Abstract Over the past few years, hydrogen (H2) has garnered exponentially increasing interest as a potential primary energy carrier of the future. While significant progress has been made in analyzing and optimizing hydrogen production processes, relatively little attention has been directed toward hydrogen transport. This aspect is critical, as it is widely regarded as a key bottleneck limiting the scalability and economic viability of hydrogen adoption. For pure hydrogen (H2), transportation in liquid form is referred to as liquid hydrogen (LH2). Alternatively, H2 can be converted into ammonia (NH3) or stored in other liquid organic hydrogen carriers (LOHCs) for transport. However, hydrogen transport presents several technical challenges that current technologies struggle to address effectively. Key issues include hydrogen's low density, high permeability, volatility, safety concerns, and the associated high production and handling costs. Furthermore, the transportation phase, regardless of the chosen form, introduces additional complexities that significantly impact the overall economic analysis and commercial viability of hydrogen adoption, ultimately influencing its scalability as a sustainable energy carrier. Focusing on the two primary clean hydrogen (H2) production processes: green hydrogen (via electrolysis powered by renewable energy) and blue hydrogen (produced from natural gas with carbon capture), this analysis highlights significant cost challenges associated with transporting liquid hydrogen (LH2) using current technologies. Specifically, centralized hydrogen production and subsequent transport result in costs that are approximately 4-5 times higher than those of natural gas, rendering it economically non-viable under present conditions. However, decentralized hydrogen production, where H2 is generated near the point of consumption (e.g., at cement or steel factories), offers a more promising pathway. By reducing the transport requirements, decentralized production positions hydrogen as a viable solution for decarbonizing heavy industries, which account for 22% of global greenhouse gas emissions. For hydrogen transport to be cost-effective, the target cost for the transmission and distribution of LH2, ammonia (NH3), or liquid organic hydrogen carriers (LOHCs) is set at approximately USD 2-3/kg. Achieving this target will require substantial investments, particularly in decentralized production infrastructure. While transforming H2 into NH3 or LOHC adds energy intensity and cost, these forms are currently the most feasible options for long-distance transport, paving the way for a global hydrogen economy to emerge by approximately 2030 and beyond. This manuscript outlines the current status of technologies for transporting LH2, NH3, and LOHCs, along with a simplified cost analysis and projections through 2030. It also examines key challenges associated with centralized and decentralized hydrogen production, highlighting their implications for transport. Brief discussions are supported by relevant economic analyses, providing insights into the viability and scalability of these technologies for building a sustainable hydrogen economy.
Generating realistic synthetic gene expression data that captures the complex interdependencies and biological context of cellular systems remains a significant challenge. Existing methods often struggle to reproduce intricate co-expression patterns and incorporate prior biological knowledge effectively. To address these limitations, we propose BioGen-KI, a novel bio-inspired generative network with knowledge integration. Our framework leverages a hybrid deep learning architecture that integrates embeddings learned from biological knowledge graphs (e.g., gene regulatory networks, pathway databases) with a conditional generative adversarial network (cGAN). The knowledge graph embeddings guide the generator to produce synthetic expression profiles that respect known biological relationships, while conditioning on contextual information (e.g., cell type, experimental condition) allows for targeted data synthesis. Furthermore, we introduce a biologically informed discriminator that evaluates not only the statistical realism but also the biological plausibility of the generated data, encouraging the preservation of pathway coherence and relevant gene interactions. We demonstrate the efficacy of BioGen-KI by generating synthetic gene expression datasets that exhibit improved statistical similarity to real data and, critically, better preservation of biologically meaningful relationships compared to baseline GAN models and methods relying solely on statistical characteristics. Evaluation on downstream tasks, such as clustering and differential gene expression analysis, highlights the utility of BioGen-KI-generated data for enhancing the robustness and interpretability of biological data analysis. This work presents a significant step towards generating more biologically faithful synthetic gene expression data for research and development.
Unmanned aerial vehicles (UAVs) are vital for surveillance missions requiring the geolocation of moving ground targets, yet small, resource-constrained platforms often lack integrated, robust systems that can handle disturbances such as wind, occlusions, and noise. This paper presents an integrated, end-to-end vision-based geolocation pipeline specifically designed for embedded deployment on resource-constrained UAVs with gimbal cameras. Starting from a rough initial position estimate, pan/tilt angles are computed to orient the gimbal, and then a visual tracking module combining object detection (via Tiny-YOLO) and feedback control (using CSRT) centers the target in the frame. The target’s absolute position is derived from UAV inertial data and gimbal angles. To mitigate noisy or unavailable direct geolocation due to disturbances or visual lock loss, Kalman filtering is integrated with a unicycle-based motion model. Both an extended Kalman filter (EKF) and unscented Kalman filter (UKF) are evaluated and tuned in high-fidelity simulations, with the UKF demonstrating superior performance by reducing the 2D position RMSE by 33% compared to the EKF in occlusion scenarios. The system is implemented on embedded hardware and validated through real flight tests, establishing the operational capability of vision-based surveillance on small UAV platforms.
