Feasibility Of Application Research Articles

Research on Mass Center Identification for Gravitational Wave Detection Spacecraft with Guaranteed Laser Link Pointing Accuracy

The deviation of the center of mass of a gravitational wave detection satellite from its calibrated position can severely impact the accuracy of gravitational wave detection. Therefore, there is an urgent need to develop effective identification methods to achieve precise center of mass identification while ensuring the alignment of the laser link. To address this issue, this paper proposes a method for the center of mass identification of gravitational wave detection spacecraft that ensures the pointing accuracy of the laser link. A study on the relative attitude dynamics modeling of gravitational wave detection spacecraft is conducted, and the conditions for small, periodic spacecraft maneuvers that maintain laser link alignment are analyzed. A high-precision, high-stability center of mass identification method based on dual-model data fusion is proposed and simulated. The results show that this method can maintain the alignment precision of the laser interferometer arms within 10 nrad, while achieving a center of mass identification accuracy of 25 μm. Compared to existing methods, this method improves the identification accuracy by an order of magnitude, demonstrating its feasibility for application in gravitational wave detection constellations. It provides theoretical support for the center of mass identification of gravitational wave detection spacecraft.

Structural modification of defective WO3 by g-C3N4 for photocatalytic gold recovery from non-cyanide-based plating effluent

A set of nCN/WO3 − x composites was synthesized through a simple thermal treatment for gold recovery from the simulated effluent of a non-cyanide-based plating bath. The obtained results exhibited that all nCN/WO3 − x composites demonstrated a higher photocatalytic activity for gold recovery than their pristine components due to the formation of nanocomposites which paved a convenient pathway for charge transfer. Among all synthesized composites, the 5.0CN/WO3 − x composite exhibited the highest photocatalytic activity, recovering around 69.4% of gold within 120 min under UV-vis light irradiation at an intensity of 3.45 mW/cm² and a catalyst loading of 1.0 g/L, in the absence of hole scavenger. This result can be ascribed to the presence of an optimal number of defects, which can act as electron trapping sites and thereby reduce the recombination rate of charge carriers. Gold recovery increased with repeated reuse, attributed to the decorated gold, which enhances light absorption and decreases the recombination rate of charge carriers. The feasible application of used CN/WO3 − x were also explored for H2 production, dye degradation and gold recovery. The obtained results provide a new insight about the photocatalytic recovery of gold from industrial wastewater and also shed light on the application of gold-decorated CN/WO3 − x as a catalyst for other photocatalytic applications.

A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study

ObjectiveTo develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.MethodsWe retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model’s diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios.ResultsIn total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model’s diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists’ sensitivity in diagnosing plaques. Additionally, the model’s diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p < 0.001).ConclusionThis AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.

Feasibility of substituting soda pulping with high consistency kneaded chemi mechanical pulping for discarded oyster farming bamboo scaffolding

In the previous study, discarded oyster farming bamboo scaffolding (BS) demonstrated the potential for application in pulping and papermaking through the soda pulping process. However, soda pulping involves high temperatures and chemical dosages. Therefore, this study develops an alternative pulping process to lower temperature and chemical demands by utilizing a high-consistency kneader (HCK), simultaneously promoting the utilization of BS in pulping and papermaking. This study applies a factorial experimental design to assess the impact of thermo-alkali pretreating conditions (temperature and NaOH dosage) by analyzing pulping yield, freeness, fiber appearance, and handsheet properties. The factorial analysis shows that the temperature and NaOH dosage of thermo-alkali pretreatment significantly affect the pulp properties. The optimal condition of 125℃ and 7.5% NaOH dosage has the highest accepts ratio, the best refining capacity, and the highest handsheet strength. Further compared with previous soda pulping research, utilizing HCK can produce similar pulp properties while increasing the pulping yield and reducing the temperature (-26.5%) and chemical demands (-46.4%). In summary, HCK demonstrates feasibility and potential for application in the pulping process, offering a new perspective on mechanical pulping as presented in this study, while also creating an opportunity to promote BS utilization.

A New Deep Learning-Based Method for Automated Identification of Thoracic Lymph Node Stations in Endobronchial Ultrasound (EBUS): A Proof-of-Concept Study

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a cornerstone in minimally invasive thoracic lymph node sampling. In lung cancer staging, precise assessment of lymph node position is crucial for clinical decision-making. This study aimed to demonstrate a new deep learning method to classify thoracic lymph nodes based on their anatomical location using EBUS images. Bronchoscopists labeled lymph node stations in real-time according to the Mountain Dressler nomenclature. EBUS images were then used to train and test a deep neural network (DNN) model, with intraoperative labels as ground truth. In total, 28,134 EBUS images were acquired from 56 patients. The model achieved an overall classification accuracy of 59.5 ± 5.2%. The highest precision, sensitivity, and F1 score were observed in station 4L, 77.6 ± 13.1%, 77.6 ± 15.4%, and 77.6 ± 15.4%, respectively. The lowest precision, sensitivity, and F1 score were observed in station 10L. The average processing and prediction time for a sequence of ten images was 0.65 ± 0.04 s, demonstrating the feasibility of real-time applications. In conclusion, the new DNN-based model could be used to classify lymph node stations from EBUS images. The method performance was promising with a potential for clinical use.

An L-cysteine based sensor for Cu2+ detection applicable for both environmental water and human plasma.

A flexible electrochemical sensor with high sensitivity and specificity is developed using gold nanoparticles (AuNPs) and a reduced graphene oxide/molybdenum disulfide (rGo-MoS2) composite modified screen printed carbon electrode (SPCE), with L-cysteine (L-Cys) as a probe for Cu2+ target recognition. Owing to the AuNPs/rGo-MoS2, the electron transference ability is improved by increasing the specific surface area of the working electrode, and a high sensitivity is achieved. Meanwhile, the bidentate chelation of L-Cys to Cu2+ contributes to a good selectivity. Using differential pulse voltammetry (DPV) for spiked standard Cu2+, the test results show a dynamic range from 0.1 μM to 100 μM, a detection limit of 0.020 μM, and a high sensitivity of 1.190 μA μM-1. Furthermore, detection in both environmental water and human plasma samples demonstrates a wide applicability of this sensor in various matrices, and an excellent feasibility for environmental and clinical applications.

Feasibility Analysis and Application of Integrating Group Reading into Middle School Mathematics Teaching—From the Perspective of Curriculum Creation

Feasibility Analysis and Application of Integrating Group Reading into Middle School Mathematics Teaching—From the Perspective of Curriculum Creation

Comprehensive performance of a new-type hybrid membrane bioreactor applied to mainstream anammox process.

The new-type submerged granular sludge membrane bioreactor (S-GSMBR) was constructed by installing a membrane module inside an upflow anaerobic sludge blanket. S-GSMBR achieved the fast start-up (47d) and long-term stable operation (133d) of mainstream Anammox process as well as the effective control of membrane fouling. The maximum nitrogen removal rate and efficiency were 0.52kgN m-3 d-1 and 88.32%. The flow field analysis indicated that the turbulent effects were found around the membrane module and benefited for alleviating the membrane fouling at the special axial flow velocity at the central section of the membrane module (4.89×10-7ms-1 to 1.30×10-6ms-1). On day 133, S-GSMBR achieved sludge granulation with 68.63% of particles larger than 200μm. Meanwhile, Candidatus Brocadia and Candidatus Kuenenia were typical Anammox bacteria in mature Anammox granular sludge. The relative abundance of Candidatus Brocadia and Candidatus Kuenenia was 21.22% and 0.36%, indicating that Candidatus Brocadia occupied the main niche under mainstream Anammox conditions. The feasibility of application of S-GSMBR in mainstream Anammox process was confirmed and moreover S-GSMBR was proven to be a robust mainstream Anammox reactor. This study lays the important theoretical foundation and technical support for the engineering application of the mainstream Anammox process.

Enhancing aerodynamic efficiency: shape modification of airfoils

Small wind turbines (SWTs) are essential for harnessing renewable energy, particularly in developing regions where energy demands are significant. They provide a sustainable solution to meet the critical energy needs of these areas. However, the low Reynolds number (Re) airflow characteristics can pose challenges, making optimising airfoil shapes essential for enhancing aerodynamic performance and energy capture. This study offers a thorough analysis of six different airfoil profiles within a Re range of 50k to 500k. Modifications to the thickness-to-camber ratio (t/c) were strategically targeted to improve aerodynamic efficiency and structural viability. Using QBLADE software, we evaluated these design alterations to optimise the lift coefficient ( C L ), drag coefficient ( C D ), stall angle of attack ( α stall ) and lift-to-drag coefficient ratio (L/D). The analysis revealed significant improvements in the maximum power coefficient ( C p . max ), with the modified BW-3 airfoil achieving a C p . max of 0.441 at a Re of 500k – a remarkable 40.5% increase over baseline values. This consistent enhancement across various Re emphasises the essential impact of aerodynamic design in enhancing energy capture and reinforcing the feasibility of SWT applications across diverse operating conditions, along with material savings that lead to lighter wind turbine (WT) blades.

A novel method for evaluating asphalt pavement structural performance based on vehicle vibration

ABSTRACT Fast, accurate, and high-frequency detection for evaluating pavement structural performance is vital for road maintenance decision-making. Traditional detection methods make it challenging to balance the detection speed, frequency, and cost. Pavement condition identification technology based on vehicle vibration has become increasingly mature and can overcome the above problems. This study aims to evaluate the structural performance of asphalt pavement. A vehicle-based test system based on two-axle vibration sensing was developed. The vibration signal from the vehicle driving in the asphalt pavement was generated. Further, the deep signal features was extracted based on the expression of the probability distribution. The evolutionary characteristics of probability density distribution and cumulative distribution were summarized. The main conclusions are as follows: The theoretical model analyzed the correlation between the probability density and the pavement modulus. Further, estimate the peak of the probability density (PKDE) and the cumulative distribution fitting value (CDF0). The estimated indexes’ correlation coefficients were calculated by matching the dynamic deflection index of 10 road sections. As a result, PKDE has a robust correlation with a correlation coefficient of 0.81. Therefore, the vehicle vibration probability distribution expression method proposed is effective for structural performance evaluation and has high application feasibility.

A comparison of the accuracy and feasibility of a low-cost mobile application versus higher-cost handheld 3D scanner for digital ear prosthetics.

Facial prosthetics are an important means to rehabilitate patients with congenital or acquired facial defects. However, with a time-consuming manual workflow and workforce shortage, access to facial prosthetics is limited in Australia and worldwide, especially for rural and remote patients. Optical 3D scanning has been increasingly integrated in digitizing data. With the development of TrueDepth® camera technology on smartphones, there is increasing availability of mobile applications which can generate 3D images to improve accessibility and reduce cost. This study compares the accuracy of mobile phone applications to high resolution 3D scanners for auricular data acquisition. We conducted a case-control study comparing the EM3D smartphone application (EM3D) with the EinScan Pro 2× Plus Shining 3D handheld scanner (EinScan) in 22 healthy participants equating to 44 ears, using CloudCompare software analysis. On average, EM3D acquired images 2.5 minutes quicker than the EinScan. The mean absolute directional distance difference was 1.10 mm, within the accepted deviation range of 2 mm. Out of the 44 ears, only 1 ear (2.27%) did not meet the accepted value of accuracy within 2 mm. The average completeness was 85% and the overall quality of images obtained from EinScanand EM3D were 53.5% and 57.7%, respectively, through observational analysis. Mobile iPhone applications such as EM3D are a viable alternative to 3D handheld scanners such as EinScan. This study demonstrates reliable results in accuracy, and improved results in time, cost and operational feasibility.

Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.

This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.

Research on the Application of Multi-rotor UAV Formation in the Hydrological Monitoring Field

In the context of the increasing demand for environmental monitoring, multi-rotor UAV formation has gradually become an important tool in hydrology monitoring. In this study, we propose a control algorithm for UAV formation utilizing an enhanced artificial potential field method. The obstacle avoidance problem in a complex environment for UAV formation is resolved by boosting the repulsive function and incorporating virtual target points, consequently strengthening the formation stability. At the same time, the consistency control algorithm is introduced to further optimize the speed oscillation problem during formation obstacle avoidance. Through MATLAB simulation, the proposed methodology demonstrates a markedly superior performance in the domains of path planning and obstacle avoidance when compared to conventional approaches, which proves its effectiveness and feasibility in practical application.

Virtual reality in surgery: minimizing stress and pain in patients undergoing minor-surgical procedures under local anesthesia-results of a feasibility study.

Virtual reality (VR) technology has gained significant importance in medical practice, particularly as an innovative approach to enhance patient experience and comfort. This prospective feasibility study investigates the impact of using VR headsets on stress and pain reduction during port surgeries under local anesthesia. In this prospective analysis, patients undergoing port implantation at the Klinikum rechts der Isar Technical University Munich were divided into two groups. The intervention group wore VR headsets during the surgical procedure, while the control group did not. Various validated questionnaires were used to measure psychological parameters such as pain perception, stress, calmness, and relaxation. The amount of local anesthesia administered was also documented. The study results showed that patients in the VR group required significantly less local anesthesia than those in the control group (p = 0.0025). Pain perception in the VR group was significantly lower (p = 0.028). Additionally, self-assessments regarding calmness, relaxation, and satisfaction were significantly improved in the VR group (p < 0.01). A stronger correlation between higher pain catastrophizing scale scores and anesthetic requirements was observed in the VR group, suggesting that VR may offer particular benefits for patients with increased pain sensitivity.Although gender differences were not statistically significant,a trend toward higher anesthetic requirements in male patients was noted. The evaluation of the VR headsets by patients was overwhelmingly positive, indicating high acceptance of the technology. The use of VR headsets during surgical procedures can significantly enhance patient comfort by reducing pain and stress levels. The high patient acceptance and positive evaluations of VR suggest its feasibility for broader clinical application. However, future studies should address potential cognitive biases, compare VR with other distraction methods, and explore its effects on different patient subgroups. Future research should also consider the role of gender-specific factors in the modulation of anesthetic requirements by VR. Additionally, a comprehensive cost-benefit analysis will be crucial for assessing the economic viability of VR technology in healthcare. VR technology represents a promising method to improve patient experience and comfort in surgical settings. The positive outcomes of this study encourage further research to fully capture and validate the potential of VR in medicine, particularly in pain management and stress reduction during various medical procedures.

Upcycling Salmon Skin Waste: Sustainable Bio-Sequins and Guanine Crystals for Eco-Friendly Textile Accessories

The significant environmental impact from fashion and textile industries has spurred interest in sustainable alternatives, especially for accessories like sequins and beads, whose usage has surged post-pandemic. This study explores the potential of utilizing salmon industry waste from Chile to produce bio-sequins (BS) and guanine crystals (GC) from salmon skin. The production of BS offers a strategy to reduce reliance on non-renewable resources and support sustainable waste management, as these materials decompose naturally without harmful residues. Physicochemical and mechanical characterization of the BS by using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), thermogravimetric analysis (TGA), X-ray powder diffraction (XRPD), and Fourier transform infrared spectroscopy (FT-IR), evaluated their feasibility for textile, design, and fashion applications. Additionally, GC were extracted from salmon scales using less hazardous solvents such as acetone, ethanol, and acetic acid, and subsequently immobilized on the BS for decorative purposes. Notably, tensile mechanical properties of the BS improved up to 75% after guanine decoration and exposure to simulated environmental factors like UV radiation. This work addresses the dual challenge of pollution and resource depletion, demonstrating that BS from salmon skin offer an eco-friendly alternative. It underscores the importance of adopting sustainable practices throughout the fashion industry’s production chain.

Super‐resolution imaging of urethral vasculature in female pigs: Validation of clinical feasibility and accuracy

AbstractThe efficacy and reliability of super‐resolution (SR) imaging for evaluating urethral vasculature (UV) in females remains uncertain. This study evaluates the super ultrasound for grand accuracy and resolution (SUGAR) method, an SR modality, for visualizing UV in female pigs within clinical ultrasound settings, aiming to establish its clinical feasibility and accuracy for potential human application. Female pigs (40–45 kg) were used to investigate UV blood flow dynamics, with data collected via a handheld ultrasound probe at 100 fps. The images were processed using SUGAR to achieve a resolution of &lt;30 µm and validated against computed tomography angiography (CTA) and histopathological analyses. SUGAR demonstrated superior capability in visualizing urethral blood flow compared to CTA, allowing for detailed multiparametric analysis of UV, including fractal dimension, vessel density, tortuosity, diameter, and blood flow velocity. Strong correlations between SUGAR imaging and histopathological findings underscore its potential clinical applicability for diagnosing and managing urological conditions in humans.

3D Heterogeneous Sensing System for Multimode Parrallel Signal No-Spatiotemporal Misalignment Recognition.

The spatiotemporal error caused by planar tiled structure design and the waste of communication resources brought on by the transmission of a single channel are two challenges facing the development of multifunctional intelligent sensors with high-density integration. A homo-spatiotemporal multisensory parallel transmission system (HMPTs) is expanded to realize multisignal no-spatiotemporal misalignment recognition and efficient parallel transmission. First, this system optimizes the distribution of multifunctional sensors, completes the 3D vertical heterogeneous layout of four sensors, and achieves material multi-information detection at a single place with no-spatiotemporal deviation. Additionally, the system couples and transmittes multiple sensory signals, delivering a fourfold increase in transmission efficiency and one-third of the power consumption compared to a single-channel transmission system. Finally, this system is used for the recognition of mixed materials, and human-computer interaction to realize the assignment of materials in VR, demonstrating the great accuracy and transmission efficiency of HMPTs as well as its feasibility in practical application. This is an a priori effort to enhance machine perception accuracy, improve signal transmission effectiveness, and advance human-machine-object triadic integration.

In Situ Regenerable Molecularly Imprinted Polymer Biosensor for Electrochemical Detection of Nonelectroactive Branched-Chain Amino Acids in Human Sweat.

The significant challenge in achieving in situ regeneration for conventional molecularly imprinted polymers (MIPs) restricts their promising application in continuous monitoring of biochemical molecules closely related to human health, especially nonelectroactive molecules. This is because they are either limited to a single use or require removal of imprinted templates through chemical washing steps, which is clearly impractical for sustainable monitoring. Here, a class of in situ regenerable MIP biosensors, taking nonelectroactive branched-chain amino acids (BCAAs) as templates and methyldopa as a functional monomer, was engineered to achieve repeatable in situ regeneration and in situ target recognition. The in situ regeneration was realized through an amperometric i-t technique with a negative voltage (-0.9 V) according to intrinsic isoelectric points of analytes instead of conventional wash steps. This electrochemical extraction process not only maximally repelled the imprinted templates, creating a large number of cavities (recognition sites) and significantly enhancing sensitivity, but also ensured the successful in situ regeneration of developed biosensing interfaces. The template extraction was evaluated by examining changes in the surface morphology, elemental composition, distribution, content, and interfacial properties. The developed BCAA MIP biosensors achieved sensitive target detection with the linear range from 0.001 to 10.0 μg/mL and limits of detection down to 0.45 (Leu), 0.47 (Ile), and 0.31 (Val) ng/mL. Beyond that, the biosensors demonstrated an excellent ability in decreasing biofouling, realizing repeatable in situ target detection in human sweat, and the obtained results were highly consistent with those of the enzyme-linked immunosorbent assay, indicating high feasibility, reliability, and accuracy in practical application. Meanwhile, the biosensors showed excellent specificity, selectivity, and stability.

Characterizing global work-conserving scheduling tardiness with uniform instances on multiprocessors

AbstractSoft real-time multiprocessor systems need scheduling policies introducing small overheads and for which it is possible to give guarantees on tardiness (i.e., the maximum delay that might arise with respect to job deadlines) in order to assess their feasibility in specific applications. For these reasons, lightweight policies such as Global Earliest Deadline First, and First-in First-out are preferred. Much effort has been spent in literature to provide efficiently computable tardiness bounds for periodic task systems scheduled on multiprocessors, but still, no exact bounds are known and results are given for specific classes of instances. In this paper, we use a work-conserving policy to schedule uniform instances, namely synchronous and periodic task systems in which tasks have the same period length and the same job length. We analytically derive a tight bound on the maximum tardiness and we give the exact length of the schedule hyper-period, showing that the latter can be computed in time linear in the number of processors. This result provides a lower bound to tardiness for the more general class of instances and is intended to close the gap with the upper bound from below.

Research on Fire Smoke Traceability Based on Emotional Intelligence Jaya Algorithm

To timely detect fire smoke in the early stages and trace the gas generated, thereby reducing the loss of human life and property and the damage to the ecological environment, this paper proposes a fire smoke tracing method based on the Emotional Intelligence Jaya algorithm (EIJaya). The algorithm assigns an anthropomorphic mental state to the Unmanned Aerial Vehicle (UAV) in the traceability task to realize its self-evaluation and social evaluation. In the simulation concentration field, the EIJaya algorithm, the basic Jaya algorithm, and the PSO algorithm were used for the simulation verification of gas traceability, and the simulation results proved the advantages of the EIJaya algorithm in terms of the success rate and the iteration times. In this paper, the TT UAV is chosen as an experimental tool to utilize the functions of its expansion module fully, and the experimental hardware system was constructed by combining it with the corresponding sensors. The corresponding experimental scene was built in the indoor environment, and the EIJaya algorithm was used to make multiple UAVs cooperate and carry out traceability experiments, which verified the algorithm's feasibility in practical applications and proved that the algorithm could quickly and accurately trace the fire smoke.