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Peer-Reviewed Publication
Updates every hour. Last Updated: 20-Dec-2025 07:11 ET (20-Dec-2025 12:11 GMT/UTC)
The Economic and Social Committee of the Valencian Community has awarded one of its 2025 Doctoral Thesis Awards to Iluminada Vallet Bellmunt, a lecturer in the Department of Business Administration and Marketing at the Universitat Jaume I in Castelló. The thesis, entitled "The resilience of independent retail: a model of antecedents and consequences", was supervised by Marisa Flor and Víctor del Corte and tutored by Teresa Vallet-Bellmunt.
The thesis, defended in December 2024 in the Interuniversity Doctoral Programme in Marketing, explores the factors that foster organisational resilience in small independent retailers and analyses how this capacity enhances innovation and business performance. The results highlight that the individual resilience of the owner and the entrepreneurial orientation of the business are two of the factors that drive organisational resilience.
In addition to the resilience and adaptability of the people who lead the businesses, organisational resilience is also influenced by the characteristics of the work team, the organisational structure, external networks and environmental conditions. The methodology was based on a closed-ended questionnaire, with 150 validated responses. The distribution of the businesses surveyed was 26.74% from Alicante, 11.42% from Castelló de la Plana and 61.84% from Valencia, representing different sectors such as food, drugstores and perfumeries, flowers, jewellery and watches, home, fashion, leisure, stationery and health.
Antibiotic resistance genes (ARGs) are emerging contaminants that are widely distributed in agricultural soils and pose potential threats to human health through the soil‒plant system.
A groundbreaking study published in Soil Ecology Letters unveils a novel deep learning method to rapidly and accurately identify soil-dwelling Collembola (springtails), tiny arthropods critical for soil health and ecosystem functioning. Developed by an international team led by researchers from Sun Yat-sen University and the Chinese Academy of Sciences, this AI-powered tool achieves over 97% accuracy in detecting these organisms, offering a transformative solution for biodiversity monitoring and environmental assessment.