Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers have advanced HAR but remain limited by vanishing or exloding gradients, high computational cost, and difficulty in capturing long-range dependencies. Structured state-space models (SSMs) like Mamba address these challenges with linear complexity and effective temporal modeling, yet they are restricted to first-order dynamics without stable longterm memory mechanisms. We introduce Momentum Mamba, a momentum-augmented SSM that incorporates second-order dynamics to improve stability of information flow across time steps, robustness, and long-sequence modeling. Two extensions further expand its capacity: Complex Momentum Mamba for frequency-selective memory scaling. Experiments on multiple HAR benchmarks demonstrate consistent gains over vanilla Mamba and Transformer baselines in accuracy, robustness, and convergence speed. With only moderate increases in training cost, momentum-augmented SSMs offer a favorable accuracy-efficiency balance, establishing them as a scalable paradigm for HAR and a promising principal framework for broader sequence modeling applications.

Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are powerful architectures, they have drawbacks, including quadratic computational complexity and an inability to address long-range dependencies efficiently. This limitation affects the analysis of large and complex datasets in medical imaging, where there are many spatial and temporal relationships. In contrast, Mamba offers benefits that make it well-suited for medical image analysis. It has linear time complexity, which is a significant improvement over transformers. Mamba processes longer sequences without attention mechanisms, enabling faster inference and requiring less memory. Mamba also demonstrates strong performance in merging multimodal data, improving diagnosis accuracy and patient outcomes. The organization of this paper allows readers to appreciate the capabilities of Mamba in medical imaging step by step. We begin by defining core concepts of SSMs and models, including S4, S5, and S6, followed by an exploration of Mamba architectures such as pure Mamba, U-Net variants, and hybrid models with convolutional neural networks, transformers, and Graph Neural Networks. We also cover Mamba optimizations, techniques and adaptations, scanning, datasets, applications, experimental results, and conclude with its challenges and future directions in medical imaging. This review aims to demonstrate the transformative potential of Mamba in overcoming existing barriers within medical imaging while paving the way for innovative advancements in the field. A comprehensive list of Mamba architectures applied in the medical field, reviewed in this work, is available at Github.

Africa has had massive oil and gas discoveries in recent years – including the Greater Tortue Ahmeyim offshore Senegal and Mauritania, the Luiperd and Brulpadda in South Africa and the Rovuma Basin discoveries offshore Mozambique, among others – but development has been slow owing largely to restricted investment, Covid-19 impacts and a lack of modern digital solutions. With more than 600-million people living without access to electricity in Africa, the accelerated development of Africa's oil and gas is key for making energy poverty history. Now, with the emergence of AI and analytics across the oil and gas sector, an opportunity has risen for Africa to drive modern and sustainable energy growth for years to come. With oil and gas production decreasing in Africa due to natural declines in legacy projects, increasing the use of AI and analytics across the upstream segment could help simplify drilling activities, revitalise the sector and expand the continent's hydrocarbons reserves for energy reliability, saving project developers, operators and owners time and resources. Furthermore, with African hydrocarbon-producing countries such as Nigeria losing billions in revenue due to theft and vandalism of infrastructure – a condition that is restraining Africa's oil and gas sector from expanding – AI and analytics tools can help optimisa industry growth by enhancing infrastructure maintenance and security across the entire oil and gas value chain, thereby helping reduce energy and revenue loss, and in the process stimulating investments across the oil and gas sector. What's more, despite Africa accounting for less than 3% of all carbon emissions, global energy transition related policies are hindering the deployment of investments necessary for boosting the continent's hydrocarbons sector.