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This research focuses on the analytical and numerical analysis of a nonlinear second-order Volterra-Fredholm integro-differential equation with two algebraic weakly singular kernels. We rigorously establish the existence and uniqueness of the solution using Krasnoselskii’s fixed-point theorem, which elegantly addresses the nonlinear structure of the equation. To approximate the solution, we employ the Nyström method combined with the product integration technique, specifically designed to overcome the challenges posed by the weak singularities. We conduct extensive numerical experiments to demonstrate the performance and the accuracy of our proposed approach. The results not only validate our theoretical findings but also underscore the method’s effectiveness in solving similar classes of integro-differential equations. This study advances our understanding of numerical methods for singular and nonlinear equations, offering valuable insights into their potential applications.

The aim of this paper is to clarify the role of the strategic monitoring in the process of internationalization of the Moroccan SMEs in the Sub-Saharan African market and to identify the determinants and risks that influence the success or the failure of this internationalization. The objective is to assess a roadmap for the practice of strategic monitoring adapted to SMEs in the African context. By conducting a study on 14 Moroccan SMEs, the study’s findings confirm that the strategic monitoring practices are influenced by the perceived uncertainties of internationalization relative to six environmental sectors. The management of uncertainty depends on the company's ability and its informational skills to access useful and valid information on the national and international markets. Managing the complexity of a constantly volatile environment requires strategic intelligence stimulated by strategic uncertainty. The results of analysing the collected information enable exporting SMEs to make the right strategic decisions and consequently reduce and/or eliminate perceived uncertainties.

As patients with COVID-19 pneumonia admitted to intensive care unit (ICU) have high rates of thrombosis, high doses of thromboprophylaxis have been proposed. The associated bleeding risk remains unknown. We investigated major bleeding complications in ICU COVID-19 patients and we examined their relationship with inflammation and thromboprophylaxis. Retrospective monocentric study of consecutive adult patients admitted in ICU for COVID-19 pneumonia requiring mechanical ventilation. Data collected included demographics, anticoagulation status, coagulation tests and outcomes including major bleeding and thrombotic events. Among 56 ICU COVID-19 patients, 10 (18%) patients had major bleeding and 16 (29%) thrombotic events. Major bleeding occurred later than thrombosis after ICU admission [17(14–23) days versus 9(3–11) days respectively (p = 0.005)]. Fibrinogen concentration always decreased several days [4(3–5) days] before bleeding; D-dimers followed the same trend. All bleeding patients were treated with anticoagulants and anticoagulation was overdosed for 6 (60%) patients on the day of bleeding or the day before. In the whole cohort, overdose was measured in 22 and 78% of patients receiving therapeutic anticoagulation during fibrinogen increase and decrease respectively (p < 0.05). Coagulation disorders had biphasic evolution during COVID-19: first thrombotic events during initial hyperinflammation, then bleeding events once inflammation reduced, as confirmed by fibrinogen and d-dimers decrease. Most bleeding events complicated heparin overdose, promoted by inflammation decrease, suggesting to carefully monitor heparin during COVID-19. Thromboprophylaxis may be adapted to this biphasic evolution, with initial high doses reduced to standard doses once the high thrombotic risk period ends and fibrinogen decreases, to prevent bleeding events.

The huge quantity of information and the high spectral resolution of hyperspectral imagery present a challenge when performing traditional processing techniques such as classification. Dimensionality and noise reduction improves both efficiency and accuracy, while retaining essential information. Among the many dimensionality reduction methods, Independent Component Analysis (ICA) is one of the most popular techniques. However, ICA is computationally costly, and given the absence of specific criteria for component selection, constrains its application in high-dimension data analysis. To overcome this limitation, we propose a novel approach that applies Discrete Cosine Transform (DCT) as preprocessing for ICA. Our method exploits the unique capacity of DCT to pack signal energy in few low-frequency coefficients, thus reducing noise and computation time. Subsequently, ICA is applied on this reduced data to make the output components as independent as possible for subsequent hyperspectral classification. To evaluate this novel approach, the reduced data using (1) ICA without preprocessing; (2) ICA with the commonly used preprocessing techniques which is Principal Component Analysis (PCA); and (3) ICA with DCT preprocessing are tested with Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) classifiers on two real hyperspectral datasets. Experimental results in both instances indicate that data after our proposed DCT preprocessing method combined with ICA yields superior hyperspectral classification accuracy.

This paper presents an experimental study on a set of 30 specimens, tested on three-point bending, divided into two categories. With the insertion of geogrids and cathodic emulsions, the first category consists of 14 prismatic beams and the second of 16 pre-cracked and reinforced slab specimens. In situ tests were carried out using a heavy deflectometer (HWD) on a flexible runway of an airfield located in the city of Ouragla (800 km south-east of Algiers), before and after its reinforcement. This work showed, with a numerical calibration, that the geogrid with emulsion, improves the displacements and the stresses approximately 30% and increases the modulus of elasticity and the modulus of rupture (MOR) by 60% and 20%, respectively. The damping coefficient (k) can reach the value of 2 to 5, which increases the longevity of a reinforced flexible pavement.

Dynamic Partial Reconfiguration (DPR) has emerged as a key enabler of runtime adaptability and hardware-software co-design in modern FPGA-based heterogeneous systems. However, with the transition toward 5nm technologies and multi-die 3D-IC architectures, spatial resource management faces a “complexity wall,” where traditional manual floorplanning techniques struggle to satisfy timing, utilization, and scalability constraints. This study presents a systematic literature review and proposes a comprehensive taxonomy of FPGA floorplanning and placement methodologies developed over the past two decades. The proposed classification organizes existing approaches into three generations: 1) the Heuristic Era, focused on rule-based automation and physical feasibility; 2) the Optimization Era, characterized by formal mathematical models and Mixed-Integer Linear Programming (MILP) for heterogeneous resource allocation; and 3) the Autonomous Era, which leverages AI-driven techniques, including Reinforcement Learning and intelligent scheduling, to enable predictive and shape-adaptive placement strategies. This evolution reflects a fundamental shift from static grid-based management toward elastic, self-optimizing FPGA fabrics. We further examine emerging architectural constraints, including Super Logic Region (SLR) boundaries and hierarchical nested Partial Reconfigurable Regions (PRRs). Beyond this taxonomy, the survey identifies a critical scalability–optimality trade-off, highlighting the need for hybrid frameworks that combine the formal guarantees of optimization-based methods with the real-time adaptability of AI-driven approaches. It further establishes a unifying perspective in which DPR is evolving from a logic-reconfiguration mechanism into a thermal–spatial management paradigm for mitigating heat in high-density 3D-IC systems. Finally, the analysis reveals a significant functional–physical gap in current autonomous design tools, emphasizing the need for context-aware agents capable of jointly reasoning about temporal task dependencies and spatial floorplanning constraints. This review provides a structured roadmap for the development of next-generation intelligent control frameworks for edge and cloud-scale reconfigurable computing systems.

The fear of cancer recurrence is an important topic in the healthcare field. In general, approximately 40% of survivors experience high levels of fear of recurrence. This study aims to fill this gap by synthesizing the findings of systematic reviews studies investigating ecosystems, correlates or predictors, and barriers and facilitators of fear of cancer recurrence among cancer survivors. An umbrella meta-synthesis was conducted using the following databases: MEDLINE, PsycINFO, PsycARTICLES, CINAHL, Business source premier, and SOCindex, ending in April 2024 with PRISMA methods. A total of 24 systematic reviews, representing 729 articles, were included in the study. In total, six ecosystems were identified, including family, work, friends, the healthcare system, caregivers, and religion. As part of this umbrella review, 55 specific ecosystemic factors were identified that may contribute to fear of cancer recurrence. Furthermore, the umbrella review identified 12 facilitators and 12 barriers related to fear of cancer recurrence. This umbrella meta-synthesis contributed significantly to our review’s strength in synthesizing the main ecosystem and its influence on fears of cancer recurrence. Understanding the interdependence of ecosystems should enable future research on intervention effectiveness or the development of interventions that could reduce the fear of cancer recurrence.

Drawing on self-regulatory perspective, this study investigates how protean individuals self-regulate their thoughts, cognition by the set-up of self-regulation tactics in order to develop their careers. Based on a sample of 423 French engineers, we tested a structural equation modeling. Our results highlighted that protean career orientation had a significant relationship with career behaviors via self-regulation tactics (planification and motivation to learn). These findings support the idea that protean individuals are more able to self-regulate in order to develop their career. The implications for theory and practice are discussed.

In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.