University of Sfax

This review describes the most common methods for recovery of chitin from marine organisms. In depth, both enzymatic and chemical treatments for the step of deproteinization are compared, as well as different conditions for demineralization. The conditions of chitosan preparation are also discussed, since they significantly impact the synthesis of chitosan with varying degree of acetylation (DA) and molecular weight (MW). In addition, the main characterization techniques applied for chitin and chitosan are recalled, pointing out the role of their solubility in relation with the chemical structure (mainly the acetyl group distribution along the backbone). Biological activities are also presented, such as: antibacterial, antifungal, antitumor and antioxidant. Interestingly, the relationship between chemical structure and biological activity is demonstrated for chitosan molecules with different DA and MW and homogeneous distribution of acetyl groups for the first time. In the end, several selected pharmaceutical and biomedical applications are presented, in which chitin and chitosan are recognized as new biomaterials taking advantage of their biocompatibility and biodegradability.

BACKGROUND: Public health recommendations and governmental measures during the COVID-19 pandemic have resulted in numerous restrictions on daily living including social distancing, isolation and home confinement. While these measures are imperative to abate the spreading of COVID-19, the impact of these restrictions on health behaviours and lifestyles at home is undefined. Therefore, an international online survey was launched in April 2020, in seven languages, to elucidate the behavioural and lifestyle consequences of COVID-19 restrictions. This report presents the results from the first thousand responders on physical activity (PA) and nutrition behaviours. METHODS: Following a structured review of the literature, the "Effects of home Confinement on multiple Lifestyle Behaviours during the COVID-19 outbreak (ECLB-COVID19)" Electronic survey was designed by a steering group of multidisciplinary scientists and academics. The survey was uploaded and shared on the Google online survey platform. Thirty-five research organisations from Europe, North-Africa, Western Asia and the Americas promoted the survey in English, German, French, Arabic, Spanish, Portuguese and Slovenian languages. Questions were presented in a differential format, with questions related to responses "before" and "during" confinement conditions. RESULTS: 1047 replies (54% women) from Asia (36%), Africa (40%), Europe (21%) and other (3%) were included in the analysis. The COVID-19 home confinement had a negative effect on all PA intensity levels (vigorous, moderate, walking and overall). Additionally, daily sitting time increased from 5 to 8 h per day. Food consumption and meal patterns (the type of food, eating out of control, snacks between meals, number of main meals) were more unhealthy during confinement, with only alcohol binge drinking decreasing significantly. CONCLUSION: While isolation is a necessary measure to protect public health, results indicate that it alters physical activity and eating behaviours in a health compromising direction. A more detailed analysis of survey data will allow for a segregation of these responses in different age groups, countries and other subgroups, which will help develop interventions to mitigate the negative lifestyle behaviours that have manifested during the COVID-19 confinement.

Experiments suggest that biodiversity enhances the ability of ecosystems to maintain multiple functions, such as carbon storage, productivity, and the buildup of nutrient pools (multifunctionality). However, the relationship between biodiversity and multifunctionality has never been assessed globally in natural ecosystems. We report here on a global empirical study relating plant species richness and abiotic factors to multifunctionality in drylands, which collectively cover 41% of Earth's land surface and support over 38% of the human population. Multifunctionality was positively and significantly related to species richness. The best-fitting models accounted for over 55% of the variation in multifunctionality and always included species richness as a predictor variable. Our results suggest that the preservation of plant biodiversity is crucial to buffer negative effects of climate change and desertification in drylands.

This study investigates whether the expertise, independence, and activities of a firm's audit committee have an effect on the quality of its publicly released financial information. In particular, we examine the relationship between audit committee characteristics and the extent of corporate earnings management as measured by the level of income-increasing and income-decreasing abnormal accruals. Using two groups of U.S. firms, one with relatively high and one with relatively low levels of abnormal accruals in the year 1996, we find a significant association between earnings management and audit committee governance practices. We find that aggressive earnings management is negatively associated with the financial and governance expertise of audit committee members, with indicators of independence, and with the presence of a clear mandate defining the responsibilities of the committee. The association is similar for both income-increasing and income-decreasing earnings management, suggesting that audit committee members are concerned with both types of earnings management and do not exhibit an asymmetric loss function similar to that of auditors.

BACKGROUND: Obesity represents an urgent problem that needs to be properly addressed, especially among children. Public and global health policy- and decision-makers need timely, reliable quantitative information to develop effective interventions aimed at counteracting the burden generated by high body mass index (BMI). Few studies have assessed the high-BMI-related burden on a global scale. METHODS AND FINDINGS: Following the methodology framework and analytical strategies used in the Global Burden of Disease Study (GBD) 2017, the global deaths and disability-adjusted life years (DALYs) attributable to high BMI were analyzed by age, sex, year, and geographical location and by Socio-demographic Index (SDI). All causes of death and DALYs estimated in GBD 2017 were organized into 4 hierarchical levels: level 1 contained 3 broad cause groupings, level 2 included more specific categories within the level 1 groupings, level 3 comprised more detailed causes within the level 2 categories, and level 4 included sub-causes of some level 3 causes. From 1990 to 2017, the global deaths and DALYs attributable to high BMI have more than doubled for both females and males. However, during the study period, the age-standardized rate of high-BMI-related deaths remained stable for females and only increased by 14.5% for males, and the age-standardized rate of high-BMI-related DALYs only increased by 12.7% for females and 26.8% for males. In 2017, the 6 leading GBD level 3 causes of high-BMI-related DALYs were ischemic heart disease, stroke, diabetes mellitus, chronic kidney disease, hypertensive heart disease, and low back pain. For most GBD level 3 causes of high-BMI-related DALYs, high-income North America had the highest attributable proportions of age-standardized DALYs due to high BMI among the 21 GBD regions in both sexes, whereas the lowest attributable proportions were observed in high-income Asia Pacific for females and in eastern sub-Saharan Africa for males. The association between SDI and high-BMI-related DALYs suggested that the lowest age-standardized DALY rates were found in countries in the low-SDI quintile and high-SDI quintile in 2017, and from 1990 to 2017, the age-standardized DALY rates tended to increase in regions with the lowest SDI, but declined in regions with the highest SDI, with the exception of high-income North America. The study's main limitations included the use of information collected from some self-reported data, the employment of cutoff values that may not be adequate for all populations and groups at risk, and the use of a metric that cannot distinguish between lean and fat mass. CONCLUSIONS: In this study, we observed that the number of global deaths and DALYs attributable to high BMI has substantially increased between 1990 and 2017. Successful population-wide initiatives targeting high BMI may mitigate the burden of a wide range of diseases. Given the large variations in high-BMI-related burden of disease by SDI, future strategies to prevent and reduce the burden should be developed and implemented based on country-specific development status.

Power costs increasing, environmental pollution and global warming are issues that we are dealing with in the present time. To reduce their effects, scientists are focusing on improving energy harvesting-based power generators. Thermoelectric generators (TEGs) have demonstrated their ability to directly convert thermal energy into an electrical one via the Seebeck effect. Also, they are environmentally friendly because they do not contain chemical products, they operate silently because they do not have mechanical structures and/or moving parts, and they can be fabricated on many types of substrates like silicon, polymers, and ceramics. Furthermore, TEGs are position-independent, present a long operating lifetime and are suitable for integration into bulk and flexible devices. This paper presents in-depth analysis of TEGs, starting by an extensive description of their working principle, types (planar, vertical and mixed), used materials, figure of merit, improvement techniques including different thermoelectric materials arrangement (conventional, segmented and cascaded), and used technologies and substrates types (silicon, ceramics and polymers). This manuscript also describes the exploitation of TEGs in various fields starting from low-power applications (medical and wearable devices, IoT: internet of things, and WSN: wireless sensor network) to high-power applications (industrial electronics, automotive engines, and aerospace).

The International League Against Epilepsy (ILAE) Task Force on Nosology and Definitions proposes a classification and definition of epilepsy syndromes in the neonate and infant with seizure onset up to 2 years of age. The incidence of epilepsy is high in this age group and epilepsy is frequently associated with significant comorbidities and mortality. The licensing of syndrome specific antiseizure medications following randomized controlled trials and the development of precision, gene-related therapies are two of the drivers defining the electroclinical phenotypes of syndromes with onset in infancy. The principal aim of this proposal, consistent with the 2017 ILAE Classification of the Epilepsies, is to support epilepsy diagnosis and emphasize the importance of classifying epilepsy in an individual both by syndrome and etiology. For each syndrome, we report epidemiology, clinical course, seizure types, electroencephalography (EEG), neuroimaging, genetics, and differential diagnosis. Syndromes are separated into self-limited syndromes, where there is likely to be spontaneous remission and developmental and epileptic encephalopathies, diseases where there is developmental impairment related to both the underlying etiology independent of epileptiform activity and the epileptic encephalopathy. The emerging class of etiology-specific epilepsy syndromes, where there is a specific etiology for the epilepsy that is associated with a clearly defined, relatively uniform, and distinct clinical phenotype in most affected individuals as well as consistent EEG, neuroimaging, and/or genetic correlates, is presented. The number of etiology-defined syndromes will continue to increase, and these newly described syndromes will in time be incorporated into this classification. The tables summarize mandatory features, cautionary alerts, and exclusionary features for the common syndromes. Guidance is given on the criteria for syndrome diagnosis in resource-limited regions where laboratory confirmation, including EEG, MRI, and genetic testing, might not be available.

Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today's large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs.

Soil salinization is a major threat to agriculture in arid and semi-arid regions, where water scarcity and inadequate drainage of irrigated lands severely reduce crop yield. Salt accumulation inhibits plant growth and reduces the ability to uptake water and nutrients, leading to osmotic or water-deficit stress. Salt is also causing injury of the young photosynthetic leaves and acceleration of their senescence, as the Na+ cation is toxic when accumulating in cell cytosol resulting in ionic imbalance and toxicity of transpiring leaves. To cope with salt stress, plants have evolved mainly two types of tolerance mechanisms based on either limiting the entry of salt by the roots, or controlling its concentration and distribution. Understanding the overall control of Na+ accumulation and functional studies of genes involved in transport processes, will provide a new opportunity to improve the salinity tolerance of plants relevant to food security in arid regions. A better understanding of these tolerance mechanisms can be used to breed crops with improved yield performance under salinity stress. Moreover, associations of cultures with nitrogen-fixing bactéria and arbuscular mycorrhizal fungi could serve as an alternative and sustainable strategy to increase crop yields in salt affected fields.

Natural language processing (NLP) has been studied in computing for decades. Recent technological advancements have led to the development of sophisticated artificial intelligence (AI) models, such as Chat Generative Pre-trained Transformer (ChatGPT). These models can perform a range of language tasks and generate human-like responses, which offers exciting prospects for academic efficiency. This manuscript aims at (i) exploring the potential benefits and threats of ChatGPT and other NLP technologies in academic writing and research publications; (ii) highlights the ethical considerations involved in using these tools, and (iii) consider the impact they may have on the authenticity and credibility of academic work. This study involved a literature review of relevant scholarly articles published in peer-reviewed journals indexed in Scopus as quartile 1. The search used keywords such as "ChatGPT," "AI-generated text," "academic writing," and "natural language processing." The analysis was carried out using a quasi-qualitative approach, which involved reading and critically evaluating the sources and identifying relevant data to support the research questions. The study found that ChatGPT and other NLP technologies have the potential to enhance academic writing and research efficiency. However, their use also raises concerns about the impact on the authenticity and credibility of academic work. The study highlights the need for comprehensive discussions on the potential use, threats, and limitations of these tools, emphasizing the importance of ethical and academic principles, with human intelligence and critical thinking at the forefront of the research process. This study highlights the need for comprehensive debates and ethical considerations involved in their use. The study also recommends that academics exercise caution when using these tools and ensure transparency in their use, emphasizing the importance of human intelligence and critical thinking in academic work.

Alginate is a hydrocolloid from algae, specifically brown algae, which is a group that includes many of the seaweeds, like kelps and an extracellular polymer of some bacteria. Sodium alginate is one of the best-known members of the hydrogel group. The hydrogel is a water-swollen and cross-linked polymeric network produced by the simple reaction of one or more monomers. It has a linear (unbranched) structure based on d-mannuronic and l-guluronic acids. The placement of these monomers depending on the source of its production is alternating, sequential and random. The same arrangement of monomers can affect the physical and chemical properties of this polysaccharide. This polyuronide has a wide range of applications in various industries including the food industry, medicine, tissue engineering, wastewater treatment, the pharmaceutical industry and fuel. It is generally recognized as safe when used in accordance with good manufacturing or feeding practice. This review discusses its application in addition to its structural, physical, and chemical properties.

Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

This paper attempts to investigate the impact of economic growth and CO2 emissions on energy consumption for a global panel of 58 countries using dynamic panel data model estimated by means of the Generalized Method of Moments (GMM) for the period 1990-2012. We also estimate this relationship for three regional panels; namely, from Europe and North Asia, Latin America and Caribbean, and Sub-Saharan, North African and Middle Eastern. The empirical evidence indicates significant positive impact of CO2 emissions on energy consumption for four global panels. Economic growth has a positive impact on energy consumption and statistically significant only for the four panel.

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

Dehydrins (DHNs), or group 2 LEA (Late Embryogenesis Abundant) proteins, play a fundamental role in plant response and adaptation to abiotic stresses. They accumulate typically in maturing seeds or are induced in vegetative tissues following salinity, dehydration, cold, and freezing stress. The generally accepted classification of dehydrins is based on their structural features, such as the presence of conserved sequences, designated as Y, S, and K segments. The K segment representing a highly conserved 15 amino acid motif forming amphiphilic α-helix is especially important since it has been found in all dehydrins. Since more than 20 years, they are thought to play an important protective role during cellular dehydration but their precise function remains unclear. This review outlines the current status of the progress made towards the structural, physico-chemical and functional characterization of plant dehydrins and how these features could be exploited in improving stress tolerance in plants.

Public health recommendations and governmental measures during the new coronavirus disease (COVID-19) pandemic have enforced numerous restrictions on daily living including social distancing, isolation, and home confinement. While these measures are imperative to mitigate spreading of COVID-19, the impact of these restrictions on psychosocial health is undefined. Therefore, an international online survey was launched in April 2020 to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from more than one thousand responders on social participation and life satisfaction. Methods: Thirty-five research organizations from Europe, North-Africa, Western Asia, and the Americas promoted the survey through their networks to the general society, in 7 languages (English, German, French, Arabic, Spanish, Portuguese, and Slovenian). Questions were presented in a differential format with questions related to responses “before” and “during” confinement conditions. Results: 1047 participations (54% women) from Asia (36%), Africa (40%), Europe (21%), and others (3%) were included in the analysis. Findings revealed psychosocial strain during the enforced COVID-19 home confinement. Large decreases (p < 0.001) in the amount of social activity through family (−58%), friends/neighbors (−44.9%), or entertainment (−46.7%) were triggered by the enforced confinement. These negative effects on social participation were also associated with lower life satisfaction (−30.5%) during the confinement period. Conversely, the social contact score through digital technologies significantly increased (p < 0.001) during the confinement period with more individuals (+24.8%) being socially connected through digital technology. Conclusion: These preliminary findings elucidate the risk of psychosocial strain during the early COVID-19 home confinement period in 2020. Therefore, in order to mitigate the negative psychosocial effects of home confinement, implementation of national strategies focused on promoting social inclusion through a technology-based solution is strongly suggested.

Nanophotocatalyst MnTiO3 powders were synthesized by sol-gel technique and characterized by means of X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), as well as their thermal behavior has been studied by differential thermal and thermogravimetric analyses (DTA/TGA). Powders morphology was analyzed by means of Transmission electron microscopy (TEM). Effect of various process parameters like amount of photocatalyst, dye concentration, solution pH and irradiation time on the extent of removal of dye were studied in detail. The photo-degradation was relatively higher using lower amount (0.005 g) of semiconductor, reached rate of 75% and 70% after 240 min for mixed MnTiO3/TiO2 and pure MnTiO3 nanocatalysts. The kinetic model of photocatalytic degradation of MB follows pseudo-first-order kinetic with a high correlation coefficient values (R2 > 0.95). These results underline the use of effective, low-cost and easily available MnTiO3 photocatalyst for the decomposition of pollutants to water under natural sunlight.

Although recognised as effective measures to curb the spread of the COVID-19 outbreak, social distancing and self-isolation have been suggested to generate a burden throughout the population. To provide scientific data to help identify risk factors for the psychosocial strain during the COVID-19 outbreak, an international cross-disciplinary online survey was circulated in April 2020. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement. The ECLB-COVID19 electronic survey was designed by a steering group of multidisciplinary scientists, following a structured review of the literature. The survey was uploaded and shared on the Google online survey platform and was promoted by thirty-five research organizations from Europe, North Africa, Western Asia and the Americas. Questions were presented in a differential format with questions related to responses "before" and "during" the confinement period. 1047 replies (54% women) from Western Asia (36%), North Africa (40%), Europe (21%) and other continents (3%) were analysed. The COVID-19 home confinement evoked a negative effect on mental wellbeing and emotional status (P < 0.001; 0.43 ≤ d ≤ 0.65) with a greater proportion of individuals experiencing psychosocial and emotional disorders (+10% to +16.5%). These psychosocial tolls were associated with unhealthy lifestyle behaviours with a greater proportion of individuals experiencing (i) physical (+15.2%) and social (+71.2%) inactivity, (ii) poor sleep quality (+12.8%), (iii) unhealthy diet behaviours (+10%), and (iv) unemployment (6%). Conversely, participants demonstrated a greater use (+15%) of technology during the confinement period. These findings elucidate the risk of psychosocial strain during the COVID-19 home confinement period and provide a clear remit for the urgent implementation of technology-based intervention to foster an Active and Healthy Confinement Lifestyle AHCL).

Numerous studies have reported beneficial effects of antioxidant drugs on semen quality, but there is no well-defined therapeutical protocol in male infertility. This study aimed to test the effects of vitamin E and selenium supplementation on lipid peroxidation and on sperm parameters. The study included 54 voluntary and infertile men who produced semen samples for spermiogram and for spectrophotometric measurement of a lipid peroxidation marker, the malondialdehyde (MDA), and produced blood samples for high-performance liquid chromatography assessment of serum vitamin E level. The trial was randomized and open. Twenty-eight men were supplemented daily by vitamin E (400 mg) and selenium (225 microg), during 3 months. The remaining 26 patients received vitamin B (4,5 g/day) for the same duration. Only 20 patients achieved their treatment and returned for control analysis. MDA concentrations in sperm were much less than in seminal plasma and motility and viability were inversely correlated with semen MDA levels. In contrast to vitamin B supplementation, vitamin E and selenium supplementation produced a significant decrease in MDA concentrations and an improvement of sperm motility. The results confirm the protective and beneficial effects of vitamin E and selenium on semen quality and advocate their use in male infertility treatment.

This study presents a study of the reduction in battery stresses by using supercapacitors (SCs) in a 500-kVA rated UPS. We aim at investigating the optimal supercapacitors-battery combination versus the SCs cost. This investigation is threefold; first, supercapacitors and battery models developed using MATLAB/Simulink are presented and validated. Second, the architecture and the simulation of the designed system that combines the SCs and the battery are shown. The supercapacitors are used as high-power storage devices to smooth the peak power applied to the battery during backup time and to deliver full power during short grid outages. By charging the SCs through the battery at a suitable rate, all impulse power demands would be satisfied by the supercapacitors. Third, extensive simulations are carried out to determine the gain in battery RMS current, the gain in energy losses, the energy efficiency and the elimination rate of surge load power. These four performance parameters are determined by simulation and then analyzed. The influence of the SCs recharge on the performance indicators is highlighted. A thorough analysis involving optimal study proposes to draw the optimal SCs number and filter constant from the variation of the aforementioned parameters versus the cost of the SCs.