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Review studies play a key role in the development of clinical practice by synthesizing data and drawing conclusions from multiple scientific sources. In recent decades, there has been a significant increase in the number of review studies conducted and published by researchers. In clinical research, different types of review studies (systematic, scoping, umbrella, and narrative reviews) are conducted with different objectives and methodologies. Despite the abundance of guidelines for conducting review studies, researchers often face challenges in selecting the most appropriate review method, mainly due to their overlapping characteristics, including the complexity of matching review types to specific research questions. The aim of this article is to compare the main features of systematic, scoping, umbrella, and narrative reviews in clinical research and to address key considerations for selecting the most appropriate review approach. It also discusses future opportunities for updating their strategies based on emerging trends in clinical research. Understanding the differences between review approaches will help researchers, practitioners, journalists, and policymakers to effectively navigate the complex and evolving field of scientific research, leading to informed decisions that ultimately enhance the overall quality of healthcare practices. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of privacy, utility, and efficiency in releasing privacy-preserving operational data for AC Optimal Power Flow (AC-OPF) research. Traditional methods, injecting noise into operational data (i.e. , demand data and dispatch profiles) within the Differential Privacy (DP) framework, often violate physical constraints within the data, leading to unrealistic and infeasible outcomes that diminish data utility. While AC-OPF-solver-based bi-level post-processing optimizations can enforce physical feasibility, the objective divergence between post-processing and AC-OPF leads to discrepancies, compromising data utility. Additionally, their non-convex and adversarial nature makes computation prohibitively expensive, further preventing efficient data release. To overcome these challenges, our research introduces a DP approach that combines strategic noise injection for demand data with the computation of corresponding dispatch profiles, ensuring the privacy-preserving data satisfy AC-OPF's physical constraints. To accelerate data release, we employ Physics-Informed Neural Networks (PINNs). This ensures solutions' physical feasibility while enhancing computational efficiency. Furthermore, we incorporate active learning to target the most informative data samples, enhancing PINN training and optimizing efficiency while maintaining solution accuracy. Comprehensive experiments on IEEE test systems reveal our approach's improved performance and accelerated computation speed over traditional methods, highlighting its efficiency in maintaining data privacy and utility and decreasing computational burden amidst diverse privacy considerations. • Realistic, feasible, and fast data synthesis via AC-OPF-tailored PINNs. • Quantifying trade-off between demand privacy and dispatch profile accuracy. • Sampling efficient PINN training set via active learning. [ABSTRACT FROM AUTHOR]

Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships in graphs, leading to suboptimal predictions. To address this, we introduce a novel method called graphon mixup (GM), which leverages the power of graphons to enhance link prediction. The augmentation strategy involves generating a synthetic graph by combining the original graph with a graphon-based synthetic graph. This process, expressed as a weighted combination of adjacency matrices, strategically blends real and synthetic information, enriching the training dataset. GM formulates link prediction as a joint optimization problem, aligning the characteristics of the synthetic graph with the true underlying structure. The objective is to minimize cross-entropy loss between predicted and true edge probabilities. A detailed computational complexity analysis evaluates the time and space requirements, aiding in understanding the efficiency and scalability of GM across different datasets and network sizes. Empirical validation on benchmark datasets demonstrates GM’s effectiveness in consistently improving average precision across diverse network types. The proposed method enhances the generalization capabilities of link prediction models, providing a more robust framework capable of accurate predictions even in the presence of noise or unseen patterns. [ABSTRACT FROM AUTHOR]