Mature Technology Research Articles

Barriers to Industry Adoption of AI Video Generation Tools: A Study Based on the Perspectives of Video Production Professionals in China

In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.

Research on the Non Copyright and Copyright Infringement Regulations of Artificial Intelligence Painting

Artificial intelligence has surpassed its instrumental role in content generation, playing the role of creators, and this is also true in the field of painting. With the maturation of artificial intelligence painting technology, the generated paintings are not only difficult to be protected by copyright, but there are also significant infringement risks that cannot be ignored in the process of obtaining and utilizing the works of others. Specifically, artificial intelligence painting lacks the rationality of obtaining copyright protection due to its characteristics of low originality, fragmented expression, and semi creation. This article takes the non copyright ability of artificial intelligence painting as the research core, and based on the analysis of the current research situation, elaborates on the issue of copyright infringement in artificial intelligence painting.

EOR Technology (Patents) and Science (Articles) Assessment of BRICS and nonBRICS with Growth Rates and Specializations within Responsible Global Energy Transition: A Critical Review

To achieve a low-carbon energy transition, it is essential to ensure that, as long as fossil fuels are needed, their production is sustainable, minimizing the environmental impact and securing resources for advancing greener technologies, in alignment with SDGs 7, 13, and 14. Enhanced oil recovery (EOR) increases the recovery rates without new developments. The recent expansion of the BRICS consortium, involving major producers, underscores the need to evaluate their EOR technologies, particularly potential gaps that could hinder global energy transition strategies. We analyzed intermediate levels of technological readiness levels (TRLs) utilizing patents (TRL4-5) and articles (TRL3) for 18 EOR methods between 2002 and 2021. Composite indicators derived from patents including compound annual growth rate, specialization, concentration, diversification, and Gini inequality were employed. Both BRICS and nonBRICS exhibited analogous distributions in the articles (particularly Norway, United Kingdom, Canada) and patents (particularly Russia, China, and Ukraine). The decline in growth rates among BRICS and negative rates in nonBRICS suggest a technological plateau for traditional methods. However, environmentally low-impact EOR methods are experiencing exponential emergence (low salinity water, MEOR, polymers and macromolecular compounds, their associations with surfactants, and WAG). Both groups are self-sufficient in EOR, ensuring a responsible and low-impact energy transition. This ensures energy quality while facilitating the maturation of renewable technologies.

Machine translation in hindsight

The paper expands on the analysis of key projects adoring the machine translation (MT) hall of fame and their role in addressing practical tasks. The most successful initiatives suggest that the fledgling MT was contingent on the level of entropy, a.k.a. random nature of natural languages: the lower the indicator, the higher the predictability of the text, and by implication the efficiency of the system. It accounts for the success of the first Georgetown-IBM experiment and Canada’s METEO-1. The letter grew into a full-fledged system that for almost a quarter of the 20th century, provided English-French-English translations of weather bulletins, boasting high language predictability. Although, in between them the 1964 ALPAC report sowed a seed of doubt of the MT validity, it never aimed at killing the research area at all. On the contrary, it highlighted technics and applications, where the technology had demonstrated promising results, including raw MT, post-edited MT, and M-AT. The authors note a cyclic nature of the development of MT-powered methods and technologies. Today’s combination of resources and the way they are used are different very little from those employed in the past century. What makes them stand apart is the maturity of MT technologies, which made it through rulebased, direct, corpus-based, and knowledge-based translation to SMT and eventually to NMT. It has been established that the improved performance comes at a cost of more elaborate and larger data sets, tagged, marked up and annotated for automated use in language models. Taking advantage of these as well as artificial intelligence (AI), the authors venture into modeling basic text processing scenarios in a bilingual environment. This results in recommendations as to future paths for the improvement of MT technologies in the hands of professional translators by fine-tuning language models individually and pursuing post-editing (PEMT) and pre-editing practices paving the way for more complex transformations and lower equivalence levels.

Intelligent Text Mining for Ontological Knowledge Graph Refinement and Patent Portfolio Analysis—Case Study of Net-Zero Data Center Innovation Management

Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and complete patent retrievals and subsequent analyses. In recent decade, the rapid growth of the information and communication technology (ICT) sector has rendered data centers (DCs) indispensable for data processing, storage, and cloud computing, while ensuring security and privacy during DC operations. However, their energy-intensive operations pose challenges to global efforts toward achieving net-zero emissions goals. In response, this research develops a formal OKG refinement process and uses DC net-zero technology OKG as case study for in-depth OKG refinement and application in patent portfolio analysis. The net-zero DC domain covers five sub-technologies. Utilizing the proposed OKG refinement and patent portfolio analysis framework, the 1801 most recent decade’s patents related to relevant “DC net-zero technologies” are retrieved and analyzed. Particularly in this case, DC colocation and server-as-a-service perspectives are the newly discovered sub-domains for OKG refinement. Furthermore, the research also adopts the technology function matrix and technology maturity to assess current and future technology development trends, providing crucial insights supporting strategic innovation management.

Laboratory-scale plastic upcycling and green growth: Evaluating the upcycling of plastic waste into carbon nanotubes from economic and environmental aspects

It is known that gas generated through plastic pyrolysis is abundant in hydrocarbons and can serve as a raw material for carbon nanotubes (CNTs). However, there has been no research evaluating both the economic and environmental feasibility of an upcycling process using pyrolysis gas to produce CNTs. A comparative evaluation was conducted between the upcycling process and the conventional process using methane as a carbon source, and each process was performed at laboratory scale. We report that the cost of the upcycling process (2,999 $ kg−1) is comparable to that of the conventional process (2,930 $ kg−1), and that this slight cost disadvantage can be mitigated by reducing the Ar flow rate by 20%. Upcycling process involving pyrolysis has a greater impact on climate change than conventional process. However, this disadvantage can be significantly mitigated up to 19.38 kgCO2eq by substituting the power generation method with renewable energy source. Enhancing the prerequisites and technological maturity of upcycling processes can contribute to the expansion of the pyrolysis industry and the realization of a circular economy, thereby promoting green growth.

Techno-economic study of different process configurations of CO2 capture by MEA absorption and utilization of an MCDM method for their ranking

Carbon dioxide emissions from fossil fuel-fired power stations are attracting growing attention as a potential approach for reducing greenhouse gas emissions. Post-combustion capture of CO2 with monoethanolamine (MEA) is one of the most successful and mature technologies for mitigating carbon dioxide emissions. The most crucial problem of this technique is the high heat requirement of the reboiler for solvent regeneration. In this study, seven different configurations for carbon capture, namely, standard absorption, vapor recompression, vapor recompression process with split-flow, absorption column with intercooling, rich-split, combined, and rich-split with vapor recompression, are studied. The simulation results showed that the reboiler energy consumption intensity of alternative configurations lowered by 15.2 %, 18.4 %, 2.9 %, 7.4 %, 20.3 %, and 23.2 %, respectively, with regard to the standard absorption configuration. Seven configurations were ranked based on the total investment cost, steam consumption, power consumption, and water consumption. Combined and rich-split were the best, and vapor recompression and Rich-split with vapor recompression were the worst.

Analysis of visual communication elements in variable font design from the perspective of digital technology

With the maturity of digital technology, the effectiveness of information transmission has received extensive attention. Variable font design is regarded as an extension of graphic design in digital media art, and variable font design is studied from the perspective of information communication design. From the perspective of digital technology, based on the research theory of variable information communication design, combined with the specific methods of font design, the perception, cognitive methods, and aesthetic laws of aesthetic subject vision and psychology in the field of font variable design are discussed. Provide relevant theoretical support for information communication design activities with variable fonts. Based on artificial intelligence technology, a new interactive art expression method is proposed. By analyzing the main expression scenarios of interactive art, namely user information communication scenarios, interactive art push scenarios, interactive art promotion scenarios and personal service scenarios, it provides a scenario application basis for interactive expression, calculate the space complexity, space complexity and resource complexity, according to the complexity calculation results, carry out interaction design from three aspects of visual elements, multimedia elements, and human-computer interaction elements, integrate the topology structure after the interaction is realized, and in the network topology structure. The proposed model consists mainly of visual design, basic design elements, basic visual design, complete design and solution generation. It realizes the expression of interactive art of multimedia elements in display and control. The experimental results show that the interactive element extraction speed of the visual communication expression method based on artificial intelligence technology is increased by 30%, and the flexibility is stronger.

The digital transformation and future development trends of CCTV1 programs

As one of China's most authoritative and credible television media platforms, China Central Television (CCTV) has a long history and a vast audience base. However, facing the dual challenges of audience attrition and competition from emerging media, CCTV cannot afford to be complacent. Today, with the gradual maturation of digital technology, CCTV is actively promoting digital transformation to enhance media competitiveness and ensure it meets the ever-changing needs of its audience. This is not only a necessity for keeping pace with the times but also an important measure to gain the favor and recognition of the new era's audience. This article will showcase CCTV's significant achievements in cross-platform dissemination, program content diversification, enhancing program-audience interaction, and program personalization. The programs cover a wide range of themes and formats, catering to the needs and interests of different audience groups. Additionally, the article will analyze the development trends of CCTV programs in terms of diversification, interactivity, and personalization, as well as the future development directions of personalized content creation, audience interaction technology application, and the use of emerging technologies.

Synergizing MBR and MCDI systems as a sustainable solution for decentralized wastewater reclamation and reuse

Decentralized wastewater reclamation and reuse systems have drawn much attention due to their capability for reducing the energy demand for water conveyance and reclaiming wastewater for local re-use. While membrane bioreactor (MBR) stands as a mature technology offering comprehensive solid and liquid separation, membrane capacitive deionization (MCDI) presents a promising avenue for ion separation. Unfortunately, MCDI has seldom been incorporated into decentralized wastewater reclamation and reuse systems. This study aims to exemplify the design and the operation of the synergistic integration of MBR and MCDI system with a practical capacity of 1 m3 d− 1, showcasing its efficacy in reclaiming and reusing water at regional level. The integrated system demonstrated significant high removal of total organic carbon (from 97 to 2 mg L− 1) and chemical oxygen demand (COD, from 218 to < 3 mg L− 1). Meantime, nearly complete transformation (approximately 91%) of NH3 to NO3− within the MBR effluent was observed with a hydraulic retention time of 4.3–4.8 h and a food-to-microorganism of 0.15–0.20 kg COD kg− 1 MLSS d− 1 which can be further removed through the MCDI system (> 92% TN removal). A significant milestone of MCDI unit was reached with the remarkable removal efficiency of total ions (93%) and water recovery (80%) using a stop-flow regeneration approach coupled with an optimized voltage of 2.0 V. The MCDI unit not only proved its high stability but also featured low energy consumption (0.44 kWh m− 3). Overall, synergizing MBR and MCDI systems emerges as a sustainable and effective solution for decentralized wastewater reclamation and reuse, contributing to a more environmentally friendly and resource-efficient water management paradigm.

Impact damage characterization approach for CFRP pipes via self-sensing

The growing usage of carbon-fiber-reinforced polymers (CFRPs) has necessitated the development of structural health monitoring (SHM) for ensuring their integrity and safe operation. This paper presents a novel technique for monitoring the structural health of 3D CFRP pipe structures subjected to multiple impacts based on real-time electrical resistance monitoring. Damage characteristics such as its location and type were identified using the monitoring data and machine learning techniques. Additionally, finite element analysis was deployed to rationalize the electromechanical behaviors of pipes with varying stacking types and thus different fracture mechanisms. Analysis of the electrical resistance data coincided with the results of the finite element analysis in terms of damage modes and types. Therefore, a simple real-time electrical resistance measurement can detect the damage type without numerical analysis. A damaged section was identified by comparing the intensity of the data. Through machine learning techniques, the timepoints of damage severity demarcation were accurately predicted within error of 10 s and accuracies over 96 %. The machine-learning tools applied are unsupervised, thus reducing the effects of data dependency, labeling, and imbalance, as well as ensuring flexibility and general applications. The proposed technique was able to detect the damage location, severity, and type in real-time; hence, it has various potential applications in structural health monitoring including CFRP materials and is one of the few methods used to assess 3D structure characterization using self-sensing, which can result in mature technology and the optimal operation of CFRP systems.

3D ToF LiDAR for Mobile Robotics in Harsh Environments: A Review

Over the past decade, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this paper systematically reviews and analyzes the use of 3D ToF LiDARs for mobile robotics under harsh conditions such as adverse weather, GPS-denied, and highly dynamic environments for both research and industrial applications. The former include LiDAR data processing in adverse weather, object detection, and autonomous navigation. The latter encompasses autonomous driving, service robotics, and public health crises applications. We hope that our efforts can effectively provide readers with a reference based on our hands-on experiences and promote the deployment of existing mature technologies in real-world systems.

Spatiotemporal analysis and forecasting of PV systems, battery storage, and EV charging diffusion in California: A graph network approach

Understanding the dynamics of clean energy adoption is crucial for shaping effective energy policies and strategies. To understand the spatiotemporal variability and bottom-up spontaneity of the diffusion of clean energy technologies, this study examined the spatiotemporal diffusion of Photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and Electric Vehicle Charging Stations (EVCS) across 1773 postcode areas in California over the past 35 years. The Bayesian change point detection algorithm (BCDA) was initially utilized to identify temporal change points in the evolution of these technologies, revealing their diffusion trends over time. Then, the Louvain community detection algorithm (LCDA) was employed to elucidate spatial diffusion patterns across regions, offering insights into the geographic proliferation of clean energy solutions. Furthermore, to forecast future distributions, the spatiotemporal graph convolutional network (STGCN) model was applied, adeptly capturing the multi-stage, nonlinear characteristics of clean energy diffusion marked by significant spatiotemporal interactions. With historical data and spatial proximity, the STGCN model demonstrated fine forecasting precision (R^2 ≥ 0.78). This study validates the effectiveness of a graph network-based approach for analyzing the diffusion of clean energy technologies. It recommends policies tailored to account for technological maturity, geographic disparities, and diffusion stages, aiming for equitable and sustainable clean energy development in the region.

Low-grade heat to electricity conversion using the self-circulation of a magnet inside a single-turn pulsating heat pipe

Low-temperature (≤100°C) heat sources are abundantly and ubiquitously available in nature, such as solar energy, and in the form of untapped waste heat. This work aims to harness low-grade heat sources by introducing a portable harvesting system capable of effectively converting small spatial temperature gradients to mechanical and then electrical energy while maintaining a relatively high heat transfer capability. The designed and studied pair of harvesters are based on single-turn pulsating heat pipes with different condenser locations. Asymmetrical heating and cooling of the looped capillary glass tube gives rise to the bulk circulation of the internal fluid with occasional oscillation. The thermally-driven two-phase flow carries a spherical neodymium magnet fitted inside the tube. Two external solenoids generate a small amount of electricity every time the magnet passes through them. The proper functioning of energy-harvesting pulsating heat pipes (EH-PHPs) was investigated for pure water, acetone, and ethanol as the working fluids. Only water demonstrated satisfactory results and was thus selected as the main working fluid. Subsequently, high-frame-rate imaging of the internal flow along with time-domain temperature and open-circuit voltage data were employed and discussed to study the working principles of the harvester. In the next step, the mechanical, thermal, and electrical performance of the two EH-PHPs were quantitatively investigated for five different filling ratios (FRs) between 20% to 80% and five levels of heat input from 20.0W to 40.0W for each FR. Among the studied conditions, the EH-PHP with horizontal condenser (HC) configuration and 50% FR demonstrated the best performance in all three aspects. Accordingly, when 40.0W of heat was supplied to this ideal case, the magnet mean circulation frequency around the loop, effective thermal conductivity, and average induced open-circuit voltage (peak-to-peak) reached their maximum values of 0.46Hz, 11.5kW/(mK), and 0.53V, respectively. Although the integration of electromagnetic generators had reduced the heat transfer performance, the thermal conductivity of the EH-PHPs was comparable to that of conventional heat pipes and at least 25 times better than copper. Finally, the EH-PHP with HC configuration and 50% FR, the superior case, produced up to 5μW of electrical power while its solenoids were connected to load resistances with an optimum impedance. Therefore, even in its early stage of development, the portable EH-PHP can be used to supply low-power electronics such as a quartz watch. The generated electricity corresponds to about 0.01% relative Carnot efficiency. The remarkable thermal conductivity and capability of electricity production with less than 5°C temperature difference across the tube distinguish the EH-PHPs from mature energy harvesting technologies with higher energy conversion efficiencies.

Selection of optimal draw solution recovery technology for forward osmosis desalination system using analytical hierarchy process

Water scarcity poses a significant threat to global food and water security, prompting a need for practical solutions. With 97% of Earth’s water situated in oceans, desalination emerges as a viable option. Among desalination technologies, forward osmosis (FO) using membrane-based technology stands out for its potential to reduce costs and energy requirements. The focus on energy consumption in FO has prompted an exploration of optimal technology selection through the Analytical Hierarchy Process (AHP), a multi-criteria decision-making method. Value judgments were collected through a questionnaire in consultation with two experts. Environmental aspects emerged as the most critical factor, weighted at 0.3963. The AHP analysis revealed nanofiltration (NF) as the optimal system, attaining a total weight of 0.2612. The NF scored highest in terms of environmental impact (C3), operating and maintenance costs (S6), and energy requirements (S4). Conversely, membrane distillation ranked as the least preferred alternative, with a total score of 0.1335, mainly due to lower maturity of technology (S3), higher capital costs (S5), and negative environmental impact (C3). Sensitivity analysis was conducted to investigate how changing weights for sub-criteria might affect the preferred technology. Notably, Reverse Osmosis became the most favored technology when efficiency (S1) and S3 weights were set at 0.3 and 0.2, respectively. Conversely, thermal separation gained preference when the weights for resistance to scaling and fouling (S2) and S5 were set at 0.3. Changes in S4, S6, and C3 have showed the most minor sensitivity.

Influence of social capital, market orientation, and technological readiness on researchers’ interactions with companies

This study investigates the influence of social capital, market orientation, and technological readiness levels (TRLs) on the intensity of collaboration between researchers from a public research institution in Brazil and companies, and how TRLs moderate this relationship. Using a quantitative approach, we applied structural equation modeling (PLS-SEM) to analyze responses from researchers at this institution. The analysis highlights the critical roles of social capital and market orientation in fostering effective R&D interactions. Social capital enhances collaboration through trust and network strength, while market orientation aligns R&D efforts with market needs, ensuring that innovations are both relevant and timely. Importantly, this study explores how technological readiness levels (TRLs) moderate these relationships, offering insights into the varying impact of social capital and market orientation across different stages of technological development. Findings reveal that participation in projects with TRLs 4 to 6, known as the Valley of Death, significantly moderates the impact of market orientation on researchers' interaction intensity with companies, underscoring the importance of considering technological maturity in R&D collaborations. The study is framed within the open innovation approach, emphasizing the importance of leveraging external knowledge and collaborative networks to enhance innovation outcomes. Theoretically, this research extends the existing models of R&D collaboration by illustrating how TRLs modify the effects of social capital and market orientation. Practically, it offers actionable insights for R&D managers and policymakers on structuring environments that foster robust academic–industry partnerships, facilitating the successful transition of innovations from conceptualization to market readiness.

Viability assessment of large-scale Claude cycle hydrogen liquefaction: A study on technical and economic perspective

The competitiveness of hydrogen as a sustainable energy carrier depends greatly on its transportation and storage costs. Liquefying hydrogen offers advantages such as enhanced purity, versatility, and higher density, yet current industrial liquefaction processes face efficiency and cost challenges. Although various large-scale and efficient liquefaction concepts exist in the literature, they often overlook the economic and technical viability of such plants. Here, we addresses this issue by establishing a framework for modeling a large-scale hydrogen liquefaction concept and conducting both technical and economic assessments, with a specific focus on 125 tonnes per day (TPD) high-pressure hydrogen Claude-cycle concept. The technical analysis involves preliminary designs of key process components, while the economic assessment utilizes Aspen Process Economic Analyzer. Our findings indicate that at an electricity price of €0.1/kWh, the Claude-cycle liquefier concept yields a specific liquefaction cost (SLC) of €1.55/kgLH2. A sensitivity analysis was performed, which shows that electricity price has a significant influence on the economics. Further investigation on the compressors design shows that incorporating high-speed centrifugal compressors could reduce the SLC by 5.42% and potentially more. Scaling up to 250 and 500 TPD reveals further cost improvements, while cost projections indicate substantial declines as the technology matures. Ultimately, this paper presents novel cost-scaling and experience curves of hydrogen liquefaction technology, demonstrating the compelling economic viability of integrating large-scale hydrogen liquefaction into sustainable energy infrastructure.

III-Nitride Magnetron Sputter Epitaxy on Si: Controlling Morphology, Crystal Quality, and Polarity Using Al Seed Layers.

Group III-nitride semiconductors have been subject of intensive research, resulting in the maturing of the material system and adoption of III-nitrides in modern optoelectronics and power electronic devices. Defined film polarity is an important aspect of III-nitride epitaxy as the polarity affects the design of electronic devices. Magnetron sputtering is a novel approach for cost-effective epitaxy of III-nitrides nearing the technological maturity needed for device production; therefore, control of film polarity is an important technological milestone. In this study, we show the impact of Al seeding on the AlN/Si interface and resulting changes in crystal quality, film morphology, and polarity of GaN/AlN stacks grown by magnetron sputter epitaxy. X-ray diffraction measurements demonstrate the improvement of the crystal quality of the AlN and subsequently the GaN film by the Al seeding. Nanoscale structural and chemical investigations using scanning transmission electron microscopy reveal the inversion of the AlN film polarity. It is proposed that N-polar growth induced by Al seeding is related to the formation of a polycrystalline oxygen-rich AlN interlayer partially capped by an atomically thin Si-rich layer at the AlN/Si interface. Complementary aqueous KOH etch studies of GaN/AlN stacks demonstrate that purely metal-polar and N-polar layers can be grown on a macroscopic scale by controlling the amount of Al seeding.

DMPs as Management Tool for Intellectual Assets by SMART-metrics

Data Management Plans (DMPs) are vital components of effective research data management (RDM). They serve not only as organisational tools but also as a structured framework dictating the collection, processing, sharing/publishing, and management of data throughout the research data life cycle. This can include existing data curation standards, the establishment of data handling protocols, and the creation, when necessary, of community curation policies. Therefore, DMPs present a unique opportunity to harmonise project management efforts for optimising the formulation and execution of project objectives. To harness the full potential of DMPs as project management tools, the SMART approach (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound) emerges as a compelling methodology. During the initial stage of the project proposal, drafted SMART metrics can offer a systematic approach to map work packages (WPs) and deliverables to the overarching project objectives. Then, the Principal Investigators (PIs) can ensure the consortia that all the project potential intellectual assets (i.e., expected research results) were considered properly, as well as their necessary timelines, resources, and execution. It becomes imperative for data stewards (DSs) and governance policymakers to educate and provide guidelines to researchers on the advantages of developing well-curated DMPs that align results with SMART metrics. This alignment ensures that every intellectual asset intended as a research result (e.g., intellectual properties, publications, datasets, and software) within the project is subject to rigorous drafted planning, execution, and accountability. Consequently, the risk of unforeseen setbacks and/or deviations from the original objectives is minimised, increasing the traceability and transparency of the research data life cycle. In addition, the integration of Technology Readiness Levels (TRLs) into this proposed enhanced DMP provides a systematic method to evaluate the maturity and readiness of technologies across scientific disciplines. Regular TRL assessments will allow PIs: (1) to monitor the WP progress, (2) to adapt research strategies if required, and (3) to ensure the projects remain in line with the drafted SMART metrics in the enhanced DMP before the project started. The TRLs can also help PIs maintain their focus on project milestones and specific tasks aligned with the original objectives, contributing to the overall success of their endeavours, while improving the transparency for the reporting and divulgation of the research results. The paper presents the overall framework for enhancing DMPs as project management tools for any intellectual assets using SMART metrics and TRLs, as well as introducing suggested support services for data stewardship teams to assist PIs when implementing this novel framework effectively.

Concept Evaluation of Radical Short–Medium-Range Aircraft with Turbo-Electric Propulsion

Ambitious targets for the coming decades have been set for further reductions in aviation greenhouse gas emissions. Hybrid electric propulsion (HEP) concepts offer potential for the mitigation of these aviation emissions. To investigate this potential in an adequate level of detail, the European research project IMOTHEP (Investigation and Maturation of Technologies for Hybrid Electric Propulsion) explores key technologies for HEP in close relation with developments of aircraft missions and configuration. This paper presents conceptual-level design investigations on radical HEP aircraft configurations for short–medium-range (SMR) missions. In particular, a blended-wing-body (BWB) configuration with a turbo-electric powertrain and distributed electric propulsion is investigated using NLR’s aircraft evaluation tool MASS. For the aircraft and powertrain design, representative top-level aircraft requirements have been defined in IMOTHEP, and the reference aircraft for the assessment of potential benefits is based on the Airbus A320neo aircraft. The models and data developed in IMOTHEP and presented in this paper show that the turbo-electric BWB configuration has potential for reduced fuel consumption in comparison to the reference aircraft. But in comparison to advanced turbofan-powered BWB configurations, which have the same benefits of the BWB airframe and advanced technology assumptions, this potential is limited.