Structure du marché du travail en Côte d’Ivoire : une étude descriptive à la lumière des programmes et projets publics d’emploi
Thesis Subjects > Social Sciences Ibero-american International University > Research > Doctoral Thesis Cerrado Francés L’économie ivoirienne a connu un dynamisme au cours de la dernière décennie, marqué par des taux de croissance du Produit Intérieur Brut (PIB) successifs de 7% en moyenne. Cependant, le pays reste confronté au problème de sous-emploi notamment des jeunes, en dépit d’un faible niveau de taux de chômage (2,8% en moyenne), voilant une précarité des emplois et un chômage accru chez les jeunes diplômés. D’où la nécessité de disséquer les composantes du marché du travail afin d’élaborer des politiques publiques d’emploi plus adaptées.La présente recherche décrit la structure actuelle du marché du travail, ses interactions avec les différents acteurs, avec un focus sur l’efficacité de certains programmes et projets d’emploi. La méthodologie utilisée est basée sur les méthodes statistiques quantitatives d’analyse descriptive, notamment l’analyse factorielle. Par ailleurs, l’analyse de l’efficacité des projets et programmes s’est faite à l’aide des outils d’analyse de la science indicamétrique. Les données de cette recherche proviennent de l’Enquête Nationale sur l’Emploi (ENE) réalisée en 2019 auprès de plus de 10 000 ménages. Les analyses mettent en exergue les principales caractéristiques suivantes du marché du travail ivoirien :-Les femmes sont désavantagées sur le marché du travail par rapport aux hommes, notamment en milieu urbain ;-Les personnes moins instruites ou n’ayant aucun diplôme sont plus insérées que celles plus instruites ;-Les jeunes détenteurs de diplômes de l’enseignement technique et professionnel sont plus insérés que leurs homologues détenteurs de diplômes de l’enseignement général ;-Le chômage est plus élevé chez les jeunes de moins de 35 ans par rapport aux autres groupes d’âge ;-Le chômage est plus élevé chez les personnes célibataires par rapport à celles en union ;-La prise en compte des capacités intrinsèques des gestionnaires des projets accroit significativement leur probabilité de succès. metadata Meite, Inza mail mitmsginza@yahoo.fr (2024) Structure du marché du travail en Côte d’Ivoire : une étude descriptive à la lumière des programmes et projets publics d’emploi. Doctoral thesis, UNSPECIFIED.
Full text not available from this repository.Abstract
L’économie ivoirienne a connu un dynamisme au cours de la dernière décennie, marqué par des taux de croissance du Produit Intérieur Brut (PIB) successifs de 7% en moyenne. Cependant, le pays reste confronté au problème de sous-emploi notamment des jeunes, en dépit d’un faible niveau de taux de chômage (2,8% en moyenne), voilant une précarité des emplois et un chômage accru chez les jeunes diplômés. D’où la nécessité de disséquer les composantes du marché du travail afin d’élaborer des politiques publiques d’emploi plus adaptées.La présente recherche décrit la structure actuelle du marché du travail, ses interactions avec les différents acteurs, avec un focus sur l’efficacité de certains programmes et projets d’emploi. La méthodologie utilisée est basée sur les méthodes statistiques quantitatives d’analyse descriptive, notamment l’analyse factorielle. Par ailleurs, l’analyse de l’efficacité des projets et programmes s’est faite à l’aide des outils d’analyse de la science indicamétrique. Les données de cette recherche proviennent de l’Enquête Nationale sur l’Emploi (ENE) réalisée en 2019 auprès de plus de 10 000 ménages. Les analyses mettent en exergue les principales caractéristiques suivantes du marché du travail ivoirien :-Les femmes sont désavantagées sur le marché du travail par rapport aux hommes, notamment en milieu urbain ;-Les personnes moins instruites ou n’ayant aucun diplôme sont plus insérées que celles plus instruites ;-Les jeunes détenteurs de diplômes de l’enseignement technique et professionnel sont plus insérés que leurs homologues détenteurs de diplômes de l’enseignement général ;-Le chômage est plus élevé chez les jeunes de moins de 35 ans par rapport aux autres groupes d’âge ;-Le chômage est plus élevé chez les personnes célibataires par rapport à celles en union ;-La prise en compte des capacités intrinsèques des gestionnaires des projets accroit significativement leur probabilité de succès.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Marché du travail, emploi, chômage, projets, programmes, insertion, indicamétrie |
| Subjects: | Subjects > Social Sciences |
| Divisions: | Ibero-american International University > Research > Doctoral Thesis |
| Date Deposited: | 22 Sep 2023 23:30 |
| Last Modified: | 08 Jul 2024 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/1393 |
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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.
Aisha Naseer mail , Madiha Amjad mail , Ali Raza mail , Kashif Munir mail , Aseel Smerat mail , Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Carlos Eduardo Uc Ríos mail carlos.uc@unini.edu.mx, Imran Ashraf mail ,
Naseer
<a href="/17593/1/s41598-025-95448-2.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Client engagement solution for post implementation issues in software industry using blockchain
In the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively.
Muhammad Shoaib Farooq mail , Khurram Irshad mail , Danish Riaz mail , Nagwan Abdel Samee mail , Ernesto Bautista Thompson mail ernesto.bautista@unini.edu.mx, Daniel Gavilanes Aray mail daniel.gavilanes@uneatlantico.es, Imran Ashraf mail ,
Farooq
<a href="/17061/1/fspor-1-1565900.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.
Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Antonio Bores-Cerezal mail antonio.bores@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Julio Calleja-González mail ,
Velarde-Sotres
<a class="ep_document_link" href="/17139/1/s41598-025-89266-9.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.
Muhammad Shoaib Farooq mail , Rizwan Pervez Mir mail , Atif Alvi mail , Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Fadwa Alrowais mail , Hanen Karamti mail , Imran Ashraf mail ,
Farooq
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Efficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into predefined categories. This research presents a unique feature identification technique based on suppression to locate interest points by computing productive sum of pixel derivatives by computing the differentials for corner scores. Scale space interpolation is applied to define interest points by combining color features from spatially ordered L2 normalized coefficients with shape and object information. Object based feature vectors are formed using high variance coefficients to reduce the complexity and are converted into bag-of-visual-words (BoVW) for effective retrieval and ranking. The presented method encompass feature vectors for information synthesis and improves the discriminating strength of the retrieval system by extracting deep image features including primitive, spatial, and overlayed using multilayer fusion of Convolutional Neural Networks(CNNs). Extensive experimentation is performed on standard image datasets benchmarks, including ALOT, Cifar-10, Corel-10k, Tropical Fruits, and Zubud. These datasets cover wide range of categories including shape, color, texture, spatial, and complicated objects. Experimental results demonstrate considerable improvements in precision and recall rates, average retrieval precision and recall, and mean average precision and recall rates across various image semantic groups within versatile datasets. The integration of traditional feature extraction methods fusion with multilevel CNN advances image sensing and retrieval systems, promising more accurate and efficient image retrieval solutions.
Jyotismita Chaki mail , Aiza Shabir mail , Khawaja Tehseen Ahmed mail , Arif Mahmood mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,
Chaki