do so through their learning at university level.
Deep Learning-Based Sound Localization Using Stereo Signals Based on Synchronized ILD

The interaural level difference (ILD) used for the sound localization using stereo signals is to find the difference in energy that the sound source reaches both ears. The conventional ILD does not consider the time difference of the stereo signals, which is a factor of lowering the accuracy. In this paper, we propose a synchronized ILD that obtains the ILD after synchronizing these time differences. This method uses the cross-correlation function (CCF) to calculate the time difference to reach both ears and use it to obtain synchronized ILD. In order to prove the performance of the proposed method, we conducted two sound localization experiments. In each experiment, the synchronized ILD and CCF or only the synchronized ILD were given as inputs of the deep neural networks (DNN) [1-2], respectively. In this paper, we evaluate the performance of sound localization with mean error and accuracy of sound localization. Experimental results show that the proposed method has better performance than the conventional methods.
ALMOST SURE AND COMPLETE CONSISTENCY OF THE ESTIMATOR IN NONPARAMETRIC REGRESSION MODEL FOR NEGATIVELY ORTHANT DEPENDENT RANDOM VARIABLES

In this paper, the author considers the nonparametric regression model with negatively orthant dependent random variables. The wavelet procedures are developed to estimate the regression function. For the wavelet estimator of unknown function g(·), the almost sure consistency is derived and the complete consistency is established under the mild conditions. Our results generalize and improve some known ones for independent random variables and dependent random variables.