Search Results (2,966)

The origin of life initiated an evolutionary continuum yielding biologically optimized systems capable of operating under extreme environmental constraints. Biomimetics, defined as the systematic abstraction and transfer of biological principles into engineering domains, has become a strategic design paradigm for addressing the multifactorial challenges of space systems. This study introduces two core contributions to formally establish the discipline of Aerospace Bionic Robotics (ABR): First, it elucidates the relevance of biologically derived functionalities such as autonomy, adaptability, and multifunctionality to enhance the efficiency of space robotic platforms operating in microgravity environments. Second, it proposed the BEAM-D (Biomimetic Engineering and Aerospace Mechatronics Design), a standard for the development of Aerospace Bionic Robotics. By integrating biological abstraction levels (morphological, functional, and behavioral) with engineering protocols including ISO, VDI, and NASA’s TRL, BEAM-D enables a structured design pathway encompassing subsystem specification, cyber–physical integration, in situ testing, and full-scale mission deployment. It is implemented through a modular BEAM-DX framework and reinforced by iterative BIOX design steps. This study thus establishes formalized bio-inspired design tools for advanced orbital and planetary robotic systems capable of sustained autonomous operations in deep space exploration scenarios. Full article

We propose a novel numerical test to evaluate the reliability of numerical propagations, leveraging the fiber bundle structure of phase space typically induced by Lie symmetries, though not exclusively. This geometric test simultaneously verifies two properties: (i) preservation of conservation principles, and (ii) faithfulness to the symmetry-induced fiber bundle structure. To generalize the approach to systems lacking inherent symmetries, we construct an associated collective system endowed with an artificial G-symmetry. The original system then emerges as the G-reduced version of this collective system. By integrating the collective system and monitoring G-fiber bundle conservation, our test quantifies numerical precision loss and detects geometric structure violations more effectively than classical integral-based checks. Numerical experiments demonstrate the superior performance of this method, particularly in long-term simulations of rigid body dynamics and perturbed Keplerian systems. Full article

Traffic flow forecasting plays a significant role in intelligent transportation systems (ITSs) and is instructive for traffic planning, management and control.Increasingly complex traffic conditions pose further challenges to the traffic flow forecasting. While improving the accuracy of model forecasting, the parameter effectiveness of the model is also an issue that cannot be ignored. In addition, existing traffic prediction models have failed to organically integrate data with well-designed model architectures. Therefore, to address the above two issues, we propose the STAE-BiSSSM model as a solution. STAE-BiSSSM consists of Spatio-Temporal Adaptive Embedding (STAE) and Bidirectional Selective State Space Model (BiSSSM), where STAE aims to process features to obtain richer spatio-temporal feature representations. BiSSSM is a novel structural design serving as an alternative to Transformer, capable of extracting patterns of traffic flow changes from both the forward and backward directions of time series with much fewer parameters. Comparative tests between baseline models and STAE-BiSSSM on five real-world datasets illustrates the advance performance of STAE-BiSSSM. This is especially so on METRLA and PeMSBAY datasets, compared with the SOTA model STAEformer. In the short-term forecasting task (horizon: 15min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 1.89%/13.74%, 3.72%/16.19% and 1.46%/17.39%, respectively. In the long-term forecasting task (horizon: 60min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 3.59%/13.83%, 7.26%/16.36% and 2.16%/15.65%, respectively. Full article

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

High-precision time synchronization among nodes of the airborne-based pseudolite navigation augmentation positioning system (A-PNAS) is a crucial indicator for ensuring the accuracy of positioning services. Due to the flight characteristics and external factors’ influence, the airborne platform usually undergoes random motion. Therefore, the time-varying effect errors and Doppler effect errors will be introduced into the clock skew measurement results during the time-synchronous processing. In A-PNAS with meter-level positioning accuracy, the time synchronization accuracy (TSA) between nodes usually needs to be within 2 ns. These dynamic errors will have an impact on the TSA between nodes, which cannot be ignored. Based on the analysis of the principle of dynamic error generation and the available sensors, a multi-sensor combination method for correcting dynamic errors is proposed. This method calculates and corrects the dynamic errors based on the motion measurements from sensors. The simulation test results show that the degree of improvement in correcting dynamic errors by this method is basically close to 80%. It can effectively meet the requirements of high-precision time synchronization system and can provide an effective reference for the high-precision time synchronization processing of similar space-based platform collaborative systems. Full article

Color grading of boiled shrimp is a critical factor influencing market price, yet the process is usually conducted visually by buyers such as middlemen and processing plants. This subjective practice raises concerns about accuracy, impartiality, and fairness, often resulting in disputes with farmers. To address this issue, this study proposes a standardized and automated grading approach based on image processing and artificial intelligence. The method requires only a photograph of boiled shrimp placed alongside a color grading ruler. The grading process involves two stages: segmentation of shrimp and ruler regions in the image, followed by color comparison. For segmentation, deep learning models based on Mask R-CNN with a Feature Pyramid Network backbone were employed. Four model configurations were tested, using ResNet and ResNeXt backbones with and without a Boundary Loss function. Results show that the ResNet + Boundary Loss model achieved the highest segmentation performance, with IoU scores of 91.2% for shrimp and 87.8% for the color ruler. In the grading step, color similarity was evaluated in the CIELAB color space by computing Euclidean distances in the L (lightness) and a (red–green) channels, which align closely with human perception of shrimp coloration. The system achieved grading accuracy comparable to human experts, with a mean absolute error of 1.2, demonstrating its potential to provide consistent, objective, and transparent shrimp quality assessment. Full article

This paper proposes and evaluates two neural network-based approaches for object classification in automotive radar systems, comparing the performance impact of grid search and genetic algorithm (GA) hyperparameter optimization strategies. The task involves classifying cars, pedestrians, and cyclists using radar-derived features. The grid search–optimized model employs a compact architecture with two hidden layers and 10 neurons per layer, leveraging kinematic correlations and motion descriptors to achieve mean accuracies of 90.06% (validation) and 90.00% (test). In contrast, the GA-optimized model adopts a deeper architecture with nine hidden layers and 30 neurons per layer, integrating an expanded feature set that includes object dimensions, signal-to-noise ratio (SNR), radar cross-section (RCS), and Kalman filter–based motion descriptors, resulting in substantially higher performance at approximately 97.40% mean accuracy on both validation and test datasets. Principal Component Analysis (PCA) and SHapley Additive exPlanations (SHAP) highlight the enhanced discriminative power of the new set of features, while parallelized GA execution enables efficient exploration of a broader hyperparameter space. Although currently optimized for urban traffic scenarios, the proposed approach can be extended to highway and extra-urban environments through targeted dataset expansion and developing additional features that are less sensitive to object kinematics, thereby improving robustness across diverse motion patterns and operational contexts. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

Osteoarthritis involves complex interactions between articular joint tissues and the immune system, which is implicated in molecular trafficking via barrier-function modulating cytokines. The current study aims to test effects of an acute spike in TNF-α or TGF-β on vascular barrier function at multiple length scales, from the heart to tissue compartments of the knee, and cellular inhabitants of those respective compartments, in a spontaneous guinea pig model of osteoarthritis. First we quantified the intensity of a fluorescent-tagged 70 kDa tracer, similar in size to albumin, the most prevalent transporter protein in the blood, in tissue compartments of bone (periosteum, marrow space, compact bone, and epiphyseal bone) and cartilage (superficial cartilage, calcified cartilage, and the interface between, i.e., the epiphyseal line), as well as at sites of tendon attachment to bone (entheses). We then examined tracer presence and intensity in the respective pericellular and extracellular matrix zones of bone and cartilage. Acute exposure to TGF-β reduced barrier function (increased permeability) at nearest vascular interfaces in four of eight tissue compartments studied, compared to TNF-α where one of eight tissue compartments showed significant diminishment in barrier function. The increase in permeability associated with reduced barrier function was observed at both tissue compartment and cellular length scales. The observation of pericellular transport of the albumin-sized molecules to osteocytes contrasts with previous observations of barrier function in healthy, untreated animals and is indicative of increased molecular transport in pericellular regions of musculoskeletal tissues in cytokine-treated animals. Understanding age- and disease-related changes in molecular transport within musculoskeletal structures, such as the knee joint, is crucial for elucidating the etiology and pathogenesis of osteoarthritis. Full article

This study investigates underground heat sources and develops effective strategies for mitigating heat hazards in coal mines, with a focus on the design and optimization of cooling systems. Using the 3107 fully mechanized mining face of Menkeqing Coal Mine as a case study, geological survey data and in situ measurements were combined to evaluate the severity of thermal hazards. Thermodynamic and heat transfer models were applied to quantify heat dissipation from multiple sources. Computational fluid dynamics (CFD) simulations, based on data-driven modeling and geometric reconstruction, tested different equipment layouts and spacing configurations to identify optimal cooling schemes. Field implementation of the designed cooling system confirmed its effectiveness, offering practical guidance for improving heat hazard control and cooling system optimization in deep coal mines. Full article

The need for non-toxic chemical propulsion systems is growing stronger in today’s space sector. One of the possible solutions for next-generation bipropellant systems is using hydrogen peroxide as the oxidizer. However, there is limited knowledge about using 98% High-Test Peroxide (HTP), which can enable high mass and volumetric performance. Therefore, this paper presents an overview of the development of green bipropellant technology using 98% HTP. The goal is to cover nearly 15 years of experience with 98% HTP and over 10 years of the use of bipropellants containing 98% HTP. The development approach and methods, including component testing and hot-firing, are described. This paper provides test data for various types of bipropellant thrusters and engines producing between 20 and 7000 N of thrust in vacuum, which is the range typically utilized for in-space propulsion. Fuel ignition processes via utilization of a catalyst bed and via hypergolic ignition are analyzed. Successful demonstrations under different operating requirements (steady state, pulse-mode operations, throttleability, etc.) are discussed. The obtained results show that green bipropellants could compete with traditional storable bipropellant technologies. The challenges and opportunities associated with using HTP bipropellants in complete propulsion systems are listed. This paper concludes with recommendations for further research. Full article

Gas monitoring and dust control in coal mine goafs are critical for ensuring safe and efficient production. To address the challenges posed by dust accumulation from mechanized mining and ventilation systems, this study designs a spiral-driven gas purification pipeline robot integrating a wet dust removal mechanism. The robot features a modular structure, including a spiral drive, a plugging and extraction system, and a wet dust removal unit, to enhance pipeline adaptability and dust removal performance. Dynamic modeling reveals that the robot’s speed increases with the deflection angle of the driving wheel, with optimal performance observed at a 45° angle. The analysis of the rolling friction, medium resistance, and deflection angle indicates that reducing the angle improves the obstacle-crossing ability. Numerical simulations of gas migration in the goaf identify a high dust concentration at the air outlet and show that flow velocity significantly affects dust removal efficiency. Simulation and prototype testing confirm stable robot operation at deflection angles of between 30° and 90° and effective crossing of 5 mm barriers. Optimal dust removal is achieved with a 5 m/s flow velocity, 0.6 MPa water mist pressure, and 400 mm chord grid spacing, providing both theoretical and practical guidance for gas monitoring and dust control in coal mine goafs. Full article

Background: Acromioclavicular joint (ACJ) injuries frequently result from trauma to the shoulder girdle and are particularly common among young, physically active individuals. These injuries account for approximately 9% of all traumatic shoulder girdle injuries and often lead to functional impairment and pain. The TightRope^® system, LARS™ band, and Bosworth screw are among over 160 currently described surgical techniques for managing ACJ dislocations. However, there is no consensus regarding the optimal surgical approach, particularly for the management of moderate Rockwood Type III ACJ dislocations. Materials and Methods: In this retrospective study, data from 246 patients who underwent surgery for ACJ dislocation between 2010 and 2018 at the Department of Orthopedics and Trauma Surgery, Medical University of Vienna, were analyzed. Patients were divided into four cohorts based on the surgical technique used: Bosworth screw, LARS (acute), LARS (chronic), and TightRope. Clinical and radiological outcomes were assessed pre- and postoperatively using the Visual Analog Scale (VAS), Constant, Disability of the Arm, Shoulder and Hand Score (DASH), Simple Shoulder Test (SST), University of California—Los Angeles Shoulder Score (UCLA), Short Form Health Survey (SF-36), and American Shoulder and Elbow Surgeons score (ASES), as well as radiographic analysis. Radiological measurements of the acromioclavicular (AC) and coracoclavicular (CC) joint spaces were taken on both the injured and uninjured shoulders to analyze and compare the reduction in joint gaps. Results: All surgical methods resulted in significant reductions in AC and CC joint gaps. The TightRope and LARS acute groups showed the greatest reductions, with minimal complication rates. Complication analysis revealed significant differences in clavicular elevation (p < 0.001) and CC-ligament ossification (p = 0.006), which were most frequent in the Bosworth group and least common in TightRope^® patients, with LARS showing intermediate values. AC joint arthrosis was uncommon in all four groups and did not differ significantly (p = 0.13). Overall, TightRope^® was associated with the most favorable complication profile. The postoperative VAS score in the TightRope group was 1.52 ± 2.06, and the Constant score was 96.83 ± 5.41, reflecting high patient satisfaction. Conclusions: All systems led to satisfactory radiological and clinical outcomes, with the LARS™ band showing particular effectiveness in chronic ACJ dislocations. While all techniques provided good results, the TightRope^® system demonstrated the most favorable overall profile in our cohort and may therefore be considered a promising contemporary option. Further studies are needed to determine the optimal treatment for moderate ACJ dislocations and to assess the cost-effectiveness of these surgical techniques. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

This study presents the development and evaluation of an Augmented Reality (AR)-based training system aimed at improving patient setup accuracy in radiation therapy. Leveraging Microsoft HoloLens 2, the system provides an immersive environment for medical staff to enhance their understanding of patient setup procedures. High-resolution 3D anatomical models were reconstructed from CT scans using 3D Slicer, while Luma AI was employed to rapidly capture complete body surface models. Due to limitations in each method—such as missing extremities or back surfaces—Blender was used to merge the models, improving completeness and anatomical fidelity. The AR application was developed in Unity, employing spatial anchors and 125 × 125 mm² QR code markers to stabilize and align virtual models in real space. System accuracy testing demonstrated that QR code tracking achieved millimeter-level variation, with an expanded uncertainty of ±2.74 mm. Training trials for setup showed larger deviations in the X (left–right), Y (up-down), and Z (front-back) axes at the centimeter scale. This meant that we were able to quantify the user’s patient setup skills. While QR code positioning was relatively stable, manual placement of markers and the absence of real-time verification contributed to these errors. The system offers a radiation-free and interactive platform for training, enhancing spatial awareness and procedural skills. Future work will focus on improving tracking stability, optimizing the workflow, and integrating real-time feedback to move toward clinical applicability. Full article