Mature Technology Research Articles

Deep Learning and Human-machine Integration in Smart Prosthetic Control: Process and Perspectives

With the rapid development of science and technology and the maturity of deep learning technology, intelligent prosthetic limb control has made significant progress in human-machine integration. While traditional prosthetic technology has seen improvements in form and function, it still faces challenges in user experience and physiological adaptability. Deep learning, with its powerful pattern recognition and decision-making capabilities, offers new possibilities to address these issues. This paper reviews the current status and future development of the application of deep learning in intelligent prosthetic limb control, emphasizing its close relationship with the concept of human-machine integration. Intelligent prosthetic control involves not only mechanical engineering and bioengineering, but also interdisciplinary integration of the fields of neural engineering, electrical engineering, motion control and computer science. This paper reviews the limitations in traditional prosthetic technology, such as accuracy and user adaptability, introduces the successful cases and latest research results of deep learning in the medical field, and explores the potential of deep reinforcement learning in optimizing complex movements and adapting to different environments. Finally, the future direction of deep learning technology in smart prosthetic limb control is envisioned, and research challenges and recommendations are presented in the hope of providing theoretical support for the innovative development of smart prosthetic limb technology and improving the quality of life of people with disabilities.

Progress on Si‐based photoelectrodes for industrial production of green hydrogen by solar‐driven water splitting

AbstractSolar power has been regarded as the ultimate green‐energy source because of its inexhaustibility and eco‐friendliness. The solar‐driven water‐splitting technology for green hydrogen production is considered to be one of effective ways for solar energy harvesting and storage, which may provide solutions for the energy crisis and environmental issues. In the past decades, great progress has been achieved in this area. Photoelectrochemical (PEC) water splitting is especially promising for the production of solar fuels because of expected large‐scale industrial application. Silicon (Si), as an ideal candidate for the photoelectrode, is the most suitable material for the PEC device in industrial photocatalytic water splitting because of its abundance, mature fabrication technology, and suitable band gap. Here, we give a systematic review on the recent progress for Si‐based photoelectrodes for water splitting with a focus on the industrial application. Particularly, the strategies, such as band‐alignment control, morphology design, and surface engineering, are summarized to enhance the PEC performance and durability for practical application. Furthermore, the perspective for the design of commercial Si‐based PEC devices with high PEC performance, long‐term stability, large‐size, and low cost are given at the end, which shall guide the development of PEC water splitting for industrial application.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective.

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Fabrication and development of mechanical metamaterials via additive manufacturing for biomedical applications: A review

Abstract In this review, we provide a comprehensive overview of additive manufacturing (AM) technologies and materials as well as design possibilities to manufacture metamaterials for a variety of biomedical applications, of which many are inspired by nature itself. It is described that how new AM technologies (such as continuous liquid interface production and multiphoton polymerization) and new developments in more mature AM technologies (such as powder bed fusion, stereolithography and extrusion-based bioprinting) have led to more precise, efficient, and personalized biomedical components. Extrusion-based bioprinting, specifically, is a revolutionary topic creating intricate models with remarkable mechanical compatibility of metamaterials, such as negative/zero Poisson's ratio and stress elimination for tissue engineering and regenerative medicine. By exploiting a variety of designs for porous structures (such as truss, triply periodic minimal surface, and plant/animal-inspired and functionally graded lattices), AM made bioactive bone implants, artificial tissues and organs are made for tissue replacement purposes. The material palette of the AM metamaterials has also become very diverse nowadays ranging from metals and alloys (such as titanium and cobalt-chromium alloys) to polymers (like biodegradable polycaprolactone (PCL) and polymethyl methacrylate (PMMA)) which could be even integrated within bioactive bioceramics. These advancements are driving the progress of biomedical technology, improving human health and quality of life for future generations.

Innovative Circular Biowaste Valorisation—State of the Art and Guidance for Cities and Regions

The management of the organic fraction of municipal solid waste (OFMSW), also called urban biowaste, and urban wastewater sludge (UWWS) represents a challenge for cities and regions, which want to adopt innovative urban bioeconomy approaches for their treatment and production of high-added-value products beyond the traditional anaerobic digestion (AD) and compost. This adoption is often restricted by the availability and maturity of technologies. The research object of this manuscript, based on the findings of EU Horizon 2020 project HOOP, is the identification of state-of-the-art circular technologies for material valorisation of OFMSW and UWWS, following a novel screening methodology based on the scale of implementation (tested at least at pilot scale). The screening resulted in 25 technologies, which have been compared and discussed under a multidisciplinary assessment approach, showing their enabling factors and challenges, their current or potential commercial status and their compatibility with the traditional technologies for urban biowaste treatment (composting and AD). The bioproducts cover market sectors such as agriculture, chemistry, nutrition, bioplastics, materials or cosmetics. Therefore, the results of this review help project promoters at city/region level to select innovative technologies for the conversion of OFMWS and UWWS into high value products.

Acid Mine Drainage Neutralization by Ultrabasic Rocks: A Chromite Mining Tailings Evaluation Case Study

Chromite is formed in nature in ophiolitic layers and ultrabasic rocks through fractional crystallization. The corresponding mining technologies separate the ore from these ultrabasic rocks, which are considered to be tailings for the process but may be valorized in other applications. The need to utilize this material is due to the large quantities of its production and the special management required to avoid possible secondary pollution. In the present work, the ultrabasic rocks of chromite mining were applied to acid mine drainage (AMD) neutralization. The aim was to increase the technological maturity of the method and promote circular economy principles and sustainability in the mining sector. Ultrabasic rocks were obtained from a chromite mining facility as reference material. Furthermore, an artificial AMD solution was synthesized and applied, aiming to simulate field conditions. According to the results, the sample was successfully utilized in AMD neutralization (pH 7), achieving rapid rates in the first 30 min and maximum efficiency (liquid to solid ratio equal to 8.3) at 24 h. However, the method presented a drawback since Mg was leached, even though the concentration of other typical metals contained in an AMD solution decreased.

Using Technology Readiness Levels (TRLs) in UAS development

In the dynamic world of aeronautical technology, the development of uncrewed aircraft systems (UAS) is an area of continuous expansion and innovation. Technological progress in this sector can be measured by the Technology Readiness Levels (TRL) Scale, which provides a standardized framework for assessing the technological maturity of a product or system. The TRL scale is widely used in industry and research to guide the systematic development of new technologies from concept and initial research to operational implementation and commercialization. This scale can also be successfully used in the research, development, and deployment of an uncrewed aircraft system.

Research on the Structural Design of a Pressurized Cabin for Civil High-Speed Rotorcraft and the Multi-Dimensional Comprehensive Evaluation Method

For civil high-speed rotorcraft designed to operate at specific cruising altitudes, this study proposes nine structural design schemes for pressurized cabins. These schemes integrate commonly used materials and processing technologies in the aviation industry with advanced PRSEUS (Pultruded Rod Stitched Efficient Unitized Structure) technology. An analysis of the structural composition reveals that frames constitute 8–19% of the total structural weight, while stringers and beams make up 15–50%, and skins account for 11–25%, with thicknesses ranging from 1.0 mm to 2.0 mm. The separating interface of the pressurized cabin contributes 4–29% of the total structural weight. The weight distribution of each component in the pressurized cabin structure varies significantly depending on the chosen materials and processing technologies. Utilizing the Analytic Hierarchy Process (AHP), along with Gray Relational Analysis (GRA) and Dempster–Shafer (D-S) evidence theory, this study compares the simulation results of the nine schemes across multiple dimensions. The findings indicate that the configuration combining 7075 aluminum alloy and T300 composite material has the greatest advantages in terms of the high structural reliability of the configuration, light weight, mature processing technology, and low production cost. This comprehensive evaluation method quantitatively analyzes the factors influencing the structural configuration design of the pressurized cabin for civil high-speed rotorcraft, offering a valuable reference for the design of similar structures in related fields.

A Study on the Application of Meta-Cosmos Technology in Risk Management of Commercial Banks

In recent years, the meteoric rise of Meta-Cosmos technology has established it as a pivotal component of the digital economy. This amalgamation of cutting-edge technologies—including virtual reality (VR), augmented reality (AR), blockchain, and artificial intelligence—has fundamentally reshaped perceptions of the digital realm. As the cornerstone of the financial system, commercial banks have consistently confronted a dynamic array of challenges in risk management. With the acceleration of digitalization, traditional risk management methods have increasingly revealed their inadequacies, particularly in navigating the complexities of fluctuating market conditions and the escalating threats to cybersecurity. The advent of Meta-Cosmos technology offers commercial banks a novel perspective and innovative tools for risk management. By constructing virtual economic environments, banks can conduct more precise risk assessments and controls, thereby enhancing the scientific rigor and timeliness of their decision-making processes. Nevertheless, the application of Meta-Cosmos technology within the banking sector remains in its exploratory phase, facing challenges related to technological maturity, security, and regulatory compliance. This study aims to examine how Meta-Cosmos technology can be integrated into the risk management frameworks of commercial banks, thereby providing theoretical support and practical guidance for the future development of financial technology.

Multi-Fidelity Aeromechanical Design Framework for High Flow Speed Multistage Axial Compressors

Abstract The development of novel engine architectures is vital in achieving the aviation sector’s net-zero carbon emission target by 2050. With today’s digital decade providing support for an accelerated technology maturation, the challenge for turbomachinery design remains to significantly push the limits of current performance within an ambitious development lead time. In this context, it is essential to adopt a design framework where the predictive models or simulations employed target a sufficiently reliable performance assessment. These models must be tailored to the dynamics of evolving industrial design processes, and therefore continuously balance required design flexibility, robust evaluation, appropriate fidelity (i.e. the level of detail and accuracy they provide), and resulting evaluation time. This paper discusses a framework for designing axial compressors and its application to the aeromechanical optimisation of a high-speed compressor rotor. The design environment integrates geometry parametrisation, modular evaluation with different levels of fidelity for the aerodynamic and structural models, and surrogate-based optimisation (SBO) capabilities. It is shown how the combination of a modular sequencing of the different models and the acceleration enabled by high-performance computing (HPC) and machine learning allows for a more advanced preliminary design. A significant gain in isentropic efficiency is attained while satisfying all structural constraints. At the same time, it is demonstrated that the framework is compatible with the characteristics of the preliminary design phase: both in its ability to adapt to cycle and design changes as well as regarding the turnaround time of the optimisation itself.

Failure analysis of the leakage and ignition of heat transfer fluid in a concentrating solar power (CSP) pilot plant

Concentrating solar power (CSP) is a technology seen key to achieve the worldwide energy transition targets. Although it is a mature robust technology, different failures in CSP plant have been reported in the literature to visualize potential point of maintenance interest. Following the same philosophy, this paper reports a failure occurred at a CSP storage pilot plant, where leakage and ignition of different joint balls suddenly occurred after several hours of testing. The experimental assessment carried out confirmed spontaneous ignition of the heat transfer fluid-soaked insulation material of the piping after leakage. The results of this study emphasize the significance of proactive maintenance and daily inspections in solar fields.

Design of experimental facility for helium gas component testing

The current state-of-the-art helium gas test loops for component testing are constrained by the maximum operating temperatures, pressures, and flow rates of such facilities. Idaho National Laboratory is designing a new experimental facility known as HECTOR (HElium Component Testing Out-of-pile Research), which is a helium gas test loop for evaluating the performance of components to helium gas pressures, temperatures, and flow rates up to 8 MPa, 800 °C, and 0.15 kg/s, respectively. The construction of this facility will enable researchers to perform experiments across a wide range of Reynolds and Nusselt numbers, which could result in the maturation of technology for high-temperature gas-cooled reactor (HTGR) components.

The Illusion of a Green Transition in Slovenia by 2050

This study analyzes the possibilities of phasing out fossil and nuclear energy sources for Slovenia by 2050. Alternative carbon-free sources include renewable energy sources (RES) i.e. electricity, synthetic fuels and hydrogen from water electrolysis. The model is based on the use of currently mature low-carbon technologies and is adapted to Slovenia’s natural conditions. Photovoltaic panels (PV) and hydropower plants are used for the majority of renewable electricity generation. To bridge the winter period with minimal PV production, storage with a pumped storage power plant is planned. One of the assumptions of the national climate strategy has been incorporated into the model, which envisages zero growth in final energy consumption by 2050. The result of the paper is an assessment of what some of the basic characteristics of the Slovenian energy system would look like after the phase-out of fossil and nuclear energy sources. The estimated storage capacity required is 5.1 MWh/capita. Abandoning fossil fuels with the currently mature RES technologies is not realistically feasible for technical and economic reasons.

Evaluation of Tidal Asymmetry and Its Effect on Tidal Energy Resources in the Great Island Region of the Gulf of California

Hydrokinetic tidal energy is one of the few marine renewable energy resources with sufficiently mature technology for commercial exploitation. However, several parameters affect its exploitability, such as the minimum speed threshold, ambient turbulence levels, or tidal asymmetry, to name but a few. These parameters are particularly important in regions with lower mean speeds than those in first-generation sites, such as the North Sea. The Gulf of California is one of those regions. In this paper, a Delft3D Flexible Mesh Suite (Delft3D FM) model in barotropic configuration is set up over the Gulf of California using a flexible mesh with resolution varying from O (500 m) in the deep regions to O (10 m) in the coastal regions. A simulation is run over the year of 2020, with a tidal forcing of 75 components. The model is validated at four tidal gauge locations and four Acoustic Doppler Current profiler (ADCP) locations. The speed, U, and tidal power density (TPD) indicators used for the validation were the annual means, the annual means for speeds above the 0.5 m s−1 threshold, the annual means of the spring tide maxima, and the annual maxima. The contour maps of the annual means, that is, the annual means for speeds above the 0.5 m s−1 threshold, allow us to identify tidal energy hot spots throughout the Gulf of California, particularly in the Great Island region (GIR). In this region, these hot spots have higher U and TPD values, in agreement with previous studies. The patterns of circulation around Tiburón Island and San Esteban Island on the East, and Ángel de la Guarda Island and San Lorenzo Island on the West, the four islands in the region with the highest tidal energy potential, are also discussed while recognizing that Tiburón Channel, between Tiburón Island and San Esteban Island, has proved to be the best siting location, based on the technical results obtained so far. The hot spots sites are further characterized by computing the tidal asymmetry in these small regions, showing the locations of the sites with smallest asymmetry, which would be the best for tidal energy exploitation. The hot spots around San Esteban Island are particularly important because they have the largest TPD in the GIR, with the model predicting a TPD on the order of 500–1000 W m−2. Here, complementary field measurements obtained with two ADCPs, close to San Esteban Island, one at 15 m depth, SEs (shallow region), and the other at 60 m depth, SEd (deep region), produced TPDs of 1200 W m−2 and 400 W m−2, respectively. The analysis of the vertical profiles and the tidal asymmetry over the vertical shows the importance of developing 3D models in future investigations.

Modeling and simulation study of acoustic response for dual-membrane capacitive MEMS microphone

Abstract As the micro-electro-mechanical systems (MEMS) technology matures, MEMS sensors have found widespread applications in mechatronics, robotics, and voice control. The high stability, high integration, and radio frequency interference resistance of MEMS microphones have rapidly led to their adoption in these domains, displacing electret condenser microphones.This article focuses on modeling and analyzing dual-membrane capacitive MEMS microphone, utilizing an lumped equivalent circuit model to calculate the microphone’s acoustic frequency response. Furthermore, the article employs the finite element method (FEM) to conduct a detailed analysis of the resonant frequency of the membrane, as well as to explore the effects of changes in the volume of the front and back chamber on the packaging performance of MEMS microphones. Finally, physical tests were conducted, and the simulation results of the two models showed considerable consistency with the curves obtained from the physical tests, verifying the accuracy of the models.

The Potential of mRNA Vaccines to Fight Against Viruses.

Vaccines have always been a critical tool in preventing infectious diseases. However, the development of traditional vaccines often takes a long time and may struggle to address the challenge of rapidly mutating viruses. The emergence of mRNA technology has brought revolutionary changes to vaccine development, particularly in rapidly responding to the threat of emerging viruses. The global promotion of mRNA vaccines against severe acute respiratory syndrome coronavirus 2 has demonstrated the importance of mRNA technology. Also, mRNA vaccines targeting viruses such as influenza, respiratory syncytial virus, and Ebola are under development. These vaccines have shown promising preventive effects and safety profiles in clinical trials, although the duration of immune protection is still under evaluation. However, the development of mRNA vaccines also faces many challenges, such as stability, efficacy, and individual differences in immune response. Researchers adopt various strategies to address these challenges. Anyway, mRNA vaccines have shown enormous potential in combating viral diseases. With further development and technological maturity, mRNA vaccines are expected to have a profound impact on public health and vaccine equity. This review discussed the potential of mRNA vaccines to fight against viruses, current progress in clinical trials, challenges faced, and future prospects, providing a comprehensive scientific basis and reference for future research.

Application of Business Intelligence Based on Big Data in E-commerce Data Evaluation

With the vigorous development of e-commerce, more and more goods are sold online. The electronic platform not only brings convenience to people's lives but also gives more people the opportunity of employment and entrepreneurship, which contributes to the promotion of economic value and the creation of wealth. With the gradual maturity of network technology represented by Big Data, it has also led to the further development of e-commerce. In the past, e-commerce mostly used business intelligence systems for data analysis. However, as the data becomes more and more complex, the innovation ability and data analysis ability of traditional business intelligence systems are relatively conservative, so it is of great significance to update and strengthen business intelligence to analyze its role in e-commerce data. Therefore, this paper proposed the application of business intelligence based on Big Data in e-commerce data analysis. By combining Big Data and a business intelligence system and taking the e-commerce data of a certain brand of beverage as the research object, the Days-Times-Money (DTM) model was established, and the data mining technology was used to classify the brand consumers. The data results showed that among the three consumption attributes, the highest consumption density of consumption days, consumption times, and consumption amount was 68.63%, 67.99%, and 69.72% respectively. These were not more than 70%, which indicated that the consumption of this brand of beverages had a high space for growth. According to the consumption density, the users of this brand of beverage were divided into four consumer groups. According to the classification results, it provided feasible marketing reference opinions for the brand beverage and could provide direction for brand value-added by mining the hidden data of the brand beverage. This paper hoped to use the brand beverage as a case to provide reference and reality for the development of e-commerce enterprises and provide a reference for the application and promotion of business intelligence based on Big Data in e-commerce data analysis.

ScDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering.

Cell clustering is foundational for analyzing the heterogeneity of biological tissues using single-cell sequencing data. With the maturation of single-cell multi-omics sequencing technologies, we can integrate multiple omics data to perform cell clustering, thereby overcoming the limitations of insufficient information from single omics data. Existing methods for cell clustering often only consider the differences in data patterns during the analysis of multi-omics data, but the dependencies between omics features of different cell types also significantly influence cell clustering. Moreover, the high dropout rates in scRNA-seq and scATAC-seq data can impact the performance of cell clustering. We propose a cell clustering model based on a masked autoencoder, scDRMAE. Utilizing a masking mechanism, scDRMAE effectively learns the relationships between different features and imputes false zeros caused by dropout events. To differentiate the importance of various omics data in cell clustering, we dynamically adjust the weights of different omics data through an attention mechanism. Finally, we use the K-means algorithm for cluster analysis of the fused multi-omics data. On commonly used sets of 15 multi-omics datasets, our method demonstrates superior cell clustering performance on multiple metrics compared to other computational methods. In addition, when datasets exhibit varying degrees of dropout noise, our method shows better performance and stronger stability on multiple metrics compared to other methods. Moreover, by analyzing the cell clusters classified by scDRMAE, we identified several biologically significant biomarkers that have been validated, further confirming the effectiveness of scDRMAE in cell clustering from a biological perspective.

Plasma discharge experiments of a Faraday shieldless driver for high-power and long-pulse radio frequency ion source for NBI application.

Due to the high stability and less maintenance, the radio frequency (RF) driven ion source is preferred for the neutral beam injection (NBI) system. In a popular design of the RF ion source for NBI application, a Faraday shield (FS) is installed inside the RF plasma driver to protect the discharge tube. However, the FS also brings some drawbacks, such as lowering the RF power transfer and increasing the processing difficulty. A prototype of the RF plasma driver without FS and with mature manufacturing technology has been developed by using a water-cooled discharge tube. After basic testing, this prototype was further tested under the RF plasma discharge experiments in views of high power, long pulse, and long term. The reliability and its plasma characteristics were the focus of these experiments, also for the hidden issues. The results show that the prototype could generate stable and high-density plasma without any damage or sputtering mark. An RF plasma discharge of 50 kW and 20s has been achieved. The expected frequency tuning was equally effective on the prototype. Moreover, compared to the RF plasma driver with FS, the prototype could produce higher electron density in the extraction region under the same RF power. Of course, some of the shortcomings of the prototype have also been exposed in the experiments and will be improved in subsequent experiments.

Co-creation and evaluation of an algorithm for the development of a mobile application for wound care among new graduate nurses: A mixed methods study.

Chronic wounds are a growing concern due to aging populations, sedentary lifestyles and increasing rates of obesity and chronic diseases. The impact of such wounds is felt worldwide, posing a considerable clinical, environmental and socioeconomic challenge and impacting the quality of life. The increasing complexity of care requires a holistic approach, along with extensive knowledge and skills. The challenge experienced by health-care professionals is particularly significant for newly graduate nurses, who face a gap between theory and practice. Digital tools, such as mobile applications, can support wound care by facilitating more precise assessments, early treatment, complication prevention and better outcomes. They also aid in clinical decision-making and improve healthcare delivery in remote areas. Several mobile applications have emerged to enhance wound care. However, there are no applications dedicated to newly graduate nurses. The aim of this study was to co-create and evaluate an algorithm for the development of a wound care mobile application supporting clinical decisions for new graduate nurses. The development of this mobile application is envisioned to improve knowledge application and facilitate evidence-based practice. This study is part of a multiphase project that adopted a pragmatic epistemological approach, using the 'Knowledge-to-Action' conceptual model and Duchscher's Stages of Transition Theory. Following a scoping review, an expert consensus, and stakeholder meetings, this study was pursued through a sequential exploratory mixed methods design carried out in two phases. In the initial phase, 21 participants engaged in semi-structured focus groups to explore their needs regarding clinical decision support in wound care, explore their perceptions of the future mobile application's content and identify and categorize essential components. Through descriptive analysis, five overarching themes emerged, serving as guiding principles for conceptual data model development and refinement. These findings confirmed the significance of integrating a comprehensive glossary complemented by photos, ensuring compatibility between the mobile application and existing documentation systems, and providing quick access to information to avoid burdening work routines. Subsequently, the algorithm was created from the qualitative data collected. The second phase involved presenting an online SurveyMonkey® questionnaire to 34 participants who were not part of the initial phase to quantitatively measure the usability of this algorithm among future users. This phase revealed very positive feedback regarding the usability [score of 6.33 (±0.19) on a scale of 1-7], which reinforces its quality. The technology maturation process can now continue with the development of a prototype and subsequent validation in a laboratory setting.