PurposeThis paper develops and empirically tests a novel big data analytic model to measure financial well-being (FWB) throughout the customer life cycle. The study uses a comprehensive, proprietary dataset that encompasses demographic and transactional data for nearly 430,000 clients across multiple financial products, approximating an "open finance" setting.Design/methodology/approachThe model combines objective indicators of financial behavior with subjective measures of perceived well-being into a composite, customer-level index. To ensure robustness and practical relevance, the model was refined using feedback from nearly one hundred industry experts and validated through extensive sensitivity analyses. Furthermore, the composite index is closely aligned with independent and nationally representative studies conducted by the Development Bank of Latin America and the Caribbean (CAF), which highlights its external validity.FindingsThe study shows that the big data analytics model can capture the main features of the proposed conceptual model for measuring FWB at different stages of life. The model produces data-driven analytical weight loads that reflect most of the implications of the lifecycle consumption theory. The results validate the analytical model's suitability to be scaled and implemented in an open finance setting.Originality/valueBy providing a scalable, actionable, and empirically validated tool, this research contributes to the literature on FWB measurement and offers financial institutions and consumers the ability to continuously monitor their financial health, while accommodating AI-driven recommendations tailored to improve well-being.
Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma; however, its primary splenic variant (DLBCL-PS) is unusual and can present atypical clinical manifestations. CD30 expression, which is uncommon in this subtype, has relevant prognostic and therapeutic implications. Case presentation: A 48-year-old male patient who presented with abdominal pain, fever, and splenomegaly is described. Following an emergency splenectomy due to an abscess with 70% splenic necrosis, histopathological and immunohistochemical analysis revealed CD30-positive DLBCL-PS with a high proliferative index. The patient was referred to hematology-oncology for specialized management. Discussion: DLBCL-PS represents less than 1% of lymphomas, and its diagnosis is complex when it mimics infectious processes. Splenectomy has diagnostic and therapeutic relevance and is associated with improved survival. CD30 expression defines a subgroup with a better prognosis and potential benefit from targeted therapies such as brentuximab vedotin. Conclusions: This case illustrates the diagnostic challenge of DLBCL-PS when it presents as a complicated splenic abscess and highlights the importance of considering DLBCL in the differential diagnosis of splenic lesions.
In this paper, we construct a theory of existence and uniqueness of solutions for an abstract Cauchy problem u '(t) + Au(t) = h(t), t is an element of (0, T) and u(0) = f, where A is the fractional Laplacian operator acting on a bounded domain with smooth boundary. The construction of the solution is based on the method of temporal discretization.
In this study, the synthesis of iron nanoparticles was optimized using an aqueous extract of Eucalyptus grandis foliage, which was used together with iron (II) chloride tetrahydrate salt and iron (III) chloride hexahydrate salt using water as a solvent, in addition to a basic sodium hydroxide solution. The nanoparticles were precipitated, filtered, and dried, achieving a yield of 98.99%. The synthesized nanoparticles exhibited a specific surface area of 131.90 m2/g. Their functional groups were analyzed using Fourier transform infrared spectroscopy (FTIR), and particle size was determined by transmission electron microscopy (TEM). The behavior of the synthesized nanoparticles during Hg(II) retention was evaluated and assessed. The adsorption isotherm fit the Freundlich model, typical of a heterogeneous adsorption model, with a maximum adsorption capacity for Hg(II) of 274.92 mg Hg/g of nanoparticle. The particle exhibited a good retention percentage for mercury (79.26%), and this synthesized particle reduces the need for reagents in its preparation, generates no polluting waste, and requires low energy input.
