Abstract

Abstract
Inherited retinal diseases (IRDs) are genetic disorders affecting photoreceptors and the retinal pigment epithelium, leading to progressive vision loss. Retinitis pigmentosa (RP), the most common IRD, manifests as night blindness, peripheral vision loss, and eventually central vision decline. RP is genetically diverse and can be categorized into non-syndromic and syndromic. Advanced imaging technologies such as fundus autofluorescence (FAF) and spectral-domain optical coherence tomography (SD-OCT) facilitate diagnosing and managing these conditions. The integration of artificial intelligence in analyzing retinal images has shown promise in identifying genes associated with RP. This study used a dataset from Portuguese public hospitals, comprising 2798 FAF images labeled for syndromic and non-syndromic RP across 66 genes. Three pre-trained models, Inception-v3, ResNet-50, and VGG-19, were used to classify these images, obtaining an accuracy of over 80% in the training data and 54%, 56%, and 54% in the test data for all models. Data preprocessing included class balancing and boosting to address variability in gene representation. Model performance was evaluated using some main metrics. The findings demonstrate the effectiveness of deep learning in automatically classifying retinal images for different RP-associated genes, marking a significant advancement in the diagnostic capabilities of artificial intelligence and advanced imaging techniques in IRD.

Abstract
PurposeDisruptions are appearing more frequently and having an ever greater impact on supply chains (SC), affecting the vulnerability and sustainability of organisations. Our study proposes an innovative approach to address contemporary challenges by introducing coopetition as a strategic capability. The aim of this study is to enable companies to adapt and thrive by applying a tool that measures and monitors different logistical scenarios to improve performance and antifragility.Design/methodology/approachWith the aim of jointly planning transport activities of two competing companies, we present a linear programming model that promotes synergies which enhance resource utilisation. To demonstrate the validity of the model, a case study is conducted to measure, monitor and evaluate the results obtained after collaborating on SC activities.FindingsCurrent tools to support logistics planning are not effective because they hamper information exchange, cost allocation and performance measurements. Our innovative model optimises collaborative networks (CNs) and monitors economic, environmental and social improvements. The case study shows the reduction of logistics costs (13%), carbon footprint (37%) and the improvement of social antifragility when agility and flexibility emerge.Originality/valueCNs have become an effective means of enhancing resilience, but there are no empirical contributions to demonstrate how to achieve this. We provide a real case with computational experiments that provide empirical evidence of the effectiveness of the model, which measures, optimises and evaluates SC performance in coopetitive environments. This approach is a guide to researchers and practitioners when creating simulations to reduce risks and facilitate decision-making.

Abstract
The world faces unprecedented challenges related to the so-called Triple Planetary Crisis (climate changes, massive pollution, biodiversity losses). The Linear Economy model of development represents a very relevant cause for these crises effects, since it is anchored on the paradox of ever-growing natural resources extraction within a finite planet space and limited policy barriers for ecosystems degradation. Circular Economy emerges as a promising alternative development model, but it still urges for effective implementation. This work presents a novel De-Production model that combines, by design or redesign, the articulation of R-Strategies and D-Strategies across the product and production life cycles in order to unblock circular business models. It is proposed a systemic approach considering product circularity by means of activating R-Strategies, improving both production operations and de-production operations via value retention mindset. The model is tested via discrete simulation in a remanufacturing case study of a bicycle wheel assembly. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract
The growing use of machine learning for integrity assessments in public administration has intensified interest in understanding how algorithms can detect corruption risk-a topic of increasing relevance in the context of rising demands for transparency. Previous research on fraud detection often overlooks the dual challenge of extreme class imbalance and the need for model explainability. This study addresses both issues by combining data-level and algorithm-level techniques in a real-world dataset from Brazil's Federal District, where there is one corruption case for every 707 non-corruption cases (a ratio of 1:707). Data engineering was essential, encompassing gathering, cleaning, transformation, and dimensionality reduction to enhance model performance and interpretability. Among the tested models, weighted logistic regression stood out, achieving the best AUC (0.692). To increase transparency, we employed SHapley Additive exPlanations, enabling both global and local interpretability of predictions. The analysis identified strong predictors of corruption risk, such as business ownership, political candidacy, and frequent job function changes. This work provides a replicable pipeline that integrates imbalanced learning and explainable AI, offering valuable contributions to risk management and decision-making in the public sector.