Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners.

International networks, although highly efficient, may produce surprising threshold effects that shift costs to geographically distant locations. International utility, transportation, and information networks facilitate the efficient flow of information, energy, goods and people. These networks exhibit a scale-free network structure with a few large “hubs”. Yet their efficiency belies their lack of robustness. Because such networks transcend national boundaries, furthermore, disruptions to the network in one geographic region may have profound economic and national security costs for countries in another region. To illustrate how complex networks may transmit costs among countries, this paper builds an agent-based model (ABM) of the international air transportation system. The ABM employs a genetic algorithm to identify “small” disruptions that produce cascading network failures. The study makes two contributions. First, it demonstrates how some complex networks evolve into network structures that trade off robustness for efficiency. Second, it illustrates how researchers can combine agent-based modeling, evolutionary computation, and network analysis to simulate differing failure modes for global networks. This convergence of simulation methodologies characterizes the emerging field of computational social science.