Mature Technology Research Articles

Exploration of the Impact of Dynamic Narrative Approach on Public Exhibition Experience

In today’s exhibition field, the dynamism of the narrative approach as a mainstream trend has permeated various aspects such as visual design, exhibit display, and media communication. On one hand, this is due to the development of digital media technology providing practical operational pathways; on the other hand, it is also a result of the information explosion brought about by the maturity of digital media technology, forcing exhibitions, as an important form of public cultural service, to stimulate public interest to the fullest extent in competition with other cultural products. In such a coincidental yet inevitable combination, how to effectively utilize a dynamic narrative approach within the exhibition field to provide the public with a sense of unfamiliarity in dimensions, boundaries, and situations, thereby constructing a unique perspective that enables people to perceive alienation relationships and ultimately resist the control of the technological landscape on humanity, becomes the key issue determining whether the integration between the two can be beneficial.

Mirage: cyber deception against autonomous cyber attacks in emulation and simulation

AbstractAs the capabilities of cyber adversaries continue to evolve, now in parallel to the explosion of maturing and publicly-available artificial intelligence (AI) technologies, cyber defenders may reasonably wonder when cyber adversaries will begin to also field these AI technologies. In this regard, some promising (read: scary) areas of AI for cyber attack capabilities are search, automated planning, and reinforcement learning. As such, one possible defensive mechanism against future AI-enabled adversaries is that of cyber deception. To that end, in this work, we present and evaluate Mirage, an experimentation system demonstrated in both emulation and simulation forms that allows for the implementation and testing of novel cyber deceptions designed to counter cyber adversaries that use AI search and planning capabilities.

Nth-plant scenario for blended pellets of Miscanthus, Switchgrass, and Corn Stover using multi-modal transportation: Biorefineries and depots in the contiguous U.S.

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. This study addresses the logistic challenge of mobilizing national cellulosic feedstock supplies for a sustainable bioenergy industry. A Mixed Integer Linear Programming (MILP) model was developed and updated to deliver on-spec biomass that considers both a desired quantity and quality at the biorefinery. Our supply chain analysis includes multi-modal transport (truck and rail), varying depot and biorefinery sizes, and feedstock blends of corn stover (harvested by either a two- or three-pass method), switchgrass, and miscanthus. The following US states: Illinois, Kansas, Missouri, North Carolina, Oklahoma, Georgia, and Texas were identified as key locations for producing accessible miscanthus. Based on our most optimistic scenario, using trucks as the only transportation mode in 2040 with a cost target of $79/dt, corn stover, switchgrass, and miscanthus could help meet 48% of the EPA target, 173 million dry tons that translate into 7.8 billion GGE. The addition of rail transportation for biomass delivery to biorefineries could help meet 79% of the EPA target, 283 million dry tons that translate into 12.7 billion GGE.

Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

Longitudinal analysis of interorganizational collaborative networks of cross-laminated timber (CLT) construction

Purpose Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction. Design/methodology/approach The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies. Findings The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors). Originality/value The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.

Process Engineering Analysis of Transport Options for Green Hydrogen and Green Hydrogen Derivatives

The transition of the German economy and energy system toward CO2 neutrality requires large quantities of climate‐friendly chemical energy carriers, such as green hydrogen, to be imported. The decision on the most favorable import routes must consider its energy efficiency To respect the remaining CO2 budget to achieve the 1.5° climate target, the implementation time and therefore additional evaluation criteria must be taken into account. In this article, as a first step, the energy utilization rate () for different hydrogen import options is evaluated. Starting off from hydrogen at 25 °C and 25 bar produced in the exporting country, liquid hydrogen, ammonia, liquid methane (Green LNG), methanol, and liquid organic hydrogen carrier are considered as chemical energy carries. Liquid hydrogen is in future the most efficient import option ( = 7%) when gaseous hydrogen is supplied in the importing country. When direct utilization of the H2 derivatives is considered, the ammonia import route has the highest energy utilization rate with 77%. If point sources for CO2 are considered, direct utilization of methanol ( = 83%) and Green LNG ( = 76%) are energetically favorable import options. Additional significant criteria for a technological assessment are maturity of technologies, available capacities, infrastructures, and production processes.

Molten sodium batteries: advances in chemistries, electrolytes, and interfaces

The need for clean, renewable energy has driven the expansion of renewable energy generators, such as wind and solar. However, to achieve a robust and responsive electrical grid based on such inherently intermittent renewable energy sources, grid-scale energy storage is essential. The unmet need for this critical component has motivated extensive grid-scale battery research, especially exploring chemistries “beyond Li-ion”. Among others, molten sodium (Na) batteries, which date back to the 1960s with Na-S, have seen a strong revival, owing mostly to raw material abundance and the excellent electrochemical properties of Na metal. Recently, many groups have demonstrated important advances in battery chemistries, electrolytes, and interfaces to lower material and operating costs, enhance cyclability, and understand key mechanisms that drive failure in molten Na batteries. For widespread implementation of molten Na batteries, though, further optimization, cost reduction, and mechanistic insight is necessary. In this light, this work provides a brief history of mature molten Na technologies, a comprehensive review of recent progress, and explores possibilities for future advancements.

Data accuracy in Vehicle-to-X cooperative awareness messages: An experimental study for the first commercial deployment of C-ITS in Europe

Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

With the maturity of hydrogen storage technologies, hydrogen-electricity coupling energy storage in green electricity and green hydrogen modes is an ideal energy system. The construction of hydrogen-electricity coupling energy storage systems (HECESSs) is one of the important technological pathways for energy supply and deep decarbonization. In a HECESS, hydrogen storage can maintain the energy balance between supply and demand and increase the utilization efficiency of energy. However, its scenario models in power system establishment and the corresponding solution methods still need to be studied in depth. For accelerating the construction of HECESSs, firstly, this paper describes the current applications of hydrogen storage technologies from three aspects: hydrogen production, hydrogen power generation, and hydrogen storage. Secondly, based on the complementary synergistic mechanism of hydrogen energy and electric energy, the structure of the HECESS and its operation mode are described. To study the engineering applications of HECESSs more deeply, the recent progress of HECESS application at the source, grid, and load sides is reviewed. For the application of the models of hydrogen storage at the source/grid/load side, the selection of the solution method will affect the optimal solution of the model and solution efficiency. As solving complex multi-energy coupling models using traditional optimization methods is difficult, the paper therefore explored the advantages of deep reinforcement learning (DRL) algorithms and their applications in HECESSs. Finally, the technical application in the construction of new power systems supported by HECESSs is prospected. The study aims to provide a reference for the research on hydrogen storage in power systems.

Machine Learning-Based Intrusion Detection System for Encrypted Attacks

In today's society, information and communication technology is developing rapidly. With the gradual maturity and popularization of encryption technology, more and more malicious attacks are also using encryption technology to evade the scrutiny of traditional traffic detection systems. Therefore, accurate identification of encrypted attacks has become a research hotspot in the international community. This paper proposes an encrypted traffic detection method based on convolutional neural network (CNN) technology to address the issues of tedious steps and low recognition accuracy in manually extracting traffic features. This method does not require manual or expert feature extraction, and can automatically extract advanced features through CNNs, which are then fed into XGBoost classifiers for classification processing. On the basis of the above methods, this article designs and implements an encrypted traffic intrusion detection system, which is divided into five parts: traffic collection, data processing, model detection, data visualization, and traffic blocking. Reasonable explanations and technical introductions are provided for these modules.

Photopolymerized 3D Printing Materials for Optical Elements

AbstractThe optical element is a key part of manufacturing optoelectronic systems, which can realize functions such as imaging, spectroscopy, image transmission, filtering, and more. The design and manufacture of optical elements is a key issue that restricts its development, and the traditional optical component preparation method is less capable of preparing optical elements with complex structures and multiple materials. As a mature advanced manufacturing technology, photopolymerization 3D printing technology has the advantages of high printing precision and small layer thickness, which plays an important role in the preparation of optical elements. This review paper briefly provides a concise overview of the benefits and applications of photopolymerization 3D printing technology in optical element preparation The development overview and research progress of materials used for the preparation of optical elements by photopolymerization 3D printing are discussed. Finally, it summarizes the current limitations, challenges, future development prospects, and potential applications in the realm of optical element manufacturing.

A multiplicative log mean Divisia index decomposition analysis of energy‐related carbon emissions in Pakistan: From the perspective of economic development phases

AbstractTo successfully implement mitigation policies, it is first needed to identify the factors that contribute to carbon emissions. Based on the extended Kaya identity approach, this study investigates the driving factors of energy‐related carbon emissions in Pakistan from 1990 to 2019 using the multiplicative LMDI‐I decomposition technique with no residual term. The decomposition results reveal that the population and affluence contributed significantly to accelerating carbon emissions from energy consumption across all the sub‐periods. The cumulative effect of population size increased to 2.286 times in 2019, far greater than the impact of other factors. The cumulative effect of economic activity per person reached 1.566 in 2019. The impact of fuel quality and energy intensity is relatively minor (as the index value is less than one) most of the time. The cumulative effect of renewable energy penetration has increased from 0.998 in 1990 to 1.034 in 2019 due to the country's less mature renewable energy technologies. Fossil fuel switching is a factor that reduces carbon emissions, as the index value decreased from 0.945 in 1990 to 0.918 in 2019. The results of the study contain helpful information for lowering carbon emissions.

Prediction of environmental benefits introducing hybrid-electric propulsion on regional aircraft

The objective of this paper is to assess the environmental benefits arising from the introduction of hybrid-electric propulsion on regional and commuter turboprop aircraft. A great focus is put on the propulsion based on a turbine engine coupled with batteries, combining mature technologies. The introduction of novel propulsion architectures on aircraft deployed on regional and commuter networks allows a substantial reduction of the fuel needed by airlines. In this work, scenarios based on real airline networks are presented, in order to quantify the reduction of greenhouse gas emissions possible considering the expected technological advancement for 2035 and 2050. This analysis is conducted by retrofitting the hybrid-electric propulsive system onto existing aircraft that can carry 19 passengers (Dornier DO228) or 70 passengers (ATR72-600). For the 19-seat class, a clean sheet design is also considered to overcome some limitations of the reference aircraft. A precise assessment of the peculiarities linked to the chosen propulsive configuration shows that networks that have shorter flights are better suited to the introduction of this technology, as the restriction on the design payload is less stringent. The proposed propulsive architecture allows a reduction of the operators’ yearly fuel budget of up to 50% in 2035 and 80% in 2050. At last, the taxi phase is of particular importance for regional aircraft that perform several rotations a day, therefore a further analysis of this phase is carried out. The result shows that the considered aircraft are capable of completing a full Landing & Take-Off (LTO) cycle without resorting to the thermal part of the propulsive system, reducing considerably the impact of the aircraft on the airport area.

Wide-Bandgap Semiconductors for Radiation Detection: A Review.

In this paper, an overview of wide-bandgap (WBG) semiconductors for radiation detection applications is given. The recent advancements in the fabrication of high-quality wafers have enabled remarkable WBG semiconductor device applications. The most common 4H-SiC, GaN, and β-Ga2O3 devices used for radiation detection are described. The 4H-SiC and GaN devices have already achieved exceptional results in the detection of alpha particles and neutrons, thermal neutrons in particular. While β-Ga2O3 devices have not yet reached the same level of technological maturity (compared to 4H-SiC and GaN), their current achievements for X-ray detection indicate great potential and promising prospects for future applications.

Research Status and Prospect of Liquid Carbon Dioxide Fracturing Technology

Liquid carbon dioxide fracturing technology has emerged as a novel unconventional method of retrieving oil and gas resources. As carbon dioxide is used as the fracturing fluid, it reduces the dependence on water resources and environmental pollution. The low viscosity and high diffusion properties of liquid carbon dioxide allow it to penetrate rocks more effectively, improving the efficiency of oil and gas extraction. In addition, the technology has potential carbon dioxide sequestration benefits that could help mitigate climate change. However, liquid carbon dioxide fracturing technology still faces challenges in terms of cost, technology maturity, and environmental impact. At present, academia and industry are actively engaged in research to improve the feasibility and economics of this technology, with the goal of providing new solutions for the sustainable development of energy in the future. This paper focuses on the research status and prospect of liquid carbon dioxide fracturing technology.

Evaluating Target Expansion for Eye Pointing Tasks

Abstract The idea of target expansion was proposed two decades ago for manual target acquisition, but it is not feasible to implement this idea in traditional user interfaces as the interactive system cannot know exactly which target is the desired one and should be expanded among several candidates. With the increasing maturity of eye tracking technology, gaze input has moved from an academically promising technique to an input method with built-in support in Windows 10; and target expansion has already become very feasible in the context of gaze input, as the user’s eye gaze is inherently an indicator of the desired target due to the natural eye-hand coordination in everyday tasks. However, a comprehensive evaluation is still lacking. In this study, two experiments were conducted, each with a different group of subjects, to investigate the effects of target expansion under different expansion feedback styles (visible vs. invisible), expansion factors, as well as different target appearances (i.e., circular vs. rectangular). The experimental results indicated that (1) the index of difficulty in eye pointing tasks (IDeye) does not depend on the initial size of the target, but on its final size, and that the corresponding human performance can be accurately predicted using the IDeye model instead of Fitts’ law; and that (2) the visible expansion style could disrupt the user’s fixations, making the measured human performance less efficient to some extent, but overall the theoretical predictions using the IDeye model were almost the same as the baselines. Following the experimental results, this study also provided some practical suggestions for UI design.

Multiscale analysis method and experiments for the transverse mechanical property of unidirectional fiber-reinforced composites

Carbon fiber-reinforced epoxy matrix composite laminates have been widely used in the case of advanced aero-engines, due to their high specific mechanical properties and mature production technology. In order to predict accurately the transverse mechanical properties of composites, this paper developed a multiscale analysis method which consists of three scales: At the microscale, an interfacial cohesive zone model is established based on atomic potential energy to describe the interface. At the mesoscale, a unit cell model is established to represent the fiber, matrix, and interface. At the macroscale, the homogenization method, failure criteria, and damage degradation models are utilized to predict transverse mechanical properties. The results of predictions are compared with experimental tests, and it is found that the maximum errors are less than 3%. Furthermore, the predicted damage evolution path aligns with the experimental results, thus validating the accuracy of the multiscale analysis method. By employing the multiscale analysis method, the effects of interfacial strength on the macroscopic transverse mechanical properties are analyzed. The simulation results indicate that: The interfacial strength has a more pronounced influence on the transverse strength and ultimate strain compared with the transverse modulus. Reducing the interfacial strength has a greater influence on the transverse modulus, strength, and ultimate strain than increasing the interfacial strength. The interfacial cohesive zone model can reflect the nonlinearity of epoxy matrix composite materials. Overall, the multiscale analysis method proves to be effective in accurately predicting the transverse mechanical properties of composites, and provides insights into the interfacial strength’s role in these properties.

Constructing a hierarchical porous nanotube In2O3 anode for high-performance lithium-ion batteries

With the rapid development of electric vehicles and various electronic products, higher energy density lithium-ion batteries are imperative to develop. As one of the key materials for lithium-ion batteries, anode materials with high energy density have become a research focus. Especially, In2O3 has received increasing attention due to its mature preparation technology, environmental protection, low work voltage, and high theoretical lithium storage capacity. However, the poor conductivity and serious volume change during the lithium removal/insertion process of In2O3 anode limit its performance. Based on this, a hierarchical porous nanotube In2O3 (HPNT-In2O3) anode is successfully prepared, in which the continuous one-dimensional nanotube structure of HPNT-In2O3 and abundant porous channels can provide the large specific surface area, accelerate the charge transfer rate, and supply a large number of Li+ storage active site. Moreover, the HPNT-In2O3 anode can maintain a higher reversible specific capacity of 470 mAh g−1 after 180 cycles at 0.1C, improve the cycling performance and rate performance, compared with a granular In2O3 anode. This work provides a new avenue to develop high performance anode materials for lithium-ion batteries.

Application of LSTM neural network based UAV attitude control in attitude estimation

From now on, Unmanned Aerial Vehicle (UAV) technologies have become more mature, and UAV has been skillfully used in various regions under this mature technology. However, during UAV flight, they often encounter interference from external factors such as airflow, wind and temperature changes, which pose challenges to their stability and flight accuracy. This paper proposes an algorithm what is based on LSTM (Long, Short-Term Memory network), aiming that the flight attitude is affected when it is subject to external interference, while still ensuring the robustness of its flight performance in this state. In this paper, the optimization method of anti-jamming adaptive adjustment based on LSTM. In the meantime, a UAV dynamic model and neural network control based on dynamic modeling are established. Then article established the LSTM rule and designed the PID controller of LSTM. And to prove the effectiveness of the designed PID controller by pilot experiments with Matlab / simulated link simulation and flight experiments. Finally, step size response, autonomous tracking, robustness and anti-interference were tested by experimental simulations.

Scour Protection Measures for Offshore Wind Turbines: A Systematic Literature Review on Recent Developments

Offshore wind energy is considered as one of the most promising resources of clean and renewable energy to replace fossil fuels. Additionally, its cost is expected to be lower than onshore wind energy as the technology matures. Offshore wind turbines (OWTs) normally operate in harsh ocean environments, which could impact their structural integrity. Scour erosion around foundations of OWTs can substantially change the overall stiffness of these structures and shorten their lifetime. Currently, there are a limited number of studies on countermeasures and their engineering requirements for decreasing the scouring effect; this is due to their different hydraulic circumstances, such as their stability, reliability, and resistance capacity. To this end, advancements in scour protection measures in the offshore energy sector are evaluated in this paper through a thorough and critical review following the PRISMA systematic literature mapping approach. This includes 68 papers on scour protection and over 30 scour protection designs for various types of wind turbine foundations. Here, we aim to provide an overview of the latest scouring protection measures and their comprehensive assessment, as well as their prospects and future challenges. The findings of this study will provide key insights into scour protection measures for OWTs and will subsequently contribute to the future growth of the offshore renewable energy sector.