Analyse comparative des initiatives One Health en Guinée et en République Démocratique du Congo: Un appel à l’opérationnalisation

La Guinée et la République Démocratique du Congo (RDC) sont deux pays confrontés à des maladies zoonotiques (ré)émergentes, lesquelles représentent de graves menaces pour la santé publique et pour l’économie. Cela renforce l’importance de mettre l'accent sur les approches interdisciplinaires pour la prévention, la détection et l’atténuation des maladies infectieuses afin de mettre en place des systèmes de réponses adéquats. Dans les dernières années, des efforts ont été fournis dans les deux pays pour la conception, la mise en œuvre et la promotion de l’approche “Une Seule Santé” (One Health) qui offre des solutions à l’interface homme-animal-plante-écosystèmes. Cependant, dans ces pays, il n’existe pas une approche systémique “Une Seule Santé” qui soit réellement opérationnelle. Ainsi, cet article vise à faire une analyse comparative des initiatives « One Health » (OH) en Guinée et en RDC. Les résultats suggèrent qu'il existe un engagement fort de la part du gouvernement guinéen à signer un ordre conjoint de collaboration entre les trois départements clés, mais la coopération et la collaboration entre les différents secteurs et disciplines font défaut. En RDC, trois plateformes existent, mais leurs actions ne sont pas coordonnées, ce qui démontre les lacunes dans la vision globale que devrait avoir l’approche OH. Le défi majeur dans ces deux pays est d'adopter une approche holistique pour dépasser les structures et les paradigmes organisationnels et disciplinaires pour développer une véritable coopération entre tous les secteurs directement ou indirectement touchés par les maladies à potentiel épidémique.   Guinea and the Democratic Republic of Congo (DRC) are two countries facing (re)emerging zoonotic diseases, which pose serious threats to public health and the economy. This reinforces the importance of emphasizing interdisciplinary approaches for the prevention, detection, and mitigation of infectious diseases to put in place adequate response systems. In recent years, efforts have been made in both countries for the design, implementation, and promotion of the “One Health” (OH) approach which offers solutions at the human-animal-animal-plant-ecosystems interface. However, in these countries, there is no operational OH systemic approach. Thus, this article aims to make a comparative analysis of the OH initiatives in Guinea and the DRC. Findings suggest there is a strong commitment on the part of the government of Guinea to sign a joint order of collaboration between the three key departments, but cooperation and collaboration between different sectors and disciplines is lacking. In the DRC, three platforms exist but are not coordinated, which shows gaps in the overall vision that OH should be in the country. The major challenge in these two countries is to adopt a holistic approach to go beyond organizational and disciplinary structures and paradigms to develop real coordination and cooperation between all the sectors directly or indirectly affected by diseases with epidemic potential

An Extension of the Quadratic Error Function for Learning Imprecise Data in Multivariate Nonlinear Regression

Multivariate noises in the learning process are most of the time supposed to follow a standard multivariate normal distribution. This hypothesis does not often hold in many real-world situations. In this paper, we consider an approach based on multivariate skew-normal distribution. It allows for a multiple continuous variation from normality to nonnormality. We give an extension of the generalized least squares error function in a context of multivariate nonlinear regression to learn imprecise data. The simulation study and application case on real datasets conducted and based on multilayer perceptron neural networks (MLP) with bivariate continuous response and asymmetric revealed a significant gain in precision using the new quadratic error function for these types of data rather than using a classical generalized least squares error function having any covariance matrix

Empirical determination of optimal configuration for characteristics of a multilayer perceptron neural network in nonlinear regression

In this paper, we determine an optimal configuration for characteristics of a multilayer perceptron neural network (MPL) in nonlinear  regression for predicting crop yield. Monte Carlo simulation approach has been used to train several databases generated by varying the internal structure of 3-MLP from simple to complex for 5 different algorithms most commonly used. Results showed that the optimal configuration is obtained with the Levenberg Marquard algorithm, 75% of the number of input variables as number of hidden nodes, learning rate 40%, minimum sample size 150, tangent hyperbolic and exponential functions in the hidden and output layers respectively. This configuration has been illustrated with real life data. Key words: artificial neural network; machine learning; sample-size effect; nonlinear models; predictio

Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables: State of the art and perspectives

Deep Learning (DL), a type of Machine Learning, has gained significant interest in many fields, including agriculture. This paper aims to shed light on deep learning techniques used in agriculture for abiotic and biotic stress detection in fruits and vegetables, their benefits, and the challenges faced by users. Scientific papers were collected from Web of Science, Scopus, Google Scholar, Springer, and Directory of Open Access Journals (DOAJ) using combinations of specific keywords such as:’Deep Learning’ OR’Artificial Intelligence’ in combination with fruit disease’, vegetable disease’, ‘fruit stress', OR ‘vegetable stress' following PRISMA guidelines. From the initial 818 papers identified using the keywords, 132 were reviewed after excluding books, reviews, and the irrelevant. The recovered scientific papers were from 2003 to 2022; 93 % addressed biotic stress on fruits and vegetables. The most common biotic stresses on species are fungal diseases (grey spots, brown spots, black spots, downy mildew, powdery mildew, and anthracnose). Few studies were interested in abiotic stresses (nutrient deficiency, water stress, light intensity, and heavy metal contamination). Deep Learning and Convolutional Neural Networks were the most used keywords, with GoogleNet (18.28%), ResNet50 (16.67%), and VGG16 (16.67%) as the most used architectures. Fifty-two percent of the data used to compile these models come from the fields, followed by data obtained online. Precision problems due to unbalanced classes and the small size of some databases were also analyzed. We provided the research gaps and some perspectives from the reviewed papers. Further research works are required for a deep understanding of the use of machine learning techniques in fruit and vegetable studies: collection of large datasets according to different scenarios on fruit and vegetable diseases, evaluation of the effect of climatic variability on the fruit and vegetable yield using AI methods and more abiotic stress studies

Diversity and conservation prioritisation of plant species utilised by communities living in the forest areas managed by the Benin National Timber Office

The diversity of forest resources is threatened in its current form of exploitation by rural communities. In order to contribute to the sustainable use of forest resources, this study aimed to assess the diversity of ‘utilitarian’ plant species (those that are useful to humans) among communities living in the 11 classified forests managed by the National Timber Office of Benin (Office National du Bois du Bénin, ONAB) and to identify priority species for conservation. Semi-structured interviews were conducted with 385 riparian residents of the forests using a simple random sampling method. The data collected focused mainly on plant biodiversity. The ecological parameters of habitats, such as the number of genera and species according to families, were calculated. In addition, the prioritisation method used four approaches and eight criteria that made it possible to identify priority species for conservation. Overall, the study revealed the existence of 97 utilitarian species divided into 33 families and 76 genera in the forest areas. The ten priority species for conservation, in order of priority as per the point score procedure, are: Khaya senegalensis, Afzelia africana, Khaya grandifoliola, Pterocarpus erinaceus, Anogeissus leiocarpa, Milicia excelsa, Albizia zygia, Vitex doniana, Antidesma laciniatum and Bombax costatum. This study provides scientific support for conservation planning and as a decision-making tool for the socio-economic conservation of these species

Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model

Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements

Exploratory analysis of the knowledge, attitudes and perceptions of healthcare workers about arboviruses in the context of surveillance in the Republic of Guinea.

BackgroundThe escalating risk and contemporary occurrences of arbovirus infections prompt a critical inquiry into the ability of nations to execute efficient surveillance systems capable to detect, prevent and respond to arbovirus outbreaks. Healthcare workers (HCWs) are the major actors in the surveillance of infectious diseases with epidemic potential. The objective of this study was to evaluate the knowledge, attitudes and perceptions of HCWs regarding arboviruses in the public health facilities of Conakry, Guinea.MethodsA cross-sectional survey was conducted during the from December 27, 2022, to January 31, 2023, encompassing from public health facilities in Conakry. The data collection process encompassed various aspects, including the characteristics of health facilities, socio-demographic and professional attributes of HCWs, the information received concerning arboviruses and the sources of information, as well as a series of inquiries designed to evaluate their knowledge, attitudes and perceptions. Subsequently, scores were computed for knowledge, attitude and perception. To identify the factors influencing the knowledge, attitudes, and perceptions of HCWs regarding arboviruses, Decision Tree and Inference Conditional Tree models were used.ResultsA total of 352 HCWs participated in the study, comprising 219 from national hospitals, 72 from municipal hospitals and 61 from primary health centers. More than half of the respondents (54.3%) had never received information on arboviruses. Only 1% of the respondents had good knowledge about arboviruses, 95.7% had a negative attitude about arboviruses. Moreover, nearly 60% of the respondents had a moderate perception and 24.1% had a good perception. The analysis revealed significant associations between the knowledge and attitudes of respondents concerning arboviruses and their years of professional experience and age.ConclusionThis study highlights the imperative requirement for comprehensive training targeting HCWs to enhance their capacity for early case detection within healthcare facilities. Additionally, there is a crucial need for analogous studies adopting a mixed-methods approach across all healthcare regions in Guinea