Abstract
Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia, which could alter metabolic, vascular and hematological parameters. A low-carbohydrate, high-fat diet (LCHF) diet benefits glycemic and blood pressure control. In turn, exercise in hypoxia (EH) is known to improve insulin sensitivity, erythropoiesis and angiogenesis. LCHF combined with EH seem to be a potential therapeutic strategy for T2DM and hypertension (HTN), but evidence is still scarce. The aim of this study was to evaluate the effects of eight weeks of EH combined with a LCHF on hematological and lipid profiles, inflammation and blood pressure in older patients with T2DM and coexistent HTN. Diabetic patients with HTN (n=42) were randomly assigned to a (1) control group: control diet (high-carbohydrate and low-fat diet) + exercise in normoxia; (2) EH group: control diet + EH; (3) intervention group: LCHF + EH. Baseline and eight-week measurements included systolic, diastolic, and mean blood pressure (SBP, DBP, MAP, respectively), and hematological and lipid profiles and inflammation biomarkers. Blood pressure decreased after interventions (p<0.001), with no differences among groups (SBP: p=0.151; DBP: p=0.124 and MAP: p=0.18). There were no differences in lipid profile and C-reactive protein (p>0.05). Mean corpuscular hemoglobin (MCH) increased in the EH group (p=0.027), while MCH concentration decreased in the EH+LCHF group (p=0.046). In conclusion, there is no additional benefit in adding hypoxia to exercise or restricting carbohydrates, on blood pressure in patients with T2DM and coexisting HTN. Further elucidation of the mechanisms underlying hematological adaptations is imperative. Trial registration number: NCT05094505.

Abstract
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions and always converging deterministically: Replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical tour of the evolution from sequential data types to CRDTs, we present in detail the two main approaches to CRDTs, operation-based and state-based, including two important variations, the pure operation-based and the delta-state based. Intended for prospective CRDT researchers and designers, this article provides solid coverage of the essential concepts, clarifying some misconceptions that frequently occur, but also presents some novel insights gained from considerable experience in designing both specific CRDTs and approaches to CRDTs.

Abstract
This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models' performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

Abstract

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Abstract
Capricious data streams, marked by random emergence and disappearance of features, are common in practical scenarios such as sensor networks. In existing research, they are mainly handled based on linear classifiers, feature correlation or ensemble of trees. There exist deficiencies such as limited learning capacity and high time cost. More importantly, the concept drift problem in them receives little attention. Therefore, drifting capricious data streams are focused on in this paper, and a new algorithm DCFHT (online learning from Drifting Capricious data streams with Flexible Hoeffding Tree) is proposed based on a single Hoeffding tree. DCFHT can achieve non-linear modeling and adaptation to drifts. First, DCFHT dynamically reuses and restructures the tree. The reusable information includes the tree structure and the information stored in each node. The restructuring process ensures that the Hoeffding tree dynamically aligns with the latest universal feature space. Second, DCFHT adapts to drifts in an informed way. When a drift is detected, DCFHT starts training a backup learner until it reaches the ability to replace the primary learner. Various experiments on 22 public and 15 synthetic datasets show that it is not only more accurate, but also maintains relatively low runtime on capricious data streams. © 2025 Elsevier Ltd