Critical Technology Research Articles

A high-precision data-driven radar modeling method for autonomous driving simulations testing

Autonomous driving represents the future of transportation, and simulation testing is a critical technology for ensuring the safety and reliability of autonomous driving. In simulation systems, a real-time and accurate radar model is crucial for enhancing the confidence of autonomous driving simulation systems. Existing radar models are typically simplistic and cannot accurately reflect the detection results of real radars. This paper introduces a data-driven radar modeling method that analyzes radar detection mechanisms to identify the model’s input parameters. Furthermore, this method decouples the model’s output parameters based on the differing mechanisms of radar output parameters and tailors model structures to align with the mechanistic characteristics of each parameter. Subsequently, considering the varied influences among input parameters, a segmented model structure is proposed that reduces input dimensionality while retaining critical information. Lastly, the model outputs are optimized using Kalman filtering. This study collects radar data from real vehicles to train the radar model. Testing has demonstrated that the radar model developed by this paper’s method yields outputs very close to actual radar predictions, with a significant improvement in prediction accuracy compared to traditional radar models. The model operates in approximately 0.8 ms, markedly faster than the real radar’s 72-millisecond detection cycle. This demonstrates that the radar model developed using this modeling method can predict radar detection results accurately and in real-time.

Improving Non-Line-of-Sight Identification in Cellular Positioning Systems Using a Deep Autoencoding and Generative Adversarial Network Model

Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.

Performance analysis of a molten salt packed-bed thermal energy storage system using three different waste materials

Concentrated Solar Power (CSP) is a critical technology for the renewable energy transition, offering high power output at elevated temperatures. However, further integration of CSP plants requires a reduction in investment costs. This study investigates the use of cost-effective, sustainable, waste-based TES materials—such as Electric Arc Furnace Black Slag (BS) and Tundish (TN) from the steel industry, as well as Demolition Waste (DW) from urban regeneration projects—as packing materials in TES systems to reduce the capital expenditure of CSP plants. A One-Dimensional Continuous Solid Phase (1D-2P) model was employed to evaluate and compare the performance of DW, TN, and BS. The results revealed that all materials demonstrated comparable properties, with TN exhibiting the highest energy storage capacity (44.7 kWh) and energy storage density (296 kWh/m³). With high utilization rates of 73–75 %, waste-based TES systems show strong potential for application in CSP plants. The TES systems were scaled for a 110 MW CSP plant, which currently operates with a 2-tank molten salt TES system providing 4648.4 MWh of storage capacity. TN required the smallest storage volume of 22,273 m³ for the 110 MW CSP plant. The reduction of molten salt usage by up to 31,000 tons in the waste-based packed-bed TES system could significantly enhance the economic feasibility of CSP plants.

Sino-Belgian research collaborations and Chinese military power

ABSTRACT China’s rise as a scientific superpower and its increasingly assertive geopolitical stance have raised concerns among Western governments over the security implications of research collaborations with China. Indeed, studies have revealed how China uses technology transfers to advance its military capabilities. In this article, we examine the links of Sino-Belgian research collaborations to the Chinese military and the evolution of these collaborations using a mixed methods approach comprising a bibliometric analysis and two case studies. Our study finds that 10% of Sino-Belgian collaborations were on critical technologies and conducted with scientists affiliated to Chinese institutes with military links. The collaborations with a military link have increased rapidly over the last two decades and have grown faster than the overall Sino-Belgian research collaborations. The results of the study reveal that research collaboration in the knowledge domain has crucial ties to the security domain and should therefore be considered in international security analysis. However, invoking Susan Strange’s structural power theory, we argue that Sino-Belgian research collaborations should not only be seen as contributing to China’s military power but also as a function of China’s growing overall power, including in the knowledge structure, where it entails the power to convey or deny knowledge.

Provably Quantum Secure Three-Party Mutual Authentication and Key Exchange Protocol Based on Modular Learning with Error

With the rapid development of quantum computers, post-quantum cryptography (PQC) has become critical technology in the security field. PQC includes cryptographic techniques that are secure against quantum-computer-based attacks, utilizing methods such as code-based, isogeny-based, and lattice-based approaches. Among these, lattice-based cryptography is the most extensively studied due to its ease of implementation and efficiency. As quantum computing advances, the need for secure communication protocols that can withstand quantum computer-based threats becomes increasingly important. Traditional two-party AKE protocols have a significant limitation: the security of the entire system can be compromised if either of the communicating parties behaves maliciously. To overcome this limitation, researchers have proposed three-party AKE protocols, where a third party acts as an arbiter or verifier. However, we found that a recently proposed three-party AKE protocol is vulnerable to quantum-computer-based attacks. To address this issue, we propose a provably quantum secure three-party AKE protocol based on MLWE. The proposed scheme leverages the user’s biometric information and the server’s master key to prevent the exposure of critical parameters. We analyzed the security of the protocol using simulation tools such as the Burrows–Abadi–Needham (BAN) logic, Real-or-Random (RoR) model, and Automated Validation of Internet Security Protocols and Applications (AVISPA). Furthermore, comparative analysis with similar protocols demonstrates that our protocol is efficient and suitable.

PB-Trajectron: Physics bounded neural network for generalized trajectory prediction

Vehicle trajectory prediction is a critical technology in autonomous driving systems, as the quality of prediction directly affects downstream path planning and vehicle control. Although recent studies have shown that models combining kinematic rules with data-driven methods exhibit better interpretability in trajectory prediction, these models often adopt fixed constraint strengths, limiting their generalization ability in diverse traffic scenarios. This fixed constraint strength design restricts the model’s adaptability to different traffic environments.To address this issue, we propose PB-Trajectron, a dynamic integration mechanism that enhances the model’s prediction performance and generalization capability. PB-Trajectron is a dynamic integration mechanism that combines vehicle kinematic rules with deep learning prediction models for generalized vehicle trajectory prediction. The model integrates physics-based motion models and Deep Neural Networks (DNNs) to provide reasonable physical explanations for predicting vehicle trajectories at different speeds. First, we establish two vehicle kinematic constraints applicable to different prediction scenarios, enabling the agent to switch between these constraints based on the causal relationships between vehicle motion states to avoid generating non-physical trajectory results. Second, we propose a kinematic constraint decision framework based on velocity thresholds, which demonstrates adaptive adjustment, allowing the agent to switch tasks according to real-time state conditions and adaptively adjust the contribution of different physical constraints based on actual vehicle speeds. Finally, we investigate the advantages of PB-Trajectron in Out-of-Domain(OOD) generalization.Cross-scenario experiments on the nuScenes dataset show that, compared to previous methods, PB-Trajectron reduces the final displacement error by 7.69% when the prediction step length is 4. Furthermore, out-of-domain generalization tests on the INTERACTION dataset demonstrate that PB-Trajectron achieves a 12.23% reduction in average prediction error on datasets from different countries and scenarios compared to previous methods. The proposed mechanism can better adapt to complex and diverse traffic scenarios, laying the foundation for explainable and robust autonomous driving systems.

A path planning method based on deep reinforcement learning for AUV in complex marine environment

The potential of autonomous underwater vehicle (AUV) on future applications is significant due to advancements in autonomy and intelligence. Path planning is a critical technology for AUV to perform operational missions in complex marine environments. To this end, this paper proposes a path planning method for AUV based on deep reinforcement learning. Initially, considering actual requirements, a complex marine environment model containing underwater terrain, sonobuoy detection, and ocean currents is established. Subsequently, the corresponding state space, action space, and reward function are formulated. Furthermore, to address the inherent limitations of existing deep reinforcement learning algorithms in terms of training efficiency, a mixed experience replay (MER) strategy is proposed. This strategy aims to enhance the efficiency of sample learning by integrating prior knowledge and exploration experience. Lastly, a novel HMER-SAC algorithm for AUV path planning is proposed by integrating the Soft Actor–Critic (SAC) algorithm with the hierarchical reinforcement learning strategy and the MER strategy. The results of the simulation and experiment demonstrate that the method is capable of efficiently planning executable paths in complex marine environments and exhibits superior training efficiency, stability, and performance.

Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained.

An electrocatalytic iodine oxidations-based configuration for hydrogen and I2/I3− co-productions driven by the Zn-air/iodine battery

Electrochemical water splitting and energy storage are key for a sustainable energy future, despite the challenges related to undesirable overpotentials and high voltage requirements. Herein, we introduce a synergistic approach between a low overpotential hydrogen evolution reaction (HER) and a low voltage zinc-air/iodine battery (ZAIB) by coupling with iodide oxidation half reactions. By developing a Pt/Co3O4 electrocatalyst in two steps and with under 2% Pt loading, we achieve an unprecedented low full cell potential for hydrogen generation at 0.574 V, exhibiting an ultra-high reduction of energy consumption of 64.7%. The Pt/Co3O4 electrode also enables ZAIB to record a power density of 154 mW cm−2 at an ultra-low charging potential of 1.68 V. Mechanistic studies and DFT calculations of the novel electrode confirm an electron rich Pt-Co interface and favorable Pt-I interactions, facilitating both HER and IOR reactions. Our design provides critical technology for potential large-scale renewable energy projects.

Thermal-hydraulic analysis of once-through steam generator with annular narrow slot tube

Integrated small modular reactors (SMR) have garnered significant attention in the field of nuclear energy, with the high-performance integrated steam generator being a critical technology for their advancement. The high-performance once-through steam generator (HP-OTSG) featuring an annular narrow slot tube, which leverages narrow channel enhanced heat transfer technology, holds significant potential for future applications. However, research on the design and experimental studies related to this type of steam generator remains significantly limited. This paper establishes a thermo-hydraulic analysis method for the OTSG based on the segmented heat exchanger design ideas, and the analysis method is verified by comparing the calculation results with those in the literatures. Then, the experimental studies conducted in the annular narrow slot tube and the related conclusions are comprehensively reviewed and subjected to an accurate assessment. The design parameters of a typical SMR, 200 MW Nuclear Heating Reactor-II (NHR200-II), are selected to evaluate and analyze the accuracy and validity of the correlation based on the annular narrow slot tube in the different heat transfer regions including the onset of nucleate boiling (ONB) point, saturated boiling heat transfer coefficient (hsat), dry-out point, and the post-dryout heat transfer coefficient (hpdo). Finally, the influences of operational and geometrical parameters on the performance of the HP-OTSG are analyzed, and the design optimization of the HP-OTSG is performed according to several performance indicators including Power Density (PD), Area Density (AD), pressure drop (Δp), secondary steam temperature (T2out), heat transfer factor (j), the proportion of nucleate boiling zone length in H (PNL), the point of dry-out (Xdryout), and the length of heat exchange tube (L). The model developed in this paper extends the application range of high-performance steam generators to annular narrow slot tubes. The calculation model including both of the analysis method and the heat transfer correlations established in the present study can serve as a useful tool for the design and optimization of HP-OTSG, which further prompts the development of integrated SMR with built-in steam generator.

Core Technologies of Sugarcane Chopper Harvester Extractor: A Critical Review

This review has systematically assessed the critical technologies of impurity removal fans in segmental sugarcane harvesters, highlighting their impact on minimizing harvester impurity rates. The analysis was conducted from three perspectives: Firstly, the physical properties of flexible sheet-like materials were thoroughly examined, including the methodologies for material parameter measurement, the utilization of technical approaches, and the constraints of existing methods. Secondly, the operational mechanisms of impurity removal fans were elucidated, encompassing their working principles, research subjects, and technical methodologies, and compared with the mechanisms of analogous fluid machinery. Lastly, a review of the structural design and parameters of impurity removal fans was undertaken, analyzing the interplay between operational modes, structural optimization designs, and aerodynamic characteristics. The review identified key issues in the design and performance of impurity removal fans and recognized the challenges in technological advancement. Through this triadic analytical framework, the review has identified salient design and performance deficiencies within existing impurity expulsion fan technologies and has underscored the formidable challenges confronting technological evolution in this domain. Synthesizing these insights, the review proffered prospective research vectors and avant-garde technological interventions poised to augment the operational efficacy of impurity expulsion fans, with the overarching goal of enhancing the operational proficiency of sugarcane harvesting machinery.

PECULARITIES OF DEVELOPING CRITICAL READING ABILITY OF THE MASTERS OF THE SPECIALTITY 7М01712 – TRAINING OF FOREIGN LANGUAGE TEACHERS

The article reveals the development of critical reading skills of master's students of the specialty 7M01712 - ‘Preparation of teachers of foreign languages’. The article emphasizes the importance of using lecture texts in the educational process to prepare master's students for professional activities. Effective use of professional texts in the classroom contributes to the improvement of scientific and professional speech and communicative competence of master's students, which in turn provides the optimal use of various methods of critical analysis in their future professional activities. Special attention is paid to the development of independent thinking skills, search, analysis, systematization, structuring and communication of information.The author identifies three stages of critical reading development. The author also suggests types of questions that promote the development of this type of reading, as well as the ability to identify contradictions and types of structures present in a text and to argue one's point of view based not only on logic, but also on the ideas of the interlocutor. The use of various methods and tools of critical reading technology at the stage of challenge, at the stage of reflection and at the stage of analysis is described. Methodological recommendations on mastering the technology of critical reading are intended for teachers of a foreign language, but can be useful for teachers of other subjects, since the technology of critical reading development belongs to the interdisciplinary modern educational technologies. The article presents practical experience of using some techniques of critical reading technology on the example of the special course “Cognitive linguocultural methodological foundations”.

Overview of Key Technologies in Surgical Robots and Optimization Discussions

Surgical robot is the national strategic diagnostic and therapeutic equipment research focus, get a number of scientific research institutes, colleges and universities and enterprises pay extensive attention to the design and development of a variety of surgical robots, and registration declaration. This article explores the critical technologies of surgical robots and key areas for optimizing their performance, including kinematic positioning errors, pose errors, feedback model errors, image recognition positioning errors, path planning, and safety aspects. The findings not only provide a scientific basis for future standardization research on surgical robots but also offer significant theoretical and practical references for the research, manufacturing, and registration processes in the medical robotics industry.

Advancements in Legged Walking Robots: Review

The purpose of this review paper is to contribute to the body of knowledge surrounding legged walking robots, specifically those utilizing four legs. It aims to inform researchers, engineers, and enthusiasts about the advancements, potential applications, and future prospects of these robots, ultimately driving further research and development in this exciting field. The review covered the most recent research articles in the field of designing and manufacturing robots, as well as how to choose the mechanism of actuator between hydraulic or electric systems and their impact on the weight increase and energy consumption, or potential noise generation during the operation like Bigdog robot. The payload was also assessed, with a comparison of the speed chart for each quadruped robot. It has been found that several critical technologies driving quadruped robots' advancement include new biomimetic structures, high power density actuators, techniques for controlling in real time and integrated environmental perception, depending on the application fields, such as industrial automation, healthcare, and exploration. Each of these elements is essential for achieving high-performance quadruped robots. Notably, the paper also delves understanding the fundamental methods used by mobile robots to perceive their surroundings, pay load (N), normalized speed (m/s), and engine mechanism. This work is valuable for all researchers in the field of a five-bar mechanism legged robot to keep up with the most recent advancements in this field. Furthermore, this review paper can be considered as a guideline for researchers who are intended to perform further research on mobile robot. Index Terms: Quadruped mobile robot, Payload, Mechanism, DOF, robotic leg.

COLREGs-Adaptive trajectory planning and decision-making in maritime autonomous surface ships

Decision-making for collision avoidance and trajectory planning are critical technologies for maritime autonomous surface ships. These systems must align with regulatory frameworks such as the International Regulations for Preventing Collisions at Sea (COLREGs) and account for the ship’s maneuvering capabilities for effective control and tracking. This study introduces a novel framework integrating regulatory consideration into the path-searching process, enhancing collision avoidance in both COLREG-compliant and non-compliant scenarios. The framework recasts the trajectory-planning problem into an optimal control problem and employs virtual obstacles and spatial–temporal navigation corridors consistent with collision-free decisions as constraints for trajectory optimization, improving navigation efficiency and comfort. The framework is validated through various encounter scenarios, and the results demonstrate that the proposed framework can produce superior collision avoidance decisions, while planning shorter navigation time and smoother collision-free trajectories, significantly improving the collision avoidance capability of maritime autonomous surface ships.

Technological Sovereignty and Economic Interests

Issues of technological sovereignty and strategic independence of the country are most often discussed from the standpoint of the realization of national interests. However, business has its own ambitions, its own ideas about the rational use of available resources and opportunities. The need to build partnerships between the state and business, to coordinate their economic interests, gives an institutional character to the problem of strengthening the technological sovereignty of the country. Institutional support is also necessary when combining the efforts of friendly countries in order to jointly achieve strategic autonomy. The development of measures to increase technological sovereignty involves the allocation of those links in the supply chains, without localization of which there is a high risk of being the object of external manipulation. Special attention is paid in the article to the choice of critical technologies, since it is influenced by narrow economic interests and the promotion of less significant technological areas by interested parties. It is shown that although the Russian economy has successfully adapted to the sanctions pressure, data on the structure of imports indicate the inertia of the country's technosphere. An analysis of the composition of Russian advisory bodies to determine the priority directions of scientific and technological development allows us to conclude that in the search for ways to strengthen the technological sovereignty of the country, not so much public-private as public-public partnership is being implemented. Various strategic measures aimed at updating the model of Russia's economic development do not remove the transition to a full-scale indicative planning system from the agenda.

The HOTspots Digital Surveillance: Conceptualisation to Clinical Deployment.

The HOTspots digital surveillance platform (HOTspots) is a critical technology of the HOTspots Surveillance and Response Program. It provides timely point-of-care access to pathology and demographic data from previously underserved regions. Co-designed with clinicians, epidemiologists, and health policy makers, the platform provides the evidence-base to empower efficient clinical management of patients with antimicrobial resistant (AMR) infections and supports national disease surveillance efforts in Australia. The pathway from conceptualisation to deployment for the HOTspots digital surveillance platform is described.

Contact State Recognition for Dual Peg-in-Hole Assembly of Tightly Coupled Dual Manipulator

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes significantly more complex. Additionally, when a tightly coupled multi-manipulator is required, the estimation errors in the contact forces between pegs and holes make contact state recognition challenging. The current state recognition methods have not been tested in such tasks. This paper tested Support Vector Machine (SVM) and several neural network models on these tasks and analyzed the recognition accuracy, precision, recall, and F1 score. An ablation experiment was carried out to test the contributions of force, image, and position to the recognition performance. The experimental results show that SVM has better performance than the neural network models. However, when the size of the dataset is limited, SVM still faces generalization issues. By applying heuristic action, this paper proposes a two-stage recognition strategy that can improve the recognition success rate of the SVM.

One case of comprehensive treatment of acute severe ammonia-induced intoxication with extracorporeal membrane oxygenation

Acute ammonia-induced intoxication is a kind of acute irritant gas poisoning, which mainly causes systemic inflammatory reaction and immune dysfunction through direct and indirect lung injury, leading to chemogenic pulmonary edema and acute respiratory distress syndrome (ARDS). The mechanism of its toxic injury is complex, and the fatality rate of patients increases significantly once ARDS occurs. In this paper, the patient with acute and severe ammonia-induced intoxication was successfully treated by extracorporeal membrane oxygenation (ECMO) combined with fiberoptic bronchoscopy and other critical technologies, and achieved good results. By analyzing the diagnosis and treatment process of severe ammonia-induced intoxication complicated with ARDS patients, this paper summarizes the clinical characteristics of ammonia-induced intoxication injury, and the application opportunities of ECMO and various critical medical technologies, so as to facilitate the efficient intervention and treatment of ammonia-induced intoxication according to the clinical opportunity and improve the survival rate of patients.

MAFNet: a two-stage multiple attention fusion network for partial-to-partial point cloud registration

Abstract 3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.