Search Results (2,082)

Plant pathogen zoospores play a vital role in the transmission of several significant plant diseases, with their early detection being important for effective pathogen management. Current methods for pathogen detection involve labour-intensive specimen collection and laboratory testing, lacking real-time feedback capabilities. Methods that can be deployed in the field and remotely addressed are required. In this study, we have developed an innovative zoospore-sensing device by combining a microfluidic sampling system with a microfluidic cytometer and incorporating a chemotactic response as a means to selectively detect motile spores. Spores of Phytophthora cactorum were guided to swim up a detection channel following a gradient of attractant. They were then detected by a transient change in impedance when they passed between a pair of electrodes. Single-zoospore detection was demonstrated with signal-to-noise ratios of ~17 when a carrying flow was used and ~5.9 when the zoospores were induced to swim into the channel following the gradient of the attractants. This work provides an innovative solution for the selective, sensitive and real-time detection of motile zoospores. It has great potential to be further developed into a portable, remotely addressable, low-cost sensing system, offering an important tool for field pathogen real-time detection applications. Full article

With the development of 5G new radio (NR) applications in high-speed scenarios, such as 5G non-terrestrial networks (NTN), the Doppler shift in the systems is significant. In this paper, an estimation scheme for frequency offset with large range in orthogonal frequency division multiplexing (OFDM) systems is proposed. The proposed scheme firstly takes advantage of the $2\pi$-periodicity of the phase offset between two pilot OFDM symbols to estimate a set of candidate frequency offsets. It then uses the autocorrelation of the pilot sequence to determine the final estimated frequency offset. This method allows for a large estimation range, independent of the symbol gap between the two pilot OFDM symbols. Moreover, the low-complexity implementation of the scheme is provided. The simulation results based on 5G NR physical uplink shared channel (PUSCH) show the effectiveness of the proposed scheme in both single-user and multi-user scenarios, where various Doppler shifts and numbers of configured resource blocks (RB) are considered. The simulation results also show that the proposed frequency-domain method outperforms the conventional time-domain method with additional computation complexity. Full article

The overhead contact system of the electrification railway is exposed to the natural environment throughout the year and is liable to encounter the problem of line icing. The icing on the line will reduce the current-collection performance of the pantograph, resulting in a decrease in the safety and reliability of the overhead contact system. It is an effective way to solve the icing problem by using the Joule heat generated by the DC in the conductor to melt the ice. In this paper, the multi-physics simulation software COMSOL is used to construct the finite element simulation model of the overhead contact system unit composed of a contact line, catenary wire and dropper. The model covers the physical processes such as convective heat transfer between conductor and air, heat conduction between overhead contact system and ice layer during ice melting, and considers the latent heat factor of ice melting. Under the condition of no icing, the actual data of several temperature points are measured under the applied current state of the overhead contact system, and the validity of the model is verified by comparing the simulated temperature data with the measured data. On this basis, the effects of ambient temperature, ice thickness and current on ice melting were studied using simulations. The results show that the ambient temperature has a significant effect on the ice-melting speed. Under 10 mm ice thickness and 2 m/s wind speed conditions, the time to start melting ice increases from 2 to 60 min until the ice cannot be melted as the ambient temperature decreases from −1 °C to −25 °C. Various initial conditions for ice thickness and wind speed were analyzed. Under the condition of no ice, the temperature rise of the contact wire and the catenary wire increases significantly with the current increase. When the current increases from 500 A to 2000 A, the temperature rise of the contact wire increases from 9.08–9.25 °C to 214.07–218.59 °C, and the temperature rise of the catenary wire increases from 6.88–7.01 °C to 173.43–177.13 °C. In addition, there is an optimal ice thickness range for the ice-melting process. When melting ice at −1 °C and −5 °C, the optimal ice thickness ranges are 4–8 mm and 1–4 mm, respectively. Full article

The spacecraft microgravity simulation air-bearing platform is a crucial component of the spacecraft ground testing system. Special disturbances, such as the flatness and roughness of the contact surface between the air bearings and the granite platform, increasingly affect the control accuracy of the simulation experiment as the number of air bearings increases. To address this issue, this paper develops a novel compensation control system based on Active Disturbance Rejection Control (ADRC), which estimates and compensates for the disturbing forces and moments caused by the roughness and levelness of the contact surface, thereby improving the control precision of the spacecraft ground simulation system. A dynamic model of the multi-air-bearing platform under disturbance is established. A cascade ADRC algorithm based on the Linear Extended State Observer (LESO) is designed. The Gauss–Newton iteration method is used to identify the parameters of the sliding friction coefficient and the tilt angle of the air-bearing platform. A full-physics simulation experimental platform for spacecraft with rotor-based propulsion is constructed, and the proposed algorithm is validated. The experimental results show that on a marble surface with a flatness of grade 00, an overall tilt angle of 0–1 degrees, and a surface friction coefficient of 0–0.01, the position control accuracy for the simulated spacecraft can reach 1.5 cm, and the attitude control accuracy can reach 1°. Under ideal conditions, the identification accuracy for the contact surface friction coefficient is 2 × 10⁻⁴, and the recognition accuracy for the overall levelness of the marble surface can reach 1 × 10⁻³, laying the foundation for high-precision ground simulation experiments of spacecraft in multi-air-bearing scenarios. Full article

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article

Aquifer Thermal Energy Storage (ATES) systems are a promising solution for sustainable energy storage, leveraging underground aquifers to store and retrieve thermal energy for heating and cooling. As the global energy sector faces rising energy demands, climate change, and the depletion of fossil fuels, transitioning to renewable energy sources is imperative. ATES systems contribute to these efforts by reducing greenhouse gas (GHG) emissions and improving energy efficiency. This review uses the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology as a systematic approach to collect and analyze relevant literature. It highlights trends, gaps, and advancements in ATES systems, focusing on simulation methods, environmental impacts, and economic feasibility. Tools like MODFLOW, FEFLOW, and COMSOL Multiphysics are emphasized for optimizing design and system performance. Europe is identified as a continent with the most favorable predispositions for ATES implementation due to its diverse and abundant aquifer systems, strong policy frameworks supporting renewable energy, and advancements in subsurface energy technologies. Full article

Gyroid-based mechanical metamaterials have garnered increasing attention for their unique mechanical properties, particularly in applications involving complex stress environments. This study focuses on the mechanical characterization of the gyroid cell, a member of the Triply Periodic Minimal Surfaces (TPMS) family, through both experimental and numerical analyses. Three different gyroid morphologies were generated by varying a single parameter in the parametric equation of the gyroid surface. Specimens were fabricated by 3D printing based on Liquid Crystal Display (LCD) technology, and compression tests were conducted to measure the equivalent Young’s modulus. Numerical models developed using Finite Element Method (FEM) analysis were validated through the experimental findings. The results indicate a good correlation between the experimental and numerical data, particularly in the linear elastic region, confirming the suitability of FEM simulations in predicting the mechanical response of these cellular structures. The study serves as a foundational step towards a broader multi-physical characterization of TPMS-based metamaterials and paves the way for the future development of tailored metamaterials for specific applications, including sacrificial limiters in plasma-facing components of Tokamaks. Full article

This study focuses on the effect of an induction heating device on the entry of a thin strip continuous casting and rolling line. A finite element model for the electromagnetic–thermal coupling of transverse magnetic flux induction heating was developed by adopting COMSOL software 6.1 to systematically investigate the effects of process parameters on the magnetic field, eddy current field, and the transverse temperature distribution of the strip. The results show that when the gap is between 20 mm and 40 mm, the maximum value of magnetic induction in the overheating region at the edges of the strip increases from 0.28 T to 0.35 T and 0.38. When the strip width is 1000 mm, there is an approximately 29% increase in magnetic induction in comparison to a strip with a width of 800 mm, and both eddy current density and temperature exhibit abnormal fluctuations. The maximum temperature difference in the temperature uniformity region at the center of the strip is only 3 °C at different frequencies, and the temperature-rise curves almost completely overlap. With increasing current, the temperature difference between the weak temperature region and the temperature uniformity region at the center widens, indicating a deterioration in temperature uniformity. Meanwhile, the field conditions are simulated using a simplified model of continuous heating. The results indicate that the maximum temperature deviation in the overheating region at the edges of the strip is 6 °C, while the deviation in the temperature uniformity region is 2 °C. Furthermore, the simulation data reveal an average temperature rise of 1156 °C across the width of the strip, with a deviation of 1.4 °C compared to the measured results, which verifies the validity of the proposed model. The analysis results provide a reference basis for designing transverse magnetic flux induction heating devices and optimizing process parameters. Full article

The reduction of noise and vibration is possible with passive, semi-active and active control strategies. Especially where self-adaptive control is required, it is necessary to evaluate the noise reduction potential before the control approach is applied to the real-world problem. This evaluation can be based on a virtual model that contains all relevant sub-systems, transfer paths and coupling effects on the one hand. On the other hand, the complexity of such a model has to be limited to focus on principal findings such as convergence speed, power consumption, and noise reduction potential. The present paper proposes a fully coupled electro-vibro-acoustic model for the evaluation of self-adaptive control strategies. This model consists of discrete electrical and mechanical networks that are applied to model the electro-acoustic behavior of noise and anti-noise sources. The acoustic field inside a duct, terminated by these electro-acoustic sources, is described by finite elements. The resulting multi-physical model is capable of describing all relevant coupling effects and enables an efficient evaluation of different control strategies such as the local control of sound pressure or active control of acoustic absorption. It is designed as a benchmark model for the benefit of the scientific community. Full article

This study investigates the design and optimization of a nanoparticle-based surface-enhanced Raman scattering (SERS) biosensing platform using COMSOL Multiphysics simulations. The primary goal is to enhance the sensitivity and specificity of SERS biosensors, which are crucial for the precise detection and quantification of biomolecules. The simulation study explores the use of gold and silver nanoparticles in various arrangements, including single, multiple, and periodic nanospheres. The effects of polarization and the phenomenon of local hotspot switching in trimer and tetramer nanosphere systems are analyzed. To validate the simulation results, a SERS biosensing platform is fabricated by self-assembling gold nanoparticles on a silicon substrate, with methylene blue used as the Raman probe molecule. The findings demonstrate the feasibility of optimizing SERS biochips through simulation, which can be extended to various nanostructures. This work contributes to the advancement of highly sensitive and specific SERS biosensors for diagnostic and analytical applications. Full article

As tunnel engineering advances towards greater depths, larger scales, and longer distances, high rock temperatures and tunnel thermal damage frequently occur, constituting some of the main geological hazards faced by transportation, water conservancy, and other tunnel engineering projects. In this study, we take the alternative option to the No.1 deep-buried water diversion tunnel of the “Diversion of Yellow River to Jining” project as the study area, combined with the geological data of the field investigation, to comprehensively consider the thermodynamic properties of the rocks along the tunnel and the logging data along the tunnel, and we use the least squares method to fit the temperature of the logging wells to obtain the thermal background parameters of the earth’s heat flow as well as other regional heat background parameters to obtain the thermal background parameters of the tunnel. The COMSOL Multiphysics software established a two-dimensional steady-state geothermal field numerical model to predict the rock temperature along the tunnel. The results show that the maximum temperature along the tunnel is 51.1 °C, the length of the tunnel with thermal damage class I is 45.9 km, the length of the tunnel with thermal damage class II is 18.4 km, the length of the tunnel with thermal damage class III is 8.0 km, and the length of the tunnel with thermal damage class IV is predicted to be 0.18 km. Compared with the on-site temperature measurement data, the model prediction error is within ±1.5 °C, which validates the accuracy of the model. This study adopts numerical simulation combined with field geological and logging data to provide a theoretical basis for tunnel heat hazard prevention. Meanwhile, it offers technical support for the design and construction of similar deep-buried high-temperature tunnels. Full article

The existing methods for transformer core-loosening fault diagnosis primarily focus on fundamental frequency analysis, neglecting higher-frequency components, which limits early detection accuracy. This study proposes a comprehensive approach integrating full-band vibration analysis, including high-order harmonics, to enhance diagnostic precision. A theoretical model coupling magnetostriction and thermodynamics was developed, combined with empirical mode decomposition (EMD) and Pearson’s correlation coefficient for fault characterization. A 10 kV transformer core vibration test platform was constructed, capturing signals under normal, partially loose, and completely loose states. The simulation results aligned with the experimental data, showing vibration accelerations of 0.01 m/s² (Phase A) and 0.023 m/s² (Phase B). A multi-physics coupling model incorporating Young’s modulus variations simulated core loosening, revealing increased high-frequency components (up to 1000 Hz) and vibration amplitudes (0.2757 m/s² for complete loosening). The joint EMD–Pearson method quantified fault severity, yielding correlation values of 0.0007 (normal), 0.0044 (partial loosening), and 0.0116 (complete loosening), demonstrating a clear positive correlation with fault progression. Experimental validation confirmed the model’s reliability, with the simulations matching the test results. This approach addresses the limitations of traditional methods by incorporating high-frequency analysis and multi-physics modeling, significantly improving early fault detection accuracy and providing a quantifiable diagnostic framework for transformer core health monitoring. Full article

The growing need for effective and sustainable solutions in humanitarian construction has prompted scholars and practitioners to explore technical approaches that address the challenges of natural disasters, health emergencies, armed conflicts and migratory flows. These solutions often encompass temporary shelters, durable shelters and multifunctional buildings designed to balance rapid deployment, cultural sensitivity and environmental sustainability. However, the assessment of sustainability in humanitarian construction remains insufficiently defined due to the complexities of crises, the variability of local materials and the impact of local climatic conditions. This study aims to bridge this gap by integrating Building Information Modeling (BIM) and simulation tools such as COMSOL Multiphysics 6.0 to study sustainable strategies for humanitarian housing. Using case studies aligned with IFRC, UNHCR and CRL (Red Cross of Luxembourg) family shelter standards, the research assessed a Climate and Local Skill-Centered Design (CLCD) by examining the performance of key design elements, including wall material emissivity and reflectance, natural lighting, and energy efficiency within the context of indoor thermal comfort. Simulation results revealed that wall finishing material reflectance significantly influences average daylight factors (D), with variations of 2% to 5% linked to lower reflectance values and changes in the window-to-floor ratio (WFR). Conversely, thermal comfort metrics indicated minimal variations in heat discomfort hours, maintaining indoor temperatures between 19 °C and 25 °C, consistent with ASHRAE Standard 55 thermal comfort criteria. This paper underscores the importance of integrating advanced IT tools and green local techniques and materials to optimize humanitarian housing for health, comfort and environmental performance, offering actionable insights for future humanitarian sustainable designs. Full article

Traditional fracture reduction relies heavily on the surgeon’s experience, which hinders the transmission of skills. This specialization bottleneck, coupled with the high demands on physical strength, significantly limits the efficiency of daily treatments in trauma orthopedics. Currently, most fracture surgery robots focus on open or minimally invasive reduction techniques, which inherently carry the risk of iatrogenic damage due to surgical incisions or bone pin insertions. However, research in closed reduction-oriented robotic systems is remarkably limited. Addressing this gap, our study introduces a novel bone-setting robot for the closed reduction of ankle fractures designed with a redundant parallel platform. The parallel robot’s design incorporates three sliding redundancy actuators that enhance its tilt flexibility while maintaining load performance. Moreover, a singularity-free redundant kinematic solver has been developed, optimizing the robot’s operational efficacy. Building upon the demonstrations from professional closed reduction techniques, we propose the use of a multivariate Student-t process as a multi-output regression model within dynamic movement primitive for accurately learning stable reduction maneuvers. Additionally, we develop an anthropomorphic variable impedance controller based on inverse dynamics. The simulation results demonstrate convincingly that the developed ankle bone-setting robot is proficient in effectively replicating and learning the nuanced closed reduction techniques. Full article

This study presents a simulation–optimization framework that integrates deficit irrigation strategies with ecological considerations to mitigate the impact of water abstraction on potential fish populations in river ecosystems. The framework addresses two primary objectives: minimizing fish population loss, an ecological index reflecting environmental impacts, and minimizing the yield reduction of rice crops caused by deficit irrigation. Regression models and adaptive neuro-fuzzy inference systems were employed to simulate the physical and water quality parameters of the river. Additionally, a multivariate linear regression model was developed to estimate potential fish populations using combined physical and water quality indices as inputs. Multi-objective particle swarm optimization was applied to achieve the defined objectives. Results from the case study demonstrate the model’s ability to balance ecological requirements with rice production through deficit irrigation. The ecological degradation of river ecosystems was found to be comparable during dry and normal years, while rice yield decreased by approximately 10% in dry years. Comparisons with unsustainable practices, where ecological flow was disregarded, revealed that significant reductions in rice production are inevitable to sustain river ecosystems. The proposed method provides a practical approach for achieving a fair balance between agricultural benefits and environmental sustainability in river basins, making it a valuable tool for water resource management. Full article