霍尼韦尔

Aviation decarbonization is one of the greatest challenges in the pursuit of sustainable mobility. While incremental improvements in aerodynamics, structures, and propulsion have led to sensible efficiency gains over the last years, the transition toward zero emission aircraft configurations requested by international guidelines needs disruptive technologies. Liquid hydrogen propulsion systems coupled with fuel cells are one of the most promising solutions to be investigated, since they offer high energy density, clean exhaust, and compatibility with regional aircraft missions. This study presents a novel configuration for a regional aircraft propelled by liquid hydrogen and fuel cells, based on conceptual design. The proposed configuration is a high-wing aircraft with T-tail. Unlike kerosene, liquid hydrogen requires specialized tanks and insulation, which significantly influence aircraft geometry and weight distribution. To address these challenges, the proposed configuration adopts a fuselage integrated cryogenic tank system installed in the aircraft rear cone, to minimize aerodynamic penalties while ensuring safety and operational feasibility. The fuel cell system is distributed to optimize redundancy and thermal management, enabling efficient power delivery to electric propulsors. The study contributes to the growing body of literature on hydrogen aviation by providing a system level configuration tailored to regional aircraft, a segment particularly suited for early adoption of hydrogen technologies due to shorter ranges and frequent operations. The findings underline the technical feasibility of liquid hydrogen–fuel cell systems, offering insights for future certification frameworks, infrastructure development, and industrial implementation. In conclusion, this work presents a feasible configuration of a novel green regional aircraft powered by liquid hydrogen and fuel cells. The results provide a foundation for further experimental validation and pave the way for the next generation of environmentally responsible regional aircraft.

Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based algorithm is developed for joint UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed algorithm within the integrated GEO terrestrial framework.

This study models the behaviour of a dynamic system when a signal is transmitted by it, considering the delay in propagation and the parameters of the system. For this purpose, a delay operator r written under the binomial form (a + b tau)(n) is used, which acts on the initial state of the system, n being the number the system constituents, and a + b = 1, a not equal b are some parameters of a certain medium. This mathematical expression generates asymmetric pulses, whit a suitable graphical representation, which allows the analysis of the propagation process and the calculation of the involved parameters.

Accurate risk stratification of precancerous polyps during routine colonoscopy screening is a key strategy to reduce the incidence of colorectal cancer (CRC). However, assessment of low-grade dysplasia remains limited by subjective histopathologic interpretation. Advances in computational pathology and deep learning offer new opportunities to identify subtle, fine morphologic patterns associated with malignant progression that may be imperceptible to the human eye. In this work, we propose XtraLight-MedMamba, an ultra-lightweight state-space–based deep learning framework to classify neoplastic tubular adenomas from whole-slide images (WSIs). The architecture is a blend of a ConvNeXt-based shallow feature extractor with parallel Vision Mamba blocks to efficiently model local texture cues within global contextual structure. An integration of the Spatial and Channel Attention Bridge (SCAB) module enhances multi-scale feature extraction, while the Fixed Non-Negative Orthogonal Classifier (FNOClassifier) enables substantial parameter reduction and improved generalization. The model was evaluated on a curated dataset acquired from patients with low-grade tubular adenomas, stratified into case and control cohorts based on subsequent CRC development. XtraLight-MedMamba achieved an accuracy of 97.18% and an F1-score of 0.9767 using approximately 32,000 parameters, outperforming conventional Convolutional Neural Networks (CNN)-based, Transformer-based and conventional Mamba architectures, which have significantly higher model complexity and computational burden, making it suitable for resource-constrained areas.