The escalating global demand for goods transport via shipping has heightened energy consumption, impacting worldwide health and the environment. To mitigate this, international organizations aim to achieve complete fuel desulphurization and decarbonization by 50% by 2050. Investigating eco-friendly fuels is crucial, particularly those with a reduced carbon and zero sulfur content. Methanol derived mainly from renewable sources and produced by carbon dioxide’s hydrogenation method, stands out as an effective solution for GHG reduction. Leveraging its favorable properties, global scalability, and compatibility with the existing infrastructure, especially LNGs, methanol proves to be a cost-efficient and minimally disruptive alternative. This review explores methanol’s role as a hybrid maritime fuel, emphasizing its ecological production methods, advantages, and challenges in the shipping industry’s green transition. It discusses the environmental impacts of methanol use and analyzes economic factors, positioning methanol not only as an eco-friendly option, but also as a financially prudent choice for global shipping. Methanol is efficient and cost-effective and excels over MGO, especially in new ships. It is economically advantageous, with decreasing investment costs compared to LNG, while providing flexibility without specialized pressure tanks. Global marine fuel trends prioritize fuel traits, accessibility, and environmental considerations, incorporating factors like policies, emissions, bunkering, and engine adaptability during transitions.

The compatibility of shipping with environmental protection is a subject that the international community is becoming increasingly concerned about, considering the threat of climate change. The current study aims to assess the worldwide shipping sector’s eco-efficiency performance for the first time using the Dynamic Slack-Based assess non-oriented Data Envelopment Analysis methodology, while the visualization of the results has been made with the geographic information system of ArcGIS. The findings show that the most vibrant shipping sectors in the world, such as the Marshall Islands, present the lowest eco-efficiency levels due to the nations’ roles as tax havens for shipowners. Furthermore, traditional maritime economies such as the Chinese one show a great growth in the eco-efficiency score due to the strategies of the shipping companies that are headquartered in the region to invest and adopt Environmental, Social, and Governance principles, which help them to achieve high scores in eco-efficiency. Finally, nations with small fleets have the greatest eco-efficiency score, as local governments have engaged in sustainable activities and initiatives over the previous four to five years to enable their marine industry to thrive and dominate the market.

The conventional utilization of fossil fuels precipitates uncontrolled carbon dioxide and sulfur oxides emissions, thereby engendering pronounced atmospheric pollution and global health ramifications. Within the maritime domain, concerted global initiatives aspire to mitigate emissions by 2050, centering on the adaptation of engines, alteration of fuel compositions, and amelioration of exhaust gas treatment protocols. This investigation pioneers experimentation with marine gas oil augmented by methanol, a practice conventionally encumbered by prohibitively expensive additives. Successful amalgamation of methanol, animal-derived biodiesel, and marine gas oil (MGO) is empirically demonstrated under meticulously controlled thermal conditions, creating a homogeneous blend with virtually zero sulfur content and reduced carbon content, featuring characteristics akin to conventional marine gas oil but with no use of expensive emulsifiers. This new blend is suitable for employment in maritime engines utilizing Delaval technology, yet with significantly lower energy requirements compared to those necessitated using conventional very low sulfur fuel oil (VLSFO) with a maximum sulfur content of 0.5% w/w.

This study systematically reviews the application of machine learning (ML) in emergency resource allocation across Europe, with the aim of synthesizing current evidence and identifying future research directions. A systematic literature review (SLR) was conducted following PRISMA guidelines. Data were collected from major academic databases (2018–2025) using predefined inclusion and exclusion criteria. A total of 52 relevant studies were analyzed through qualitative thematic synthesis. The review finds that ML significantly enhances predictive analytics, enabling accurate forecasting of emergency demand and proactive resource allocation. ML-driven optimization improves ambulance dispatch, hospital resource management, and logistics efficiency, while real-time decision support systems strengthen situational awareness and coordination. However, challenges persist, including data quality issues, system fragmentation, ethical concerns (bias, transparency), and limited interoperability across European systems. ML has transformative potential in shifting emergency resource allocation from reactive to data-driven, predictive systems. Its effectiveness, however, depends on robust data infrastructure, ethical governance, and system integration. The study recommends strengthening data systems, adopting hybrid ML-optimization models, enhancing ethical frameworks, investing in human capacity, and promoting cross-border collaboration.

Traditional biomechanical analysis is constrained by high capital costs and the physical limitations imposed by markers, posing significant barriers to clinical adoption. This review evaluates the emergence of artificial intelligence (AI)-based markerless motion capture (MMC) as a transformative approach for democratizing movement science in clinical rehabilitation. The discussion outlines the progression from legacy geometric visual hulls to advanced deep learning architectures, with particular focus on YOLO-based two-dimensional detection and spatio-temporal transformer models for three-dimensional pose estimation. Evidence indicates that multi-camera MMC frameworks achieve research-grade positional accuracy (16–34 mm Mean Per-Joint Position Error—MPJPE), while monocular systems provide sufficient sensitivity (82–88%) for longitudinal monitoring of geriatric fall risk and stroke recovery. While challenges persist in achieving precise axial rotation measurement, integrating real-time signal refinement enables objective and ecologically valid assessments in community-based healthcare settings. This technological advancement redefines movement analysis, shifting it from a laboratory-bound procedure to a widely accessible and interoperable diagnostic tool.

The rapid evolution of deep learning and computer vision has moved markerless mo-tion capture (MoCap) from specialized biomechanical tools into a key technology for real-time interactive systems, video game engines, and virtual production. This review analyzes state-of-the-art markerless pipelines and focuses on integrating 3D human pose estimation (HPE) into standard graphics engines. We evaluate the shift from sin-gle-view regression to temporal convolutional architectures. Our review emphasizes the trade-offs between spatial precision and algorithmic overhead for interactive use cases. Central to our analysis is the spatio-temporal refinement of noisy skeletal data. We assess adaptive smoothing techniques, such as the 1-Euro filter and predictive neural filtering, designed to minimize jitter and motion-to-photon latency. Findings indicate that modern markerless systems approach the standards of marker-based tools. However, maintaining temporal consistency under strict real-time budgets re-mains a fundamental challenge for human-display feedback loops. We examine indus-try-standard engines and their role in permitting seamless character retargeting throughout diverse applications, including clinical monitoring and sports coaching. Ultimately, this work synthesizes methodologies to optimize the responsiveness of AI-based MoCap systems within contemporary real-time software architectures.