Abstract

Abstract

The following article seeks to understand the consumer from a viewpoint of the tribes of consumption. In turn, position itself as a new alternative methodology for market research companies as an innovative approach to segmentation. Field explorations visits are exposed to various tribes’ consumption of Lima. In total we dive in nine cultural communities: skaters, gamers, surfers, k-popers, athletics, hipsters, otaku, music lovers and graffiti artists. We focus first on society and kindred relationship. In second hand, its features and applications of marketing. The article concludes with a reflection on key elements of the criterion of consumer segmentation tribes.

Abstract

Abstract

Aquest article analitza la segmentació del mercat laboral a Catalunya entre 2014 i 2018,
utilitzant microdades de l’Enquesta Quadriennal d’Estructura Salarial per identificar i estudiar
conglomerats de llocs de treball. L'objectiu és sistematitzar la diversitat de situacions
laborals i les desigualtats en condicions, intensitat de treball i remuneracions, vinculant
aquestes variables amb les característiques dels treballadors i l'estructura productiva. A més
a més, es distribueixen aquests grups per branques d’activitat, categories professionals i
dimensions empresarials, i es revisa qui ocupa aquests llocs, posant especial atenció a la
segregació per sexe, nacionalitat, edat i nivell educatiu. Finalment, s'estudia l’evolució
temporal dels segments laborals i les seves condicions entre 2014 i 2018. L'anàlisi temporal
ofereix una comprensió més profunda de la desigualtat salarial i la segmentació del mercat
laboral en relació amb el model productiu i els mecanismes d'ajust en un context marcat per
la progressiva reactivació econòmica després dels màxims d’atur assolits el 2013.

Abstract

Traditional data augmentation methods, which employ static hyperparameters, often lead to model overfitting. To address this limitation, a novel hyperparameter-driven data augmentation approach (HDAug) is introduced in this study. Training images that simulate a plethora of lighting and exposure conditions are synthesized by HDAug through the stochastic sampling of augmentation hyperparameters, within predefined ranges. Additionally, HDAug does not rely on prior knowledge of specific datasets, endowing it with superior generalization capabilities. The Dice coefficient was utilized as the primary evaluation metric. Experimental results demonstrate that HDAug achieves significant performance improvements in two challenging cross-modality medical image segmentation datasets, with average Dice coefficients of 86.77%, 88.08%, and 84.11%, respectively. The superiority of HDAug lies in its ability to substantially enhance model robustness across diverse imaging conditions while circumventing the overfitting issues inherent in conventional methods. Furthermore, HDAug is computationally efficient and is integrated into existing medical image segmentation workflows.OPEN ACCESS Received: 30/08/2024 Accepted: 22/10/2024 Published: 07/04/2025

Abstract

Abstract

Abstract