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Summary Introduction Self-medication of antibiotics among children is a very common problem in Tunisia. Its prevalence isn’t well established. The aims of this study are to evaluate parents’ knowledge concerning antibiotic use, and identify the factors associated with this problem. Patients and methods We conducted a cross-sectional study over a one year period (between August 2019 and July 2020). Data collection was performed using a questionnaire guided interview. We included parents of children consulting or hospitalized in the pediatric department of the university hospital Taher Sfar in Mahdia. Results A total of 354 parents were included with an average age of 36.4 ± 9.2 years. The average knowledge score was 2 ± 1.3 points. In fact, 61.6% of the parents had poor knowledge about antibiotics. The frequency of non-prescription antibiotics use among children was 20.6%. Amoxicillin was the most used antibiotic (72.6%). Sore throat, important fever and flu-like symptoms were the main symptoms justifying non-prescription antibiotic use among our pediatric population in 60.3%, 34.2% and 23.3% of cases respectively. The main reason of self-medication was the fact that the same antibiotic was once prescribed to treat the same symptoms (58.9%). The used antibiotic came from an old prescription for the same child in 57.5% of the cases and was recommended by the pharmacist in 39.7% of the cases. After multivariate analysis, the factors associated with parental self-medication with antibiotics were: the advanced parent's age, the ability to name an antibiotic and knowledge's score > 2. Conclusion Our study confirmed that parental knowledge about antibiotic use is low. In fact, the government should from one hand, organize antibiotic delivery and prohibit off the counter sells and in the other hand promote the education of the public through different procedures to stop this major health problem.

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a Byzantine behavior. Several approaches have been proposed recently to tolerate Byzantine workers. Yet all require trusting a central parameter server. We initiate in this paper the study of the ``general'' Byzantine-resilient distributed machine learning problem where no individual component is trusted. We show that this problem can be solved in an asynchronous system, despite the presence of $\frac{1}{3}$ Byzantine parameter servers and $\frac{1}{3}$ Byzantine workers (which is optimal). We present a new algorithm, ByzSGD, which solves the general Byzantine-resilient distributed machine learning problem by relying on three major schemes. The first, Scatter/Gather, is a communication scheme whose goal is to bound the maximum drift among models on correct servers. The second, Distributed Median Contraction (DMC), leverages the geometric properties of the median in high dimensional spaces to bring parameters within the correct servers back close to each other, ensuring learning convergence. The third, Minimum-Diameter Averaging (MDA), is a statistically-robust gradient aggregation rule whose goal is to tolerate Byzantine workers. MDA requires loose bound on the variance of non-Byzantine gradient estimates, compared to existing alternatives (e.g., Krum). Interestingly, ByzSGD ensures Byzantine resilience without adding communication rounds (on a normal path), compared to vanilla non-Byzantine alternatives. ByzSGD requires, however, a larger number of messages which, we show, can be reduced if we assume synchrony., This is a merge of arXiv:1905.03853 and arXiv:1911.07537; arXiv:1911.07537 will be retracted

Remote management is a modular and effective approach that allows for remote control and monitoring of irrigation systems and hydro-mechanical facilities that are spatially separated, while also tracking real-time irrigation data such as flow, pressure, electric pump groups, solar panels, and agro-meteorological data. Control instrumentation and automation equipment indicate the various defects that may occur on the hydro-mechanical equipment, which can upset the normal functioning of the structures of the irrigation system. Identifying the level of operation of the remote management system is an essential step to understand better the functionality of the remote control and monitoring system and define the objectives to be met and the technical specifications necessary for its implementation. This article will present a methodological approach for the implementation of a remote management system for large-scale irrigation. The proposed methodological approach focuses on improving the hydraulic parameters of pumping stations and irrigation hydrants and decreasing pumping station electricity consumption.

Organic–inorganic hybrid perovskite solar cells have become one of the most promising photovoltaic technologies with conversion efficiency exceeding 25%. Increasing the efficiency of the perovskite solar cells is one of the most important challenges in the photovoltaic domain. The development of good hole transport layers (HTLs) is crucial for high-performance perovskite-based photovoltaic systems. Furthermore, it is important to accurately optimize the energy level matching between perovskites and hole transport materials via better charge collection. This work explores the effect of the HTLs on the growth process, morphology and crystallinity of the perovskite active layer and their impacts on the photovoltaic performance. Devices with planar inverted architecture ITO/HTL/MAPbI3-xClx/PC60BM/BCP/Ag were fabricated using a one-step deposition method. The effects of the use of various HTL materials and the effect of the thermal annealing treatment of this layer were investigated. Morphological and microstructural analyzes of the perovskite films were carried out by scanning electron microscopy, X-ray diffraction. The optical properties in particular the absorption and the recombination process were studied by UV–vis absorption spectroscopy and photoluminescence spectroscopy. The photoelectric properties were investigated by photocurrent–voltage characteristic curves.

Objectives: With advances in data availability and computing capabilities, artificial intelligence and machine learning technologies have evolved rapidly in recent years. Researchers have taken advantage of these developments in healthcare informatics and created reliable tools to predict or classify diseases using machine learning-based algorithms. To correctly quantify the performance of those algorithms, the standard approach is to use cross-validation, where the algorithm is trained on a training set, and its performance is measured on a validation set. Both datasets should be subject-independent to simulate the expected behavior of a clinical study. This study compares two cross-validation strategies, the subject-wise and the record-wise techniques; the subject-wise strategy correctly mimics the process of a clinical study, while the record-wise strategy does not.Methods: We started by creating a dataset of smartphone audio recordings of subjects diagnosed with and without Parkinson’s disease. This dataset was then divided into training and holdout sets using subject-wise and the record-wise divisions. The training set was used to measure the performance of two classifiers (support vector machine and random forest) to compare six cross-validation techniques that simulated either the subject-wise process or the record-wise process. The holdout set was used to calculate the true error of the classifiers.Results: The record-wise division and the record-wise cross-validation techniques overestimated the performance of the classifiers and underestimated the classification error.Conclusions: In a diagnostic scenario, the subject-wise technique is the proper way of estimating a model’s performance, and record-wise techniques should be avoided.

The use of solar energy in isolated sites for different applications, such as water pumping, is of primary interest to people in developing countries who do not have safe access to water drinking. But photovoltaic generators have two major drawbacks that are a low yield and a high blow. In order to increase the performance of these systems. Proper adaptation between the solar generator and the load reduces the cost of installation. The choice of an energy system must obey and comply with certain rules. The selected energy system must show as a preliminary its competitiveness with respect to other systems for the same rendered service. The present study have a propose the modeling, simulation, sizing and realization of a photovoltaic pumping system operating over the sun. This system consists of a set of interacting elements; namely the photovoltaic panels, the voltage inverter, the asynchronous motor, the centrifugal pump and the hydraulic circuit.

Crop identification is vital to make an inventory of the crops grown in a given area and their cultivation period. The Remote sensing (RS) techniques can provide information on the distribution of cultivated land, crop types, and areas for the agricultural sector's effective management. In remote sensing, various vegetation indices (VI) can analyze and evaluate multiple phenomena and themes. The Normalized Difference Vegetation Index (NDVI) is an essential and highly significant remote measurement widely used in agriculture for phenological monitoring and crop health (Ray and Dadhwal, 2001). In this work, we present a methodology for the contribution of NDVI from Landsat 7 (TM) and (ETM+) images to crop mapping in the Gharb region using a classification based on the pixel approach and estimating rice crop coefficient from NDVI. The classification results concern six main types of crops planted in this region (beet, maize, sugar cane, market gardening, cereals, and rice). The classification map showed differences in agricultural practices adopted by farmers in crop spatial distribution. The classification results showed the ability of this methodology to discriminate between crops. Crop coefficients were deduced from the NDVI extracted from the images. Due to meteorological data collected from the meteorological station TCSC of SK Tlet, the estimation of the reference evapotranspiration was made and subsequently the potential evapotranspiration of each crop during the agricultural season 2019-2020. The highest values for ETC were obtained when the crop was in its full development when water was mainly lost through transpiration after a slight decrease in the ratio values observed during the phase of the vegetative cycle (maturity). The water requirements (daily, monthly and annual) for the crops were determined and their electrical energy consumption. Renewable energy can be an effective solution to meet the energy needs of plots , greenhouses and large farms. A technical-economic study of different combinations of autonomous hybrid renewable energy systems (HRES) in order to meet the power supply needs of the above mentioned crops in the Gharb region. The renewable energy sources considered are solar, wind and biomass. The results show that for an average energy requirement of 92 kWh/day and a peak load of 6.5 kW, the unit energy cost of the optimal configuration scenario A (PV-wind-biomass-battery) is 0.19 $/kWh. Therefore, the design, development and implementation of the proposed system is a promising solution for the security of energy supply. For a 100% integration of renewable energy, the HRES produces electricity according to the following distribution: 11% from wind, 41% from solar and 48% from biomass.

Summary Introduction The hyoid bone is of great importance in forensic medicine and it has been studied for various forensic purposes. However, there have been few studies dealing with age and sex estimation using the hyoid bone, especially the radiographic methods. Objective To analyze the relationship between age, sex and metric parameters of hyoid bone in Tunisian population sample, using CT scan. Materiel and methods A prospective study was conducted in the department of Forensic Medicine of Fattouma Bourguiba Hospital of Monastir-Tunisia during 4 years. A total of 240 samples of hyoid bone were studied. The age of cases ranged from 18 days to 81 years. The specimens were collected only from the deceased of known age. Once dried, each hyoid bone was scanned using CT scan. For each specimen, 10 measurements were taken using a computer program. The measurements consisted of 6 lengths and 4 widths. A regression analysis was used to estimate the relationship between age, sex and different measurements. For age estimation, a multiple logistic regression was carried out for samples ≤ 35 years. For sex determination, ROC curve was performed. Discriminant value finally retained was based on the best specificity with the best sensitivity. Results The correlation between real age and estimated age was good (r2 = 0.72) for samples aged 35 years or less. The unstandardized canonical function equation was estimated using three variables: maximum length of the right greater cornua, length from the middle of the left joint space to the middle of the right joint space and perpendicular length from the center point of a line between the distal ends of the right and left greater cornua to the center point of the anterior view of the body of the hyoid bone. For sex determination, the ROC curve analysis reveals that the area under curve was at 81.8%. Discriminant value was 0.451 with a specificity of 73% and sensibility of 79%. The equation function was estimated based on two variables: maximum length of the greater cornua and maximum length of the hyoid bone. Conclusion The findings of the current study suggest that metric analysis of the hyoid bone may predict the age ≤ 35 years. Sex estimation seems to be more reliable. Further studies dealing with the fusion of the hyoid bone and the current study could help to achieve more accurate age estimation rates.

Perovskite solar cells (PSC) have shown extraordinary progress over the last few years with remarkable efficiency. The perovskite film quality and structure can affect the performance of the resulting devices. Herein, the effect of PCDTBT addition on perovskite MAPbI3-xClx precursor solutions on the photovoltaic properties was investigated. It was found that the addition of the PCDTBT has effectively enhanced the efficiencies of the devices. This behavior would be due to the improvement of the MAPbI3-xClx film crystallinity and to the reduction of the recombination pathways due to the passivation of the trap states of the perovskite layer.

Chemie - Ingenieur - Technik : CIT (2020). doi:10.1002/cite.202000048, Published by Wiley-VCH Verl., Weinheim

Objectives Parkinson's disease (PD) is the second most common neurodegenerative disorder; it affects more than 10 million people worldwide. Detecting PD usually requires a professional assessment by an expert, and investigation of the voice as a biomarker of the disease could be effective in speeding up the diagnostic process. Methods We present our methodology in which we distinguish PD patients from healthy controls (HC) using a large sample of 18,210 smartphone recordings. Those recordings were processed by an audio processing technique to create a final dataset of 80,594 instances and 138 features from the time, frequency, and cepstral domains. This dataset was preprocessed and normalized to create baseline machinelearning models using four classifiers, namely, linear support vector machine, K-nearest neighbor, random forest, and extreme gradient boosting (XGBoost). We divided our dataset into training and held-out test sets. Then we used stratified 5-fold cross-validation and four performance measures: accuracy, sensitivity, specificity, and F1-score to assess the performance of the models. We applied two feature selection methods, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), to reduce the dimensionality of the dataset by selecting the best subset of features that maximizes the performance of the classifiers. Results LASSO outperformed ANOVA with almost the same number of features. With 33 features, XGBoost achieved a maximum accuracy of 95.31% on training data, and 95.78% by predicting unseen data. Conclusions Developing a smartphone-based system that implements machine-learning techniques is an effective way to diagnose PD using the voice as a biomarker.

Nowadays, in healthcare industry, data analysis can save lives by improving the medical diagnosis. And with the huge development in software engineering, different data mining tools are available for researchers, and used to conduct studies and experiments. For this, we have decided to compare six common data mining tools: Orange, Weka, RapidMiner, Knime, Matlab, and Scikit-Learn, using six machine learning techniques: Logistic Regression, Support Vector Machine, K Nearest Neighbors, Artificial Neural Network, Naive Bayes, and Random Forest by classifying heart disease. The dataset used in this study has 13 features, one target variable, and 303 instances in which 139 suffers from cardiovascular disease and 164 are healthy subjects. Three performance measures were used to compare the performance of the techniques in each tool: the accuracy, the sensitivity, and the specificity. The results showed that Matlab was the best performing tool, and Matlab’s Artificial Neural Network model was the best performing technique. We concluded this research by plotting the Receiver operating characteristic curve of Matlab and by giving several recommendations on which tool to choose taking into account the users experience in the field of data mining.

Hybrid organic–inorganic halide perovskite solar cells (PSCs) have gained exceptional attention in photovoltaic fields with an attractive yield of 25%. Characterization tools present as an important means that would help define optimized treatment parameters at an early stage of device manufacturing, instead of measuring the J (V) curves of complete solar cells. In this work, devices with planar NIP architecture ITO/SnO2/MAPbI3-xClx/HTL/Au were elaborated using one-step deposition method. The effects of annealing temperature of the ETL layer (SnO2) and various materials as an HTL layer have been studied. In parallel, X-ray diffraction, UV–visible absorption and photoluminescence were performed as well as photoluminescence spectroscopy, to analyze the active layer crystallinity, absorption properties and to probe charge transfer between perovskite and interface layers. By varying processing parameters, device efficiency could be raised from 10% up to 13.2%.

Organic agriculture production in Tunisia is relatively recent and was mainly oriented to the export market. However, local market demand is growing for such products. The objective of the current study is to assess consumers’ awareness and consumption of organic food products. A survey was implemented and included 250 respondents from the capital city Tunis. We used descriptive statistics, factor and cluster analysis to analyze the data. The results indicated that respondents’ perceptions towards organic food are positive. Four groups of consumers were identified with respondents from the main three groups have good understanding about organic food products characteristics. However, the proportion of consumers who purchase organic food on a regular basis is quite low (16%). Improving availability and access to organic products through the integration in mainstream sales channels and lower price premiums will help narrowing the gap between the intention to purchase organic products and the actual purchase. Nadhem Mtimet, Asma Souissi, Neama Mhamdi

Resume Cas d’un jeune homme de 24 avec syndrome d’Asperger ayant absorbe de fortes quantites de medicaments dont de la flecainide. Resume de son sejour en reanimation, notamment effet indesirable grave responsable de troubles du rythme ventriculaires (tachycardie ventriculaire) sur effet stabilisant de membrane responsable d’un allongement de la duree de sejour en reanimation. Etude de la litterature evaluant les differentes prises en charges deja publiees, avec notion de relargage a distance de la flecainide ingurgitee quelques jours avant. Releve de la litterature evaluant les differentes prises en charge deja etudiees au niveau mondial.

The pathophysiological link between adiposity and blood pressure is not completely understood, and evidence suggests an influence of sex and genetic determinants. We aimed to identify the relationship between adiposity and blood pressure, independent of a robust set of lifestyle and metabolic factors, and to examine the modulating role of sex and Angiotensin-Converting Enzyme (ACE) insertion/deletion (I/D) polymorphisms. In the Relationship Between Insulin Sensitivity and Cardiovascular Disease (RISC) study cohort, 1211 normotensive individuals, aged 30 to 60 years and followed-up after 3.3 years, were characterized for lifestyle and metabolic factors, body composition, and ACE genotype. Body mass index (BMI) and waist circumference (WC) were independently associated with mean arterial pressure, with a stronger relationship in women than men (BMI: r =0.40 versus 0.30; WC: r =0.40 versus 0.30, both P ID and II ACE genotypes in both sexes ( P ACE genotype only in women ( P =0.03). A 5 cm larger WC at baseline increased the risk of developing hypertension at follow-up only in women (odds ratio, 1.56 [95% CI, 1.15–2.10], P =0.004) and in II genotype carriers (odds ratio, 1.87 [95% CI, 1.09–3.20], P =0.023). The hypertensive effect of adiposity is more pronounced in women and in people carrying the II variant of the ACE genotype, a marker of salt sensitivity.

Background Pneumococcal infections are an important cause of morbidity and mortality in the world and in Tunisia. Data on the economic burden of these infections are needed to inform decision-making to include pneumococcal vaccinations in routine childhood immunization. Aims This study aimed to estimate the medical cost of hospitalizations due to invasive pneumococcal disease (pneumonia and meningitis) among children aged under 15 years old in Tunisia. Methods A prospective multicentre study was conducted in 15 paediatric departments, across different socio-economic areas of Tunisia, from June 2014 to May 2015. All children aged under 15 years old who were hospitalized for pneumococcal pneumonia or confirmed bacterial meningitis were enrolled. A case report form was completed for every eligible case. Activity Based Costing method was used to estimate the hospital cost. Data entry and statistical analysis were conducted using SPSS, version 20.0. Results During the study period, 727 children were hospitalized for pneumococcal pneumonia and 60 children were hospitalized for bacterial meningitis, among them 21(35%) had confirmed pneumococcal meningitis. The median hospital cost for pneumococcal pneumonia was 353.910 Tunisian Dinars (TND) and TND 1680.632 for pneumococcal meningitis. Using overall data extrapolation, it was estimated that nearly 1091 hospitalizations for pneumococcal pneumonia and 69 hospitalizations for pneumococcal meningitis occurred each year in Tunisian children aged under 15 years of age, incurring total costs of TND 502 079.408. Conclusion The economic burden of pneumococcal infections seems to be substantial in Tunisia. The estimated costs does not reflect the real costs of this infection. Cost-effectiveness studies would be helpful to inform policy-makers to take appropriate decisions.

Optimization of the energy consumption at fluctuating short-term electricity markets is a promising measure to increase the economic efficiency of energy-intense processes. This can be addressed by integrating the economic perspective directly into the process control, i.e., by using economic nonlinear model predictive control (eNMPC). We present a single-layer eNMPC framework for optimal operation of an industrial-scale nitrogen plant participating in real-time electricity markets. To achieve real-time capability, we utilize suboptimal updates as well as our reduced modeling approach for rectification columns combining compartmentalization and artificial neural networks (Schafer et al., AIChE J., doi:10.1002/aic.16568). We demonstrate the real-time capability of the approach in-silico. We explicitly account for model-plant mismatch by using a detailed full-order stage-by-stage model that is common in literature as plant replacement. Our results show that close-to-optimal savings in electricity costs are enabled via the eNMPC strategy even under consideration of inherently uncertain market forecasts whilst safely satisfying production targets. Furthermore, the disturbance rejection capability of the control structure is investigated, showing that severe unmeasured disturbances with slow dynamics can be rejected effectively without violating product requirements.

Introduction The COVID-19 pandemic is causing considerable lifestyle changes through barriers measures, lockdown and social distancing. These changes include: Preoccupation with Covid-19 pandemic news, sleep disturbances and eating habits changes. Aim To assess the lifestyle changes after exposure to a risk of coronavirus contamination. Methods We conducted a cross sectional study during November and December 2020 among adults tested for COVID-19 in Mahdia (Tunisia).To assess the life habits changes (sleep, physical exercises and diet), the compliance with barrier measures and preoccupation with COVID-19 pandemic news, a reliable tool was developed and pretested by experts at the department of Preventive and Community Medicine Results A total of 277 participants were enrolled, the average age was 34.32 years ± 13.7. Among them 60.8% were women (sex-ratio = 0.51).Almost two thirds 62.2% were tested positive for Covid-19. Doctors presented 29.2% of the respondents. Among the study sample, 38.6% lived with elderly people. Approximately three quarters 77.8% reported an improvement in compliance with barrier measures. The majority 92.1% respected more the social distancing. As for hand washing and for mask wearing, 87.7% said to wash their hands more, and 95.3% were more concerned about wearing masks. A life habits change was observed among 16.7% of the respondents. Almost half of the participants 45.5% increased their physical activity, while 76.2% tried to get a healthy diet. Sleep disorders were registered among 48.7% of respondents. Preoccupation with the COVID news was observed in approximately half of the participants (57.8%). Conclusions The COVID19 pandemic affected different aspects of our lives. Research related to lifestyle, social and behavioral studies are as important as those related to medical and biological science. Key messages A life habits change was observed among 16.7% of the respondent, sleep disorder were registered among 48.7%, and 77.8% reported and improvement in applying barrier measures. Research related to lifestyle, social and behavioral studies are as important as those related to medical and biological science.

Introduction Health workers, as the first line of defence during infectious pandemic, are also one of the main vectors of coronavirus transmission. Preventing the spread of infection to and from caregivers relies on the effectiveness of personal protective equipment (PPE). This study aimed at assessing knowledge, attitudes and practices related to PPE use in a healthcare setting in the region of Mahdia (Tunisia). Methods A cross-sectional study was carried out on December 2020 among health care professionals (HCP) in the University Hospital Tahar Sfar Mahdia. The measurement tool was developed by specialists at the Department of Preventive Medicine after reviewing literature then pretested. Results A total of 173 HCP were enrolled with an average age of 35 ±9.3 years. More than half of participants were females (64.7%), 32.9% were doctors and 34.7% were nurses. Among them, 35.8% have worked during a pandemic before. The level of good knowledge was 52%. The main sources of information for HCP were the Ministry of Health (63.6%), social network and media (58.4%). About 25.4% of HCP was satisfied by the preventive measures training received in the hospital. Half of HCP (54.3%) admitted that their knowledge related to the pandemic was not sufficient. While third of the HCP (31.8%) thought that it is necessary to wear the full PPE, 16.8% knew how to put on their PPE and 27.2% knew how to take it off. Conclusions Taking into account knowledge, attitudes and practices related to PPE use allows the implementation of a strategy aimed at mastering the management of this equipment by health care workers. Key messages Assessing the Knowledge, attitudes and practices related to the personal equipment among health care givers: 52%. Implementation of a strategy aimed at mastering the management of this equipment by health care workers.

Background The massive spread of the corona virus being relatively deadly, leads to psychological pressures dominated by an atmosphere of panic and worries mainly among Health Care workers (HCWs). Only few studies have evaluated this impact on HCWs mental health in Tunisia. Aim we aimed at determining the prevalence of corona virus fear and its associated factors among HCWs in the University Hospital Tahar Sfar Mahdia (Tunisia). Methods We conducted a cross sectional study among HCWs staff in the University Hospital Tahar Sfar Mahdia which is located in the Central-East part of Tunisia in December 2020. The fear of corona virus disease was assessed using the valid French version of the Fear of Covid-19 Scale (FCV-7). Results A total of 173 professionals were recruited with a mean age of 35.35 ± 9.31 years. Among them, 66% were women. One third of participants (33.3%) were medical professionals. Almost, 68% have experienced a high level of fear. Our survey showed a significantly higher level of fear among females 70.1% and elderly people 60.8%. It was positively associated (p < 0.05) with anxiety and depression, high risk perception and the responsibility degree of being a virus vector for their loved ones. Anxiety and depression disorders (standardized β = 3.118, CI = 0.545-5.832,p=0.018), high risk perception (β = 3.321,CI = 0.097-6.545, p = 0.044), knowledge about Covid-19 transmission (β = 2.586, CI = 0.439-4.734, p = 0.019), degree of responsibility (β = 3.130, CI = 0.373-5.887, p = 0.026), being a doctor (β = -6.475, CI = -8.785- -4.165, p = 0.001) and Perceived Vulnerability to Disease (β = 0.26,CI = 0.157-0.363, p < 001) were found to be predictive factors of corona virus fear in the linear regression model (Adjusted R2 = 0.374). Conclusions Authorities should introduce a preventive strategy based on psychological intervention for HCWs to overcome their fear and worries. Key messages The high prevalence of the fear of Covid-19 among health care workers is alarming. Its impact on mental health cannot be ignored. The health authorities worldwide should not have as objective only the reduction of the transmission, but also the work on individual fears to ensure a society totally free of covid-19.

Introduction: Covid-19 pneumonia has been shown to be associated with an increased prevalence of pulmonary embolism (PE) and along with it, an expected higher risk of morbidity and mortality. Aim: Identifying risk factors for PE and eventual prognosis relating to these factors. Methods: 139 patients were admitted to the military hospital of Tunis, positive on covid-19 RT-PCR testing, from September 2020 to February 2021. Results: Out of those patients,12 (11.8%) had a pulmonary embolism confirmed by CT-angiography.There was no significant correlation between PE and gender, history of pulmonary disease (asthma and COPD), or cardiovascular risk factors (type-2 diabetes, hypertension, dyslipidemia).There was a statistically significant difference between Hemoglobin serum levels in the PE positive group vs the PE negative group(11,66 g/dL vs 13,01 g/dL, p-value 0,22).This was also the case for prothrombin ratio which was lower in the PE positive group than in the negative group(79,5% vs 90,32%, p-value 0,39).Mean D-dimer, measured on admission, was not significantly higher in the PE positive group (5052 ng/mL) than the PE negative group(2404 ng/mL), similarly to C-reactive protein levels(129,12 mg/L vs 152,7mg/L).No significant correlation was found between PE, fibrinogen and ferritinemia serum levels.On CT-scans, out of 12 patients, 5 had a 25-50% lesion extent,3 had 50-75% and only 1 had >75% while the rest were not siginificant.PE was not associated with a worst outcome:no patients needed optiflow or intubation during admission and there was no significant difference in death between the two groups. Conclusion: These findings give another perspective on PE in the context of covid-19 and emphasize the heterogeneity of its induced coagulopathy.

Introduction: Health care workers (HCW) are at a high risk of COVID 19 infection and may endure mental health disorders facing this stressful situation. Aims: Evaluate the degree of anxiety, insomnia and depression in Tunisian healthcare workers during COVID19 pandemic. Methods: A cross-sectional study based on a self-administrated questionnaire in February 2021 has included HCW of the military hospital of Tunis. Depression, anxiety and insomnia were assessed by the French versions of the Hospital Anxiety and Depression Scale and the Insomnia Severity Index. Results: A total of 110 HCW responded to the questionnaire. Sex ratio was 0.89. Fifty-seven (51.8%) were single. The mean age was 33.7 years (SD 9.3). Our participants were doctors in 45.5%, nurses in 26.4% and technicians in 12.7%. The mean professional seniority was 9.5 years (SD 9.9). HCW worked in medical services in 77.3% and in COVID units in 81.8%. Sixty five per cent of respondents perceived that their put them in a great risk of contamination. Almost 37 % of them declared that their work is no longer important compared to their health (93.6%) and their social relationships (84.6%). The mean scores of anxiety and depression were 8.31(SD 4.27) and 8.43(SD 3.63) respectively. Anxiety and depression was found in 30% and 25.5% of HCW. The mean score of ISI was 8.71(SD 7.64). Insomnia was found in 41.8%. This disorder was moderate and severe in 6.4% and 10% respectively. Conclusions: Health care workers have high levels of insomnia, anxiety and depression. These mental disorders may have a negative impact on social and professional quality of life.

For the existence of humans, agriculture is vitally crucial. For a big population of the globe, agriculture provides a living. It also provides the locals with a large number of work openings. Many farmers desire to use old-fashioned farming techniques, which provide poor income. Critical to the economy's long-term development and advancement are agriculture and the related industries. Decision making, crop selection and supporting systems for increased crop output are the primary problems for agricultural production. The prediction of agriculture depends on parameters such as temperature, soil fertility, amount of water, water quality and seasons, crop price, etc. Machine learning plays an important role in crop yield prediction on the basis of geography, climate details, and season. It helps farmers in growing most appropriate crop for their farm land. This paper presents a machine learning based framework for prediction of crop yield. For experimental set up, a data set is created for crop details. Machine learning algorithms SVM, random forest and ID3 are used for investigation.

Internet of Vehicles (IoV), whose basic concepts are like those of the Internet of Things (IoT), refers to the ability of vehicles to communicate intelligently with each other through the exchange of information. To guarantee the services offered by IoV, it is necessary to consider the exchange of a large amount of information without human intervention and in a decentralized manner. To overcome these challenges, the use of blockchain technology is required. This technology is supported by the concept of smart contracts and implements the notion of decentralized applications (DApp). Our objective is, then, to design a decentralized application that brings together all the services accessible by vehicles. The user, depending on the type of service, can filter the search result and benefit from the appropriate service. Via this paper, we present the basic concepts to be used in the development of our platform such as blockchain, smart contracts, DApp and their relationship with IoV. We model the communication between the different actors of the system as well as the design of the proposed framework.

Introduction Retinal vasoproliférative tumors (VPTs) have been reported as uncommon complications of intermediate Uveitis. Case description A patient consulted for a gradually decreased vision in the right eye (RE). The examination of the RE found a corrected visual acuity at 20/100 with normal eye pressure measured. The anterior segment was deep and quiet and 2+ vitreous haze was found. Funduscopy showed a VPT. Retinal fluorescein angiography of the RE revealed macular cystoid edema also objectified by the Optical Coherence Tomography (OCT). Intermediate Uveitis was considered idiopathic. The patient received a cryoapplication and was put on oral corticosteroid therapy with improvement of visual acuity and a regress of both vitreous inflammation and cystoid macular edema. Conclusion The association of a VPT with intermediate uveitis represents a real diagnostic and therapeutic challenge and imposes rigorous care and monitoring strategy combining internist and ophthalmologist., Highlights • Retinal vasoproliferative tumors (VPTs) have been reported as uncommon complications of intermediate Uveitis. • The presence of active intermediate uveitis accompanied by VPTs suggests the need for an aggressive uveitis treatment. • The causative process underlying the development of VPT in eyes with intermediate Uveitis remains unknown. • We speculate in this case report a pathogenic relationship of this long-standing inflammation to the vascular tumor.

The emergence of the Internet of things (IoT) shaped the future of many domains where connectivity became a hot topic in electronic engineering. Recently, IoT was being a part of Indoor positing systems where devices need to communicate continuously at a defined time slot leading to various problematics for the conventional positioning algorithm. We propose an ESP8266 NodeMCU tracking system of multiple tags using a Wireless communication. The proposed implemented algorithm consists of two-phase in the first stage, a master anchor sends a signal to tags in order to establish the communication. In the second one, the master anchor initiates distance ranging and positioning. This two-phase based system aims to minimize reading time and increase the accuracy of positioning by reducing distance ranging errors, which we validate in our proposed model and algorithm by an experiment in an indoor environment.

Organometal halide perovskite solar cells are becoming one of the most competitive emerging technologies. They have reached a power conversion efficiency (PCE) of 22.7% in 10 years. Their high efficiency and simple fabrication process render perovskite solar cells a promising player in the field of third-generation photovoltaics. The deposition methods play an important role in the fabrication of a high quality films. In this paper, we report the preparation of methylammonium lead bromide (MAPbBr3) thin film using a two-step method based on the transformation of PbBr2 into MAPbBr3 perovskite after dipping in a MABr solution. The effects of the dipping time and the annealing time on the photovoltaic, optical and structural properties of the devices were studied. The dipping time treatments of the inorganic film in organic solution were conducted from 30 s to 15 min. The obtained result showed that the PCE of the devices was improved with the increase of dipping time. In addition, an increase of annealing time induces an enhancement of the perovskite properties. Furthermore, the as-fabricated perovskite solar cell dipped and annealed for 10 min exhibited the highest power conversion efficiency of 4.8% with a short circuit current density of 16.16 mA cm−2, an open circuit voltage of 0.84 V, and a fill factor of 35.50.

The overall prognosis in patients with advanced and metastatic lung cancer is poor, however a subset of these patients has durable survival and they are called long-term survivors (LS). The definition of LS varies in published series from 18 months to 5 years. Few studies have focused on them. We report six cases of LS patients: three cases of non-small- cell lung cancer (stage IIIB and stage IV), two case of small cell lung cancer, and a case of composite carcinoma (small cell carcinoma and squamous carcinoma), all having a survival of five years or more. Our patients show clinical and evolutionary similarities with LS in the literature regarding the prognostic factors associated with prolonged survival namely performance status and good response to first-line chemotherapy.

Early life adversities (ELAs) are shown as significant risk factors for chronic health conditions (CHCs). ELAs include multiple types of abuse such as the social abuse (peer, community, and collective violence). The purpose is to describe the relationship between childhood social abuse and chronic conditions in adulthood among a sample of adults in Tunisia and to investigate the role of obesity and tobacco use as mediators of this association. A cross-sectional study was conducted in Tunisia, from January to June 2016 using the Arabic Adverse Childhood Experiences International Questionnaire (ACE-IQ). Items of social abuse (peer violence, witnessing community violence, and exposure to collective violence) were analyzed. A total of 2,120 adults were enrolled. After adjustment for age, gender, and intrafamilial ELA, social adversities were associated significantly with the selected CHC. Experiencing more than two social ELA increase the risk of occurrence of hypertension and coronary diseases. After accounting for the indirect effect of body mass index, statistically significant partial mediation effects were observed for the cumulative number of social ELA as the exposure variable and chronic diseases as the outcome variable (p ≤ .001; % mediated = 44.5%). These findings support an association between many chronic health disorders and childhood social abuse, independently of intrafamilial ACEs.

Introduction Renal involvment of Behçet disease is not usual and Ig A vasculitis complicated by Behçet disease is extremely rare. Case presentation We report a case of Henoch Scholein purpura and nephritis associated with Behçet disease revealed by ophthalmic examination. Clinical discussion Recurrent oral ulcer is not a manifestation of Ig A vasculitis which doesn't affect large or middle sized arteries. If the patient had'nt been referred to our department for ophthalmic examination, we may have failed to make the diagnosis and treat coexisting Behçet disease. Conclusion The link of HSP and BD has yet to be fully understood, and more researchisdefinitelyneeded., Highlights • The renal involvement is not verycommon in Behçet disease. • Studies have shown a correlation between certain human leukocyte antigens (HLA) and renal involvment in Behçet disease. • Ig A vasculitis complicated by Behçet disease is extremely rare. • The link of HSP and BD has yet to be fully understood, and more researchisdefinitelyneeded.

International audience; This paper presents a new cell-aware diagnosis flow that can be used to address a specific scenario (test protocol) one may encounter during diagnosis of customer returns. In this flow, we use a Bayesian classification method to precisely identify defect candidates. Experiments done on benchmark circuits as well as on a test chip from STMicroelectronics have proven the efficacy of our flow in terms of diagnosis accuracy and resolution.

Location-based systems have come under the spotlight in the last decade. They became significant with the bloom of applications that have tracking services. Indoors, wireless technologies can be an alternative for precise positioning in small or large areas where GPS signals are too weak and inaccurate. In this paper, we present a Wi-Fi Received Signal Strength based system using fingerprinting and trilateration techniques. We constructed radio- maps by collecting signals with ESP8266 modules in the offline phase. Then, we looked for the high number of matches in real-time by comparing collected signals to those stored in database. Simultaneously, we use trilateration to find positions with three nearby APs in the online phase. We achieved an accuracy of 98% by combining fingerprinting and trilateration locating techniques. We also analyzed the error probability for position calculation at different time of the day and demonstrated high stability of our system with a low standard deviation.

The enhancement of patient safety and the improvement of medical care services remain one the main concerns of the World Health Organization. Thus, given the healthcare critical impact on patients, there is a growing need for high-risk and expensive medicine management. For this purpose, we propose an innovative system prototype for the reduction of medication error possibility from prescription, validation to its preparation. We present a Radio Frequency IDentification monitoring system for hospital and pharmacy using digital signature. Our designed system is constituted of RFID readers, ESP8266 modules, and tags to ensure an automated medication management and a good inventory control.

The rapid development of the Internet of Things (IoT) is affecting several sectors, in particular the Vehicular Ad-Hoc Network (VANET). The link between the IoT and VANET is giving rise to the Internet of Vehicles (10V). With the IoV,vehicles are able to communicate with each other and with other connected objects. To ensure the exchange of information flows between the different entities, autonomy, speed and the decentralized concept must be taken into account. Blockchain technology is a distributed register technology. It is the solution to overcome these challenges and to guarantee vehicular communication. This research work deals primarily with this new technology and its applications in IoV.We then present a multi-agent model of vehicle communication based on Blockchain technology and smart contracts.

The prolonged treatment of immunosuppressed (IS) individuals with anti-influenza monotherapies may lead to the emergence of drug-resistant variants. Herein, we evaluated oseltamivir and polymerase inhibitors combinations against influenza A/H3N2 infections in an IS mouse model. Mice were IS with cyclophosphamide and infected with 3 &times, 103 PFU of a mouse-adapted A/Switzerland/9715293/2013 (H3N2) virus. Forty-eight hours post-infection, the animals started oseltamivir, favipiravir or baloxavir marboxil (BXM) as single or combined therapies for 10 days. Weight losses, survival rates and lung viral titers (LVTs) were determined. The neuraminidase (NA) and polymerase genes from lung viral samples were sequenced. All untreated animals died. Oseltamivir and favipiravir monotherapies only delayed mortality (the mean day to death (MDD) of 21.4 and 24 compared to 11.4 days for those untreated) while a synergistic improvement in survival (80%) and LVT reduction was observed in the oseltamivir/favipiravir group compared to the oseltamivir group. BXM alone or in double/triple combination provided a complete protection and significantly reduced LVTs. Oseltamivir and BXM monotherapies induced the E119V (NA) and I38T (PA) substitutions, respectively, while no resistance mutation was detected with combinations. We found that the multiple dose regimen of BXM alone provided superior benefits compared to oseltamivir and favipiravir monotherapies. Moreover, we suggest the potential for drug combinations to reduce the incidence of resistance.