University of Béjaïa

Uncertain times require prompt reflexes to survive and this study is a collaborative reflex to better understand uncertainty and navigate through it. The Coronavirus (Covid-19) pandemic hit hard and interrupted many dimensions of our lives, particularly education. As a response to interruption of education due to the Covid-19 pandemic, this study is a collaborative reaction that narrates the overall view, reflections from the K12 and higher educational landscape, lessons learned and suggestions from a total of 31 countries across the world with a representation of 62.7% of the whole world population. In addition to the value of each case by country, the synthesis of this research suggests that the current practices can be defined as emergency remote education and this practice is different from planned practices such as distance education, online learning or other derivations. Above all, this study points out how social injustice, inequity and the digital divide have been exacerbated during the pandemic and need unique and targeted measures if they are to be addressed. While there are support communities and mechanisms, parents are overburdened between regular daily/professional duties and emerging educational roles, and all parties are experiencing trauma, psychological pressure and anxiety to various degrees, which necessitates a pedagogy of care, affection and empathy. In terms of educational processes, the interruption of education signifies the importance of openness in education and highlights issues that should be taken into consideration such as using alternative assessment and evaluation methods as well as concerns about surveillance, ethics, and data privacy resulting from nearly exclusive dependency on online solutions.

Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyper-parameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

Uncertain times require prompt reflexes to survive and this study is a collaborative reflex to better understand uncertainty and navigate through it. The Coronavirus (Covid-19) pandemic hit hard and interrupted many dimensions of our lives, particularly education. As a response to interruption of education due to the Covid-19 pandemic, this study is a collaborative reaction that narrates the overall view, reflections from the K12 and higher educational landscape, lessons learned and suggestions from a total of 31 countries across the world with a representation of 62.7% of the whole world population. In addition to the value of each case by country, the synthesis of this research suggests that the current practices can be defined as emergency remote education and this practice is different from planned practices such as distance education, online learning or other derivations. Above all, this study points out how social injustice, inequity and the digital divide have been exacerbated during the pandemic and need unique and targeted measures if they are to be addressed. While there are support communities and mechanisms, parents are overburdened between regular daily/professional duties and emerging educational roles, and all parties are experiencing trauma, psychological pressure and anxiety to various degrees, which necessitates a pedagogy of care, affection and empathy. In terms of educational processes, the interruption of education signifies the importance of openness in education and highlights issues that should be taken into consideration such as using alternative assessment and evaluation methods as well as concerns about surveillance, ethics, and data privacy resulting from nearly exclusive dependency on online solutions.

We compare the science capabilities of different eLISA mission designs, including four-link (two-arm) and six-link (three-arm) configurations with different arm lengths, low-frequency noise sensitivities and mission durations. For each of these configurations we consider a few representative massive black hole formation scenarios. These scenarios are chosen to explore two physical mechanisms that greatly affect eLISA rates, namely (i) black hole seeding, and (ii) the delays between the merger of two galaxies and the merger of the black holes hosted by those galaxies. We assess the eLISA parameter estimation accuracy using a Fisher matrix analysis with spin-precessing, inspiral-only waveforms. We quantify the information present in the merger and ringdown by rescaling the inspiral-only Fisher matrix estimates using the signal-to-noise ratio from nonprecessing inspiral-merger-ringdown phenomenological waveforms, and from a reduced set of precessing numerical relativity/post-Newtonian hybrid waveforms. We find that all of the eLISA configurations considered in our study should detect some massive black hole binaries. However, configurations with six links and better low-frequency noise will provide much more information on the origin of black holes at high redshifts and on their accretion history, and they may allow the identification of electromagnetic counterparts to massive black hole mergers.

Cancer is one of the major deadly diseases globally. The alarming rise in the mortality rate due to this disease attracks attention towards discovering potent anticancer agents to overcome its mortality rate. The discovery of novel and effective anticancer agents from natural sources has been the main point of interest in pharmaceutical research because of attractive natural therapeutic agents with an immense chemical diversity in species of animals, plants, and microorganisms. More than 60% of contemporary anticancer drugs, in one form or another, have originated from natural sources. Plants and microbial species are chosen based on their composition, ecology, phytochemical, and ethnopharmacological properties. Plants and their derivatives have played a significant role in producing effective anticancer agents. Some plant derivatives include vincristine, vinblastine, irinotecan, topotecan, etoposide, podophyllotoxin, and paclitaxel. Based on their particular activity, a number of other plant-derived bioactive compounds are in the clinical development phase against cancer, such as gimatecan, elomotecan, etc. Additionally, the conjugation of natural compounds with anti-cancerous drugs, or some polymeric carriers particularly targeted to epitopes on the site of interest to tumors, can generate effective targeted treatment therapies. Cognizance from such pharmaceutical research studies would yield alternative drug development strategies through natural sources which could be economical, more reliable, and safe to use.

The resort worldwide to edible medicinal plants for medical care has increased significantly during the last few years. Currently, there is a renewed interest in the search for new phytochemicals that could be developed as useful anti-inflammatory and anti-allergic agents to reduce the risk of many diseases. The activation of nuclear transcription factor-kappa B (NF-κB) has now been linked to a variety of inflammatory diseases, while data from numerous studies underline the importance of phytochemicals in inhibiting the pathway that activates this transcription factor. Moreover, the incidence of type I allergic disorders has been increasing worldwide, particularly, the hypersensitivity to food. Thus, a good number of plant products with anti-inflammatory and anti-allergic activity have been documented, but very few of these compounds have reached clinical use and there is scant scientific evidence that could explain their mode of action. Therefore, this paper intends to review the most salient recent reports on the anti-inflammatory and anti-allergic properties of phytochemicals and the molecular mechanisms underlying these properties.

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre‐processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.

The Routing Protocol for Low-Power and Lossy Networks (RPL) is the standardized routing protocol for constrained environments such as 6LoWPAN networks, and is considered as the routing protocol of the Internet of Things (IoT). However, this protocol is subject to several internal and external attacks. In fact, RPL is facing many issues. Among these issues, trust management is a real challenge when deploying RPL. In this paper, we highlight and discuss the different issues of trust management in RPL. We consider that using only TPM (Trust Platform Module) to ensure trustworthiness between nodes is not sufficient. Indeed, an internal infected or selfish node could participate in constructing RPL topology. To overcome this issue, we propose to strengthen RPL by adding a new trustworthiness metric during RPL construction and maintenance. This metric represents the level of trust for each node in the network, and is calculated using selfishness, energy, and honesty components. It allows a node to decide whether or not to trust the other nodes during the construction of the topology.

Establishing and maintaining protected areas (PAs) are key tools for biodiversity conservation. However, this approach is insufficient for many species, particularly those that are wide-ranging and sparse. The cheetah Acinonyx jubatus exemplifies such a species and faces extreme challenges to its survival. Here, we show that the global population is estimated at ∼7,100 individuals and confined to 9% of its historical distributional range. However, the majority of current range (77%) occurs outside of PAs, where the species faces multiple threats. Scenario modeling shows that, where growth rates are suppressed outside PAs, extinction rates increase rapidly as the proportion of population protected declines. Sensitivity analysis shows that growth rates within PAs have to be high if they are to compensate for declines outside. Susceptibility of cheetah to rapid decline is evidenced by recent rapid contraction in range, supporting an uplisting of the International Union for the Conservation of Nature (IUCN) Red List threat assessment to endangered. Our results are applicable to other protection-reliant species, which may be subject to systematic underestimation of threat when there is insufficient information outside PAs. Ultimately, conserving many of these species necessitates a paradigm shift in conservation toward a holistic approach that incentivizes protection and promotes sustainable human-wildlife coexistence across large multiple-use landscapes.

Phenolic compounds and antioxidant capacities of ten Algerian date (Phoenix dactylifera L.) cultivars were investigated. The total phenolic, flavonoid, flavonol and condensed tannin contents of the different cultivars were measured using colorimetric methods. Free phenolic acid and flavonoid profiles of the date cultivars were analyzed by high performance liquid chromatography with diode array detection (HPLC-DAD), while antioxidant capacities were evaluated in vitro using scavenging assays of 1,1-diphenyl-2-picrylhydrazyl radical and hydrogen peroxide, ferric reducing power, and ferrous ion chelating ability. The results showed that the cultivars exerted different antioxidant capacities, and had different phenolic acid and flavonoid patterns. Among the tested cultivars, Ghazi, Arechti and Sebt Mira possessed the strongest antioxidant capacities and the highest phenolic contents. Four phenolic acids (gallic, ferulic, coumaric and caffeic acids) and five flavonoids (isoquercetrin, quercetrin, rutin, quercetin and luteolin) were identified and quantified.

This paper presents an approach that combines conventional image processing with deep learning by fusing the features from the individual techniques. We hypothesize that the two techniques, with different error profiles, are synergistic. The conventional image processing arm uses three handcrafted biologically inspired image processing modules and one clinical information module. The image processing modules detect lesion features comparable to clinical dermoscopy information-atypical pigment network, color distribution, and blood vessels. The clinical module includes information submitted to the pathologist-patient age, gender, lesion location, size, and patient history. The deep learning arm utilizes knowledge transfer via a ResNet-50 network that is repurposed to predict the probability of melanoma classification. The classification scores of each individual module from both processing arms are then ensembled utilizing logistic regression to predict an overall melanoma probability. Using cross-validated results of melanoma classification measured by area under the receiver operator characteristic curve (AUC), classification accuracy of 0.94 was obtained for the fusion technique. In comparison, the ResNet-50 deep learning based classifier alone yields an AUC of 0.87 and conventional image processing based classifier yields an AUC of 0.90. Further study of fusion of conventional image processing techniques and deep learning is warranted.

Over the last 20 years, the use of dietary supplements (DS) has continued to grow in many countries. Due to the public health crisis brought on by the COVID-19 pandemic and amidst fears regarding COVID-19 vaccines and their low supply in many regions of the world, there has been a marked interest in the use of DS as alternative means of protecting against and treating this emerging disease, as well as boosting the immune system and minimizing the risk of inflammation. Despite a lack of evidence to suggest their efficacy, a surge in the sales of DS has been reported in many parts of the world. Questions have also been raised about the health effects associated with DS due to their increased use during the health crisis. Numerous scientific studies have demonstrated their beneficial properties as well as some adverse and even toxic effects. In addition, given the current global interest in this issue, a review is needed to establish the status of dietary supplements before and during the health crisis. The aim of this review is to summarize the current evidence on the impact of dietary supplements on the incidence of the COVID-19 pandemic, as well as their regulation and associated market trends. First, we provide an overview of DS, including a comprehensive review of the legislative and regulatory aspects of DS in the USA, China, the EU, and Algeria. Second, we describe the prevalence of the most commonly consumed DS and their efficacy as a prophylactic modality in the era of COVID-19. Additionally, we examine the structure and size of the DS market in the countries that predominantly produce and import them, its global market trend, and the impact of the COVID-19 pandemic on market growth. Finally, in this review, we also discuss the profile of DS users.

The study provides a study on energy storage technologies for photovoltaic and wind systems in response to the growing demand for low-carbon transportation. Energy storage systems (ESSs) have become an emerging area of renewed interest as a critical factor in renewable energy systems. The technology choice depends essentially on system requirements, cost, and performance characteristics. Common types of ESSs for renewable energy sources include electrochemical energy storage (batteries, fuel cells for hydrogen storage, and flow batteries), mechanical energy storage (including pumped hydroelectric energy storage (PHES), gravity energy storage (GES), compressed air energy storage (CAES), and flywheel energy storage), electrical energy storage (such as supercapacitor energy storage (SES), superconducting magnetic energy storage (SMES), and thermal energy storage (TES)), and hybrid or multi-storage systems that combine two or more technologies, such as integrating batteries with pumped hydroelectric storage or using supercapacitors and thermal energy storage. These different categories of ESS enable the storage and release of excess energy from renewable sources to ensure a reliable and stable supply of renewable energy. The optimal storage technology for a specific application in photovoltaic and wind systems will depend on the specific requirements of the system. It is important to carefully evaluate these needs and consider factors, such as power and energy requirements, efficiency, cost, scalability, and durability when selecting an ESS technology.

The prevalence of obesity, diabetes, non-alcoholic fatty liver disease, and related metabolic disorders has been steadily increasing in the past few decades. Apart from the establishment of caloric restrictions in combination with improved physical activity, there are no effective pharmacological treatments for most metabolic disorders. Many scientific-studies have described various beneficial effects of probiotics in regulating metabolism but others questioned their effectiveness and safety. Postbiotics are defined as preparation of inanimate microorganisms, and/or their components, which determine their safety of use and confers a health benefit to the host. Additionally, unlike probiotics postbiotics do not require stringent production/storage conditions. Recently, many lines of evidence demonstrated that postbiotics may be beneficial in metabolic disorders management via several potential effects including anti-inflammatory, antibacterial, immunomodulatory, anti-carcinogenic, antioxidant, antihypertensive, anti-proliferative, and hypocholesterolaemia properties that enhance both the immune system and intestinal barrier functions by acting directly on specific tissues of the intestinal epithelium, but also on various organs or tissues. In view of the many reports that demonstrated the high biological activity and safety of postbiotics, we summarized in the present review the current findings reporting the beneficial effects of various probiotics derivatives for the management of metabolic disorders and related alterations.

Summary Illegal killing/taking of birds is a growing concern across the Mediterranean. However, there are few quantitative data on the species and countries involved. We assessed numbers of individual birds of each species killed/taken illegally in each Mediterranean country per year, using a diverse range of data sources and incorporating expert knowledge. We estimated that 11–36 million individuals per year may be killed/taken illegally in the region, many of them on migration. In each of Cyprus, Egypt, Italy, Lebanon and Syria, more than two million birds may be killed/taken on average each year. For species such as Blackcap Sylvia atricapilla , Common Quail Coturnix coturnix , Eurasian Chaffinch Fringilla coelebs , House Sparrow Passer domesticus and Song Thrush Turdus philomelos , more than one million individuals of each species are estimated to be killed/taken illegally on average every year. Several species of global conservation concern are also reported to be killed/taken illegally in substantial numbers: Eurasian Curlew Numenius arquata , Ferruginous Duck Aythya nyroca and Rock Partridge Alectoris graeca . Birds in the Mediterranean are illegally killed/taken primarily for food, sport and for use as cage-birds or decoys. At the 20 worst locations with the highest reported numbers, 7.9 million individuals may be illegally killed/taken per year, representing 34% of the mean estimated annual regional total number of birds illegally killed/taken for all species combined. Our study highlighted the paucity of data on illegal killing/taking of birds. Monitoring schemes which use systematic sampling protocols are needed to generate increasingly robust data on trends in illegal killing/taking over time and help stakeholders prioritise conservation actions to address this international conservation problem. Large numbers of birds are also hunted legally in the region, but specific totals are generally unavailable. Such data, in combination with improved estimates for illegal killing/taking, are needed for robustly assessing the sustainability of exploitation of birds.

Rapid detection of carbapenem-resistant Acinetobacter baumannii strains is critical and will benefit patient care by optimizing antibiotic therapies and preventing outbreaks. Herein we describe the development and successful application of a mass spectrometry profile generated by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) that utilized the imipenem antibiotic for the detection of carbapenem resistance in a large series of A. baumannii clinical isolates from France and Algeria. A total of 106 A. baumannii strains including 63 well-characterized carbapenemase-producing and 43 non-carbapenemase-producing strains, as well as 43 control strains (7 carbapenem-resistant and 36 carbapenem-sensitive strains) were studied. After an incubation of bacteria with imipenem for up to 4 h, the mixture was centrifuged and the supernatant analyzed by MALDI-TOF MS. The presence and absence of peaks representing imipenem and its natural metabolite was analyzed. The result was interpreted as positive for carbapenemase production if the specific peak for imipenem at 300.0 m/z disappeared during the incubation time and if the peak of the natural metabolite at 254.0 m/z increased as measured by the area under the curves leading to a ratio between the peak for imipenem and its metabolite being <0.5. This assay, which was applied to the large series of A. baumannii clinical isolates, showed a sensitivity of 100.0% and a specificity of 100.0%. Our study is the first to demonstrate that this quick and simple assay can be used as a routine tool as a point-of-care method for the identification of A. baumannii carbapenemase-producers in an effort to prevent outbreaks and the spread of uncontrollable superbugs.

Cucurbita genus has received a renowned interest in the last years. This plant species, native to the Americas, has served worldwide folk medicine for treating gastrointestinal diseases and intestinal parasites, among other clinical conditions. These pharmacological effects have been increasingly correlated with their nutritional and phytochemical composition. Among those chemical constituents, carotenoids, tocopherols, phenols, terpenoids, saponins, sterols, fatty acids, and functional carbohydrates and polysaccharides are those occurring in higher abundance. However, more recently, a huge interest in a class of triterpenoids, cucurbitacins, has been stated, given its renowned biological attributes. In this sense, the present review aims to provide a detailed overview to the folk medicinal uses of Cucurbita plants, and even an in-depth insight on the latest advances with regards to its antimicrobial, antioxidant and anticancer effects. A special emphasis was also given to its clinical effectiveness in humans, specifically in blood glucose levels control in diabetic patients and pharmacotherapeutic effects in low urinary tract diseases.

There are numerous studies indicating that a moderate consumption of red wine provides certain health benefits, such as the protection against neurodegenerative diseases. This protective effect is most likely due to the presence of phenolic compounds in wine. Wine polyphenolic compounds are well known for the antioxidant properties. Oxidative stress is involved in many forms of cellular and molecular deterioration. This damage can lead to cell death and various neurodegenerative disorders, such as Parkinson's or Alzheimer's diseases. Extensive investigations have been undertaken to determine the neuroprotective effects of wine-related polyphenols. In this review we present the neuroprotective abilities of the major classes of wine-related polyphenols.

The crystallization rate of polyamide 11 has been quantified in a wide temperature range between 320 and 450 K, using fast scanning chip calorimetry and differential scanning calorimetry. Different mechanisms of crystal nucleation/growth have been identified at temperatures below and above 370 K, causing a bimodal distribution of the crystallization rate as a function of temperature. Crystallization at low supercooling is connected with formation of triclinic α-crystals of lamellar morphology and ringed/banded spherulites. At high supercooling, formation of pseudohexagonal δ′-mesophase is observed. Because of the high nucleation density at low temperature, growth of the δ′-mesophase is nonspherulitic. The δ′-mesophase transforms on heating to α-crystals without affecting the superstructure. The study is completed by quantification of the cooling conditions to allow δ-crystal formation at low supercooling, δ′-mesophase formation at high supercooling, and complete vitrification of the melt. The interplay between nucleation density and mesophase formation according Ostwald’s rule of stages is discussed as a consequence of immobilization of the amorphous phase/formation of a rigid amorphous fraction.

Bacterial endophytes constitute an essential part of the plant microbiome and are described to promote plant health by different mechanisms. The close interaction with the host leads to important changes in the physiology of the plant. Although beneficial bacteria use the same entrance strategies as bacterial pathogens to colonize and enter the inner plant tissues, the host develops strategies to select and allow the entrance to specific genera of bacteria. In addition, endophytes may modify their own genome to adapt or avoid the defense machinery of the host. The present review gives an overview about bacterial endophytes inhabiting the phytosphere, their diversity, and the interaction with the host. Direct and indirect defenses promoted by the plant-endophyte symbiont exert an important role in controlling plant defenses against different stresses, and here, more specifically, is discussed the role against biotic stress. Defenses that should be considered are the emission of volatiles or antibiotic compounds, but also the induction of basal defenses and boosting plant immunity by priming defenses. The primed defenses may encompass pathogenesis-related protein genes (PR family), antioxidant enzymes, or changes in the secondary metabolism.