Mature Technology Research Articles

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector.

Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration.

A 64-pixel mid-infrared single-photon imager based on superconducting nanowire detectors.

A large-format mid-infrared single-photon imager with very low dark count rates would enable a broad range of applications in fields like astronomy and chemistry. Superconducting nanowire single-photon detectors (SNSPDs) are a mature photon-counting technology as demonstrated by their figures of merit such as high detection efficiencies and very low dark count rates. However, scaling SNSPDs to large array sizes for mid-infrared applications requires sophisticated readout architectures in addition to superconducting materials development. In this work, an SNSPD array design that combines a thermally coupled row-column multiplexing architecture with a thermally coupled time-of-flight transmission line was developed for mid-infrared applications. The design requires only six cables and can be scaled to larger array sizes. The demonstration of a 64-pixel array shows promising results for wavelengths between 3.4 μm and 10 μm, which will enable the use of this single-photon detector technology for a broad range of new applications.

Current Status and Analysis of the Clinical Application of Digital Technology in Oral Medicine

With the increasing maturity and popularization of digital technology in oral medicine, its application has now expanded to various clinical subspecialties of oral medicine. Digitalization has become one of the important development directions of oral medicine. What is the current development status of digital technology in oral medicine? In what ways is digital technology applied across various clinical specialties of oral medicine? Dentists are particularly concerned about these issues in their clinical work and research. In this paper, all the digital technologies applied in oral medicine are organized and categorized from a technical perspective. In this paper, we focused on presenting three-dimensional data acquisition technology, dental computer-aided design technology, dental computer-aided processing technology, and oral surgery implementation technology. Their technical principles, technical characteristics, applications in oral medicine, a secondary discipline of medicine, and the development status of domestically-developed technology are described and reviewed in detail. The other technologies such as oral digital materials, oral virtual simulation teaching, and oral multi-source data management are briefly discussed. We intend to provide references for dentists to apply digital technology in clinical practice and research.

Advancing speed limit detection in ADAS: A novel data-driven approach using Pareto-GBDTMO

Recognizing speed limit information is crucial for advanced driver assistance systems (ADAS) as it directly affects the safety planning and decision-making process of intelligent driving systems. However, traditional image recognition-based solutions confront inherent restrictions and precision issues due to uncontrolled external factors. This paper endeavors to present a novel, data-driven solution for speed limit information recognition that leverages the stability and maturity of data-driven technologies, overcoming these challenges. We introduce Pareto-GBDTMO, a cutting-edge method that synergistically blends Gradient Boosting Decision Trees for Multiple Output (GBDT-MO) and Fast Pareto Feature Selection (FPFS). This integration is instrumental in discerning salient features to direct and expedite the learning process of GBDT-MO. When coupled with Bayesian optimization, the feature set undergoes dynamic updates at each boosting iteration, allowing GBDT-MO to concentrate on the most prominent features. This adaptive, relevance-guided feature space regularization mechanism enhances the efficiency and precision of speed limit recognition. Fujian Province highway electronic toll collection (ETC) data is used for further validation, and the experimental results emphasize the effectiveness of our model, with an high accuracy of 97%, a low loss rate of 0.7%, and minimal latency. These findings affirm the feasibility and scientific validity of our data-driven approach, offering a reliable and redundant solution for speed limit information recognition in ADAS. This study not only contributes to the practical application of ADAS but also lays the groundwork for future large-scale lane-level data research

Development status and application analysis of new energy photovoltaic power generation energy market based on artificial intelligence

Abstract With the rapid development of artificial intelligence, it is present in various fields. With the progress of the economy, existing energy has been unable to meet the growing demand of people, and the development of renewable resources to resolve the energy crisis is our common goal. In recent years, my country’s scientific and technological levels have greatly improved, and new energy photovoltaic power generation has also made great progress. The increasingly mature photovoltaic power generation technology in my country can effectively meet the demand for energy consumption and greatly reduce the waste of nonrenewable resources. This article mainly discusses the development status and application analysis of the new energy photovoltaic power generation energy market under the background of artificial intelligence and promotes the effective development of new energy in my country through the analysis of the new energy photovoltaic power generation energy market.

Research on prosthesis customization with 3D modeling and printing technology

With the development of science and technology and the maturity of technology, the application of 3D printing technology has developed from the art to the clinical medical treatment. Nowadays, the research and development and production of personalized customized orthopedic medical devices are possible, and the production line and supply chain of customized prostheses are constantly improving. The research on 3D printing and prosthesis customization technology is at the forefront of science. This article uses the method of literature review, reading, quoting, and summarizing several domestic and foreign professional experimental records and opinions, focusing on the history of 3D modeling and printing technology, the application method of this technology and the current clinical application of 3D modeling and printing technology. This paper puts forward reflection on the current shortcomings of the technology and my thinking and suggestions for future research. The conclusion of this paper is that 3D modeling and printing technology has played a great role in medical treatment, but there are still shortcomings in terms of cost and materials.

Machine learning approaches for automatic classification of single-particle mass spectrometry data

Abstract. The chemical composition of aerosol particles is a key parameter for human health and climate effects. Single-particle mass spectrometry (SPMS) has evolved to a mature technology with unique chemical coverage and the capability to analyze the distribution of aerosol components in the particle ensemble in real time. With the fully automated characterization of the chemical profile of the aerosol particles, selective real-time monitoring of air quality could be performed, e.g., for urgent risk assessments due to particularly harmful pollutants. For aerosol particle classification, mostly unsupervised clustering algorithms (ART-2a, K-means and their derivatives) are used, which require manual postprocessing. In this work, we focus on supervised algorithms to tackle the problem of the automatic classification of large amounts of aerosol particle data. Supervised learning requires data with labels to train a predictive model. Therefore, we created a labeled benchmark dataset containing ∼ 24 000 particles with eight different coarse categories that were highly abundant at a measurement in summer in Central Europe: elemental carbon (EC), organic carbon and elemental carbon (OC-EC), potassium-rich (K-rich), calcium-rich (Ca-rich), iron-rich (Fe-rich), vanadium-rich (V-rich), magnesium-rich (Mg-rich) and sodium-rich (Na-rich). Using the chemical features of particles, the performance of the following classical supervised algorithms was tested: K-nearest neighbors, support vector machine, decision tree, random forest and multi-layer perceptron. This work shows that despite the entrenched position of unsupervised clustering algorithms in the field, the use of supervised algorithms has the potential to replace the manual step of clustering algorithms in many applications, where real-time data analysis is essential. For the classification of the eight classes, the prediction accuracy of several supervised algorithms exceeded 97 %. The trained model was used to classify ∼ 49 000 particles from a blind dataset in 0.2 s, taking into account also a class of “unclassified” particles. The predictions are highly consistent with the results obtained in a previous study using ART-2a.

Energy Management Systems for Microgrids: Evolution and Challenges within the Framework of the Energy Transition

Context: Microgrids have been gaining space and credibility in terms of research and real applications. Technological maturity and new regulations have allowed these types of systems to position themselves as a real alternative to increase the coverage of the energy service and improve its quality. One of the biggest challenges of microgrids is the management of resources and their synchronization with conventional grids. In order to overcome the inconvenience of synchronizing and managing the components of a microgrid, research on management systems has been conducted, which usually consist of a set of modules and control strategies that manage the available resources. However, these studies have not reached unanimity on the best method to perform these tasks, which is why it is necessary to perform a systematic collection of information and clearly define the state of research in energy systems management for this type of network. Method: Based on the above, a systematic mapping was carried out in this article, wherein a significant number of papers that have contributed to this area were compiled. Taxonomies were generated based on the nature of the variables collected. These variables correspond to the data or information that enters and/or leaves the microgrid management system, such as meteorological variables, power, priority loads, intelligent loads, economic, operating states, and binary outputs. Conclusions: It was observed that, despite the advances in studying different techniques and strategies microgird control and management, other factors that may affect performance have not been covered in a relevant way, such as the nature of variables and microgrid topology, among others.

Recent Advances in Digital Twins for Agriculture 5.0: Applications and Open Issues in Livestock Production Systems

The potential application of the digital twins concept and its principles to all industries, including agriculture, has recently been the subject of considerable research interest. Since livestock farming is one of the mainstays of the agricultural industry, contributing to food security as well as employment and income generation, digital twins appear to be a promising technology to address issues related to the intensive nature of livestock production systems, which are typically characterized by high levels of input and output. The objective of this study is to identify and analyze the latest scientific and technological contributions in the field of digital twins technology, with a view to its adoption and assimilation in livestock production systems in the context of Agriculture 5.0. In particular, the aim of this study is to synthesize the existing research and identify the opportunities and open issues related to the implementation of digital twins in livestock production systems. The results derived in the literature review indicate that digital twins is still in the early stages of development in livestock farming, but it has the potential to transform the industry. As Agriculture 5.0 evolves and the digital twins technology matures, it is expected to play an increasingly important role in the livestock farming industry.

Characterization of Production under feed-in tariffs in Portugal Mainland

EDP Serviço Universal (EDP SU), in its core business, must ensure the purchase of energy from producers under special regime and distributed production, with feed-in tariffs, and sell it through spot and future markets. Furthermore, the company must ensure the purchase of energy in those markets for the supply of its customers. The process of buying and selling electricity is quite complex from the legislative and regulatory point of view. In Portugal, over the last two and a half decades, high focus on the promotion of renewable energies has been set, particularly through power plants with feed-in tariffs, which has increased the use of internal energy resources and has stimulated the reduction of the country’s energy dependence. During this process, the development of related legislation and the dynamism of the sector, with the introduction of incentives which prompted the investment and promoted the maturation of new technologies, has been essential. The present work characterizes the producers to whom EDP SU purchases energy, with emphasis on technologies, number of installations, installed power, geographic location and acquired energy, showing the evolution over last years.

Mars In Situ Resource Utilization (ISRU) with Focus on Atmospheric Processing for Near-Term Application—A Historical Review and Appraisal

The inspirational paper by Ash, Dowler, and Varsi in 1978, proposing to utilize in situ resources on Mars (ISRU) rather than bringing them from Earth, originated the field of Mars ISRU that has been the subject of research ever since. In this paper, we reviewed significant research reported on Mars ISRU since 1978 and reported briefly on accomplishments. We found that prior to 2014, progress on small tasks was sporadic and intermittent, always at low Technology Readiness Level (TRL). In 2014, the National Aeronautics and Space Administration (NASA) took a bold, imaginative, unprecedented step to fund a major project in Mars ISRU: the so-called “MOXIE” (Mars Oxygen In Situ Experiment), in which an oxygen production plant based on solid oxide electrolysis (SOEC) was developed, and finally demonstrated on Mars in 2022 and 2023. While MOXIE leaves behind it a wealth of accomplishments, there remains the need to close remaining gaps with additional laboratory and field work. Solid-oxide electrochemical cell (SOEC) technology has become a major area of worldwide investment for terrestrial energy and CO2 control. There is a very strong overlap between this terrestrial technology and Mars ISRU. NASA has already leveraged the terrestrial development work via MOXIE. NASA can leverage further advances with a comparatively small investment beyond 2023. Because NASA is engaged in a major program to return humans to the Moon, NASA’s focus is on lunar ISRU. Unfortunately, the mission impact and return on investment for lunar ISRU does not compare to that for Mars ISRU. NASA’s concept for Mars ISRU is futuristic, involving autonomous mining, transporting, and processing large amounts of Mars regolith. This might well occur long after initial human landings which could better profit in the near-term from MOXIE technology. By continuing further development of SOEC technology beyond MOXIE, while leveraging large investments in terrestrial applications, NASA can develop the Mars ISRU appropriate to nearer term human missions at modest investment. The goal of this paper is to place the relatively mature MOXIE technology advance and solid oxide electrolysis in general in perspective to the historical evolution of low TRL Mars ISRU technology.

Advancements in copy number variation screening in herbivorous livestock genomes and their association with phenotypic traits.

Copy number variations (CNVs) have garnered increasing attention within the realm of genetics due to their prevalence in human, animal, and plant genomes. These structural genetic variations have demonstrated associations with a broad spectrum of phenotypic diversity, economic traits, environmental adaptations, epidemics, and other essential aspects of both plants and animals. Furthermore, CNVs exhibit extensive sequence variability and encompass a wide array of genomes. The advancement and maturity of microarray and sequencing technologies have catalyzed a surge in research endeavors pertaining to CNVs. This is particularly prominent in the context of livestock breeding, where molecular markers have gained prominence as a valuable tool in comparison to traditional breeding methods. In light of these developments, a contemporary and comprehensive review of existing studies on CNVs becomes imperative. This review serves the purpose of providing a brief elucidation of the fundamental concepts underlying CNVs, their mutational mechanisms, and the diverse array of detection methods employed to identify these structural variations within genomes. Furthermore, it seeks to systematically analyze the recent advancements and findings within the field of CNV research, specifically within the genomes of herbivorous livestock species, including cattle, sheep, horses, and donkeys. The review also highlighted the role of CNVs in shaping various phenotypic traits including growth traits, reproductive traits, pigmentation and disease resistance etc., in herbivorous livestock. The main goal of this review is to furnish readers with an up-to-date compilation of knowledge regarding CNVs in herbivorous livestock genomes. By integrating the latest research findings and insights, it is anticipated that this review will not only offer pertinent information but also stimulate future investigations into the realm of CNVs in livestock. In doing so, it endeavors to contribute to the enhancement of breeding strategies, genomic selection, and the overall improvement of herbivorous livestock production and resistance to diseases.

Scientific and technological advances in the development of sustainable disease management tools: a case study on kiwifruit bacterial canker.

Plant disease outbreaks are increasing in a world facing climate change and globalized markets, representing a serious threat to food security. Kiwifruit Bacterial Canker (KBC), caused by the bacterium Pseudomonas syringae pv. actinidiae (Psa), was selected as a case study for being an example of a pandemic disease that severely impacted crop production, leading to huge economic losses, and for the effort that has been made to control this disease. This review provides an in-depth and critical analysis on the scientific progress made for developing alternative tools for sustainable KBC management. Their status in terms of technological maturity is discussed and a set of opportunities and threats are also presented. The gradual replacement of susceptible kiwifruit cultivars, with more tolerant ones, significantly reduced KBC incidence and was a major milestone for Psa containment - which highlights the importance of plant breeding. Nonetheless, this is a very laborious process. Moreover, the potential threat of Psa evolving to more virulent biovars, or resistant lineages to existing control methods, strengthens the need of keep on exploring effective and more environmentally friendly tools for KBC management. Currently, plant elicitors and beneficial fungi and bacteria are already being used in the field with some degree of success. Precision agriculture technologies, for improving early disease detection and preventing pathogen dispersal, are also being developed and optimized. These include hyperspectral technologies and forecast models for Psa risk assessment, with the latter being slightly more advanced in terms of technological maturity. Additionally, plant protection products based on innovative formulations with molecules with antibacterial activity against Psa (e.g., essential oils, phages and antimicrobial peptides) have been validated primarily in laboratory trials and with few compounds already reaching field application. The lessons learned with this pandemic disease, and the acquired scientific and technological knowledge, can be of importance for sustainably managing other plant diseases and handling future pandemic outbreaks.

A global analysis of renewable energy project commissioning timelines

Many countries are targeting the rapid and deep decarbonization of their energy supply, with renewable energy technologies playing a key role. Despite increased technology maturity and dramatically decreased costs, the deployment momentum of renewables often lags behind ambitious policy commitments. Accordingly, policymakers must accelerate the development of projects to meet decarbonization goals. However, we currently lack a comprehensive understanding of how long projects of renewable energy take to commission, whether projects are executed faster as technologies mature, and which factors affect the timelines – this knowledge gap affects policymakers and energy researchers providing model-based policy advice alike. To fill this gap, we analyzed global commissioning times between 2005 and 2022, drawing on the data for 12,475 projects using solar photovoltaic (PV), wind onshore, wind offshore, biomass, and run-off-river hydro in 48 countries. We found that average commissioning times have increased substantially over the past two decades for all renewable energy technologies of all project sizes. This finding highlights the need to incorporate up-to-date commissioning times in energy models to generate realistic model-based policy advice. In addition, we identified five categories of factors that affect commissioning timelines, conducted cross-sectional analyses with fixed-effect models, and present implications for policymakers, businesses, and researchers.

Research on Enterprise Regulation under the Background of Financial Technology

With the development and maturity of technology, the financial industry has become increasingly connected to technology. In recent years, fintech companies have flourished in the financial industry and are gradually becoming an important mainstay of global business empowerment. Fintech has opened up new areas of financial business and driven the development of finance and society. It has revolutionized the financial industry with the high efficiency brought by technology, but at the same time, it has also produced adverse events such as Alipay's annual bill exposing personal privacy and P2P mines, which have brought new risks for financial institutions and investors in general, and therefore it is urgent to take strict measures to regulate it. This paper starts from the connotation of fintech, briefly describes the development history of fintech in China, and puts forward corresponding countermeasures and suggestions on how to better regulate fintech enterprises based on the high-frequency risks faced by fintech enterprises and the internal operational deficiencies of the regulatory authorities.

A Proposed Framework for ERP System Implementation in SMEs

SMEs face numerous challenges and opportunities due to their pivotal roles in the economic development of a country. Adopting an ERP system is believed to be the catalyst to cope with the challenges and grab the opportunities faced by SMEs. However, implementing ERP led to potential risks caused by implementation failure, even for big companies. SMEs have a unique characteristic due to being receptive to adopting new technologies but having limited resources. Most previous research related to designing frameworks for ERP implementation was focused on big companies, although the fact that SMEs have distinct characteristics compared to big companies. Therefore, this study aims to design a framework for SMEs' ERP implementation. The framework phase consists of (1) measuring the business maturity, information communication and technology (ICT) maturity, proposed business process improvement, and (2) implementing an ERP system. The author has also provided a case study of ERP adoption in an SME in this paper by compiling both steps. This research will contribute to research on ERP in SMEs and practice as guidance for ERP implementors and SMEs in adopting the ERP system

Learning analytics dashboards are increasingly becoming about learning and not just analytics - A systematic review

This systematic review explores the emerging themes in the design and implementation of student-facing learning analytics dashboards in higher education. Learning Analytics has long been criticised for focusing too much on the analytics, and not enough on the learning. The review is then guided by an interest in whether these dashboards are still primarily analytics-driven or if they have become pedagogically informed over time. By mapping the identified themes of technological maturity, informing frameworks, affordances, data sources, and analytical levels over publications per year, the review identifies an emerging trajectory towards student-focused dashboards. These dashboards are informed by theory-oriented frameworks, designed to incorporate affordances that supporting student learning, and realised through integration of more than just activity data from learning management systems – allowing the dashboards to better support students' learnings processes. Based on this emerging trajectory, the review provides a series of design recommendations for student-focused dashboards that are connected to learning sciences as well as analytics.

A Cross-Citation-Based Model for Technological Advancement Assessment: Methodology and Application

Assessing technological advancements is crucial for the formulation of science and technology policies and making well-informed investments in the ever-evolving technology market. However, current assessment methods are predominantly geared towards mature technologies, limiting our capacity for a systematic and quantitative evaluation of emerging technologies. Overcoming this challenge is crucial for accurate technology evaluation across various fields and generations. To address this challenge, we present a novel approach that leverages bibliometrics, specifically paper citation networks, to gauge shifts in the flow of knowledge throughout the technological evolution. This method is capable of discerning a wide array of trends in technology development and serves as a highly effective tool for evaluating technological progress. In this paper, we showcase the accuracy and applicability of this approach by applying it to the realm of mobile communication technology. Furthermore, we provide a comparative analysis of its quantitative results with other conventional assessment methods. The practical significance of our model lies in providing a nuanced understanding of emerging technologies within a specific domain, enabling informed decisions, and fostering strategic planning in technology-oriented fields. In terms of originality and value, this model serves as a comprehensive tool for assessing technological progress, quantifying emerging technologies, facilitating the evaluation of diverse technological trajectories, and efficiently informing technology policy-making processes.

Unveiling the evolution of light within photonic integrated circuits

Silicon photonics leverages mature semiconductor technology to produce cost-effective and high-performance components for various applications in data centers, artificial intelligence, and quantum computing. While the geometry of photonic integrated circuits can be characterized by existing means, their optimal and accurate performance requires detailed characterization of the light propagating within them. Here we demonstrate the first, to our knowledge, direct visualization of the light as it travels inside photonic integrated circuits. We employ the natural nonlinear optical properties of silicon to directly map the electric field of the waves guided inside the integrated circuits, characterizing waveguides and multimode splitters while extracting various parameters of the device—all in real-time and in a noninvasive manner. Our approach for visualizing light inside photonic circuits is the only solution directly providing such information without any overhead or penalty, potentially making it a crucial component for the characterization of photonic circuitry, toward their improved design, fabrication, and optimization.

Novel Control System Based in DSPs for 800 kW Wind Power Station.

Wind energy conversion is a very mature technology. Its applications are widely accepted all over the world. Consequently, a tendency to erect more wind turbines can be observed. Therefore, wind turbines may gradually start to replace the output of conventional generators in the future, specially during low load situations with much wind. The wind power has a huge potential to supply electrical power without generating pollution. It has the potential to provide many countries with more of one-third of their electricity demand. A novel control system for a variable speed wind turbine generator has been developed in order to improve the wind turbine behaviour, the hardware is based in two digital signals processors (DSPs) in parallel operation, first one is a TMS320VC33, which manages all the signal analysis, control strategies, the protection systems and mechanical behaviour, and second one is a TMS320LF2407, that controls the PC monitoring program communication, the chopper and inverter PWM devices, temperature measures, encoders velocity and the programmable logic controller communication. The conjunction of the variable speed constant-frequency operation with the digital signal processors capacities improves cost and reliability, low Harmonic distortion (THD), stresses reduction in the drive train, turbine efficiency, audible noise minimization, mechanical design, active power production, reactive production or absorption (leading or lagging power factor) and control strategies (PWM, for example).