Miracle-dz/UniFAR-FTUs huggingface dataset:
FrenLyS: A Tool for the Automatic Simplification of French General Language Texts

Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture. To evaluate the different methods, a new evaluation dataset for French is introduced.
A Landscape of Lignocellulosic Biopolymer Transformations into Valuable Molecules by Heterogeneous Catalysis in C’Durable Team at IRCELYON

This review article highlights part of the research activity of the C’Durable team at IRCELYON in the field of sustainable chemistry. This review presents a landscape of the work performed on the valorization of lignocellulosic biopolymers. These studies intend to transform cellulose, hemicellulose and lignin into valuable molecules. The methodology usually consists in evaluating the behavior of the biopolymers in the absence of catalyst under various conditions (solvent, temperature), and then to assess the influence of a catalyst, most often a heterogeneous catalyst, on the reactivity. The most significant results obtained on the upgrading of cellulose and lignin, which have been mainly investigated in the team, will be presented with an opening on studies involving raw lignocellulose.
A Spatial Analysis of Tuberculosis Related Mortality in South Africa

Background: South Africa, with an estimated annual tuberculosis (TB) incidence of 360,000 cases in 2019, remains one of the countries with the largest burden of TB in the world. The identification of highly burdened TB areas could support public health policy planners to optimally target resources and TB control and prevention interventions. Objective: To investigate the spatial epidemiology and distribution of TB mortality in South Africa in 2010 and its association with area-level poverty and HIV burden. Methods: The study analysed a total of 776,176 TB deaths for the period 2005–2015. Local and global and spatial clustering of TB death rates were investigated by Global and Local Moran’s Indices methods (Moran’s I). The spatial regression analysis was employed to assess the effect of poverty and HIV on TB mortality rates. Results: There was a significant decrease in TB mortality rate, from 179 per 100,000 population in 2005 to 60 per 100,000 population in 2015. The annual TB mortality rate was higher among males (161.5 per 100,000 male population; (95% confidence interval (CI) 132.9, 190.0) than among females (123.2 per 100,000 female population; (95% CI 95.6, 150.8)). The 35–44 age group experienced higher TB mortality rates, regardless of gender and time. Hot spot clusters of TB mortality were found in the South-Eastern parts of the country, whereas cold spot clusters were largely in the north-eastern parts. Tuberculosis death rates were positively associated with poverty, as measured by the South African Multidimension Poverty Index (SAMPI) as well TB death rates in the neighbouring districts. Conclusion: The findings of this study revealed a statistically significant decrease in TB deaths and a disproportionate distribution of TB deaths among certain areas and population groups in South Africa. The existence of the identified inequalities in the burden of TB deaths calls for targeted public health interventions, policies, and resources to be directed towards the most vulnerable populations in South Africa.
Inherent Stability of Multibody Systems with Variable-Stiffness Springs via Absolute Stability Theory

In this paper, the inherent stability problem for multibody systems with variable-stiffness springs (VSSs) is studied. Since multibody systems with VSSs may consume energy during the variation of stiffness, the inherent stability is not always ensured. The motivation of this paper is to present sufficient conditions that ensure the inherent stability of multibody systems with VSSs. The absolute stability theory is adopted, and N-degree-of-freedom (DOF) systems with VSSs are formulated as a Lur’e form. Furthermore, based on the circle criterion, sufficient conditions for the inherent stability of the systems are obtained. In order to verify these conditions, both frequency-domain and time-domain numerical simulations are conducted for several typical low-DOF systems.
RAMANI: Uma Ferramenta de Apoio à Colaboração durante a Execução de Estudos Sistemáticos

Systematic mapping is one type of secondary study that aims to summarize and synthesize the current state of the art of a specific area, providing a general map of this area. It is a technique that requires the execution of a series of error-prone steps. Because of that, this technique is often performed collaboratively. Despite that, there is a lack of tools to support collaboration in this process. This paper presents RAMANI, a tool that aims to support collaboration among stakeholders performing systematic mapping studies. The collaborative aspects of the tool are described as well as an initial evaluation of the tool that was realized through semi-structured interviews.
What Is the Relationship between Online Sex Workers and Cybersecurity?

Although online sex work has become more accessible to people of all socio-economic statuses, labor practices and work safety have not improved since the widespread use of the internet. One way that we can help empower sex workers is to understand their motivations and experiences when using the internet. In a survey conducted by Sanders et al. (2017), the highest crime that 56.2% sex workers experienced was being threatened or harassed through texts, calls, and emails. Because there is no theory application to date on this marginalized group, three theories were proposed. This literature review highlights the need to explore why sex workers, as end-users, should be included in the user cybersecurity defense conversation, such as the cybercrimes that they face, their relationship with law enforcement, and what other factors affect their safety.
Auricular stimulation for preoperative anxiety - A systematic review and meta-analysis of randomized controlled clinical trials.

STUDY OBJECTIVE Previous randomized controlled trials (RCTs) suggest that auricular stimulation (AS) is safe and effective in treatment of preoperative anxiety; however, a systematic evaluation is lacking. The aim was to summarize the evidence on efficacy and safety of AS for preoperative anxiety, as well as for other outcomes. DESIGN We conducted a systematic review of RCTs including patients from all available populations. The search was done through MEDLINE (PubMed), EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), ISI Web of Science and Scopus Database from inception to June 2020. Study selection and data extraction were performed in by 2 independent reviewers with ability to resolve disagreements by a third author. Meta-analyses as well as the risk of bias and evidence quality assessments were performed according to the Cochrane 6.2, 2021 handbook recommendations. INTERVENTIONS We compared AS with pharmacological and non-pharmacological interventions for different outcomes. MEASUREMENTS We assessed the repercussion of the evaluated interventions over anxiety scores and their safety, physiological parameters, perioperative medications requirement and intensity of postoperative pain. MAIN RESULTS We have included 15 studies with 1603 patients. AS has presented reduced anxiety scores as compared to the sham control (Standardized Mean Difference (SMD) -0.72, 95% confidence interval (CI) -1.09 to -0.36, p < 0.0001; 8 trials; 701 patients; heterogeneity: I2 80%; GRADE: moderate certainty) and to no intervention (SMD -1.01, 95% CI -1.58 to -0.45, p = 0.0004; 4 trials; 420 patients; heterogeneity: I2 84%; GRADE: very low certainty). There was no difference between AS and benzodiazepines (SMD -0.03; 95% CI: -0.34 to 0.28; p = 0.84; 3 trials; 158 patients; heterogeneity: I2 0%; GRADE: very low certainty). No trials reported serious adverse effects of AS. CONCLUSIONS AS may be useful in treatment of preoperative anxiety. Due to heterogenous certainty in effect estimates, further research is needed to clarify the actual efficacy of AS for preoperative anxiety.
Real-Time Monitoring of Levetiracetam Effect on the Electrophysiology of an Heterogenous Human iPSC-Derived Neuronal Cell Culture Using Microelectrode Array Technology

Levetiracetam (LEV) is a broad-spectrum and widely used antiepileptic drug that also has neuroprotective effects in different neurological conditions. Given its complex interaction with neuronal physiology, a better comprehension of LEV effects on neurons activity is needed. Microelectrode arrays (MEAs) represent an advanced technology for the non-invasive study of electrophysiological activity of neuronal cell cultures. In this study, we exploited the Maestro Edge MEA system, a platform that allows a deep analysis of the electrical network behavior, to study the electrophysiological effect of LEV on a mixed population of human neurons (glutamatergic, GABAergic and dopaminergic neurons, and astrocytes). We found that LEV significantly affected different variables such as spiking, single-electrode bursting, and network bursting activity, with a pronounced effect after 15 min. Moreover, neuronal cell culture completely rescued its baseline activity after 24 h without LEV. In summary, MEA technology confirmed its high sensitivity in detecting drug-induced electrophysiological modifications. Moreover, our results allow one to extend the knowledge on the electrophysiological effects of LEV on the complex neuronal population that resembles the human cortex.
Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.
APPLICATION OF SPECIAL FILTERING TECHNIQUES IN THE ANALYSIS OF EMAT DATA

The applicability of Electromagnetic Acoustic Transducers (EMAT) for downhole applications in the oil and gas industry is being currently investigated. This application, when compared to conventional usage of EMAT for pipeline inspection, imposes significant engineering and data processing challenges due to difficult downhole conditions, wide variability of casing sizes (both in diameter and thickness) and signal to noise ratio (SNR) limitations. In this paper the investigation of different filtering techniques and methods aimed at analyzing EMAT data for various downhole scenarios, separation and detection of different modes and improvement of SNR is detailed. The techniques being investigated are frequency (FIR) filtering, Gaussian wavelet decomposition, synchronous detection and their combination. The methods and techniques proposed are confirmed and validated based on the results obtained from the numerical simulations and experiments with physical models.
Using Observer Ratings to Assess Situation Awareness

Abstract : This chapter reviews the development and use of observational measures to assess situation awareness among fighter pilots. The chapter begins with a general description of air combat and situation awareness. The next two sections summarize the general approach and the results of this effort. The chapter concludes with a discussion of the advantages and disadvantages with this approach and some general comments on the problems involved in measuring concepts that are as ill-defined as situation awareness (SA).
Improved performance of stepped impedance resonator low‐pass filter by using defect ground structure

A stepped impedance resonator (SIR) low‐pass filter using defect ground structure (DGS), which has high isolation and compact size, is proposed. The proposed defect ground structure (DGS) provides excellent cutoff and stopband characteristics for improving the stopband rejection and the attenuation rate of the bandpass edge. In order to illustrate the improved performance, we compare the conventional low‐pass filter and the proposed novel low‐pass filter with DGS. The measurement results of the fabricated low‐pass filter show excellent agreement with the theoretical results. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 43: 382–384, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20477
Effect of Breastfeeding and Its Duration on Impaired Fasting Glucose and Diabetes in Perimenopausal and Postmenopausal Women: Korea National Health and Nutrition Examination Survey (KNHANES) 2010–2019

Background and Objectives: To examine the effect of maternal breastfeeding on the subsequent risk of diabetes in parous Korean women aged >50 years. Materials and Methods: A total of 14,433 participants from the Korea National Health and Nutrition Examination Survey (KNHANES) were included. The subjects were divided into three groups: normal, impaired fasting glucose, and diabetes. The adjusted odds ratios (ORs) for impaired fasting glucose (IFG) and diabetes were assessed using multivariate logistic regression. Results: A total of 2301 (15.94%) women were classified as having diabetes, and 3670 (25.43%) women were classified as having impaired fasting glucose. Breastfeeding was associated with an OR for diabetes of 0.76 (95% confidence interval (CI): 0.61, 0.95) compared with non-breastfeeding after adjustment for possible confounders in the multivariable logistic regression analysis. Breastfeeding for 13–24 months was associated with an OR of 0.68 (95% CI, 0.5, 0.91), and breastfeeding for 25–36 months was associated with an OR of 0.68 (95% CI, 0.52, 0.87) for diabetes compared with breastfeeding for <1 month in the multivariable logistic regression analysis. Conclusions: Our results suggest that long-term breastfeeding, particularly breastfeeding for 13–36 months, may be associated with a lower risk for diabetes later in life.
Y-Net: Dual-branch Joint Network for Semantic Segmentation

Most existing segmentation networks are built upon a “U-shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “U-shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “U-shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.
Autophagy in α-Synucleinopathies—An Overstrained System

Alpha-synucleinopathies comprise progressive neurodegenerative diseases, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). They all exhibit the same pathological hallmark, which is the formation of α-synuclein positive deposits in neuronal or glial cells. The aggregation of α-synuclein in the cell body of neurons, giving rise to the so-called Lewy bodies (LBs), is the major characteristic for PD and DLB, whereas the accumulation of α-synuclein in oligodendroglial cells, so-called glial cytoplasmic inclusions (GCIs), is the hallmark for MSA. The mechanisms involved in the intracytoplasmic inclusion formation in neuronal and oligodendroglial cells are not fully understood to date. A possible mechanism could be an impaired autophagic machinery that cannot cope with the high intracellular amount of α-synuclein. In fact, different studies showed that reduced autophagy is involved in α-synuclein aggregation. Furthermore, altered levels of different autophagy markers were reported in PD, DLB, and MSA brains. To date, the trigger point in disease initiation is not entirely clear; that is, whether autophagy dysfunction alone suffices to increase α-synuclein or whether α-synuclein is the pathogenic driver. In the current review, we discuss the involvement of defective autophagy machinery in the formation of α-synuclein aggregates, propagation of α-synuclein, and the resulting neurodegenerative processes in α-synucleinopathies.
The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.
Infant fatality patterns in shared sleep: keys to intervention strategies?

This study examines infant fatalities that occurred while sharing a sleep surface. Fatality data reported to the U.S. Consumer Product Safety Commission (CPSC) during the time period January, 2013 through December, 2017 and involving infants through age 10 months were reviewed. 1,587 Cases were analyzed on the following variables: infant age and sex; sleep environment by product; cause of death; fatality pattern; and breastfeeding, where it was mentioned. 97% Of deaths were due to some form of asphyxia. Adult beds were associated with 78% of shared sleep fatalities, and the primary fatality pattern was overlay (35.4%)/probable overlay (8.8%). Infants <3 months made up 65% of fatalities. The data reflect that bedsharing continues, despite AAP guidelines to the contrary, and that overlay is the primary hazard pattern to be addressed. This paper discusses potential risk reduction strategies that may reduce the potential for overlay fatalities.
Thyroid Function Test in Patients with Chronic Kidney Disease

Background: Kidneys have a significant role in the metabolism, degradation and excretion of thyroid hormones. Both thyroid hormones and kidney functions have a multifaceted mutual interdependence. Objectives: To find out the possible association between the severity of chronic kidney disease and thyroid dysfunction; To estimate the correlation between thyroid dysfunction and various stages of chronic kidney disease. Materials and Methods: A prospective Cross-sectional study was done on 50 patients with Chronic kidney disease who were not on dialysis and fulfilled all the inclusion criteria at Saveetha medical college over a period of 6 months. Free T3, Free T4 and TSH levels were estimated for those patients. Results: Results of this study showed that majority of subjects included in our study were in the age group of 50-60 years with Male predominance. Out of 50 patients included in our study, 8 patients(16%) were found to hypothyroidism; 5 patients (10%) were having subclinical hypothyroidism; 20 patients (40%) were having low T3 syndrome and 17 patients (34%) were having normal functioning thyroid gland. Staging of CKD was done in relation to the glomerular filtration rate . Most of the patients(n=20) were in Stage 5 of Chronic kidney disease out of which 18 patients were having thyroid disorders. Conclusion: There is a positive correlation between the severity of CKD and thyroid dysfunction. Hence a routine thyroid function status should be evaluated in each and every patient of CKD to reduce the morbidity and mortality rate of CKD patients as well as reduce the social burden and health expenditure.
Tentando Recuperar a Interação Social Perdida Durante a Pandemia da COVID-19 por meio da Gamificação

A pandemia da COVID-19 mudou a vida social das pessoas. A educação, em especial, não passou ilesa a esse processo, enfrentando uma situação que trouxe, entre outros aspectos, o choque de trocar a sala de aula por atividades do ensino remoto. Para tanto, a gamificação social é uma alternativa que pode impactar a experiência dos estudantes no ensino remoto. Diante disso, realizamos um estudo com 17 estudantes e uma professora para entender como a gamificação social influencia o processo de ensino e aprendizagem no ensino remoto. Os resultados demostram que a gamificação social pode impactar a experiência dos estudantes, mas chama atenção para a necessidade de abordar outros aspectos da personalização da gamificação.