Lack Of Universality Research Articles

Multi-Dimensional Fuzzy Clustering-Based Trajectory Initialization Algorithm for Infrared Weak Target Trajectories in Robust Clutter Environments

When conducting maneuver target tracking, trajectory initialization plays a crucial role in enhancing the accuracy of tracking algorithms. During maneuver target tracking, the accuracy of the tracking algorithm can be significantly improved through trajectory initialization. However, the traditional trajectory initialization algorithms face issues such as susceptibility to noise interference, lack of universality, and poor robustness in environments with high clutter levels. To address these issues, this study proposes a trajectory initialization algorithm based on multidimensional fuzzy clustering (MDF-clustering). The algorithm utilizes multidimensional feature information of the target, such as speed and irradiance, to determine point trajectory affiliation by assigning weights based on the clustering center of each feature type. Subsequently, it updates the clustering center and weight assignment using the new target features, ultimately deriving the correct trajectory through iterative processes. Experimental results demonstrate that the proposed method achieves an average stable initialization frame number of 3.12 frames, an average correct trajectory initialization rate of 99.59%, an average false trajectory occupancy rate of 0.04%, and an average missed batch rate of 0.06%. These results indicate improvements of at least 0.87 frames, 27.11%, 60.28%, and 6.48%, respectively, in terms of initialization rate, false trajectory rate, and missed batch rate, when compared to traditional methods. The algorithm enhances the accuracy and robustness of trajectory initialization in challenging environments characterized by solid clutter and target maneuvers, offering significant practical value for target tracking in complex scenarios.

An Investigation on a Comprehensive Calibration Technique to Determine the Discrete Elemental Characteristics of Unrotted Sheep Dung at Varying Water Concentrations

An experimental study was undertaken to investigate the problem of the substantial variability in water content in unrotted sheep dung, which leads to a lack of universality and practicality in calibrating its discrete element simulation parameters. The stacking angle was used as the response value in these experiments. The objective of this study was to establish precise simulation parameters for the composting process. A model for water content-stacking angle was established using the cylinder-lifting technique, resulting in a correlation value of 0.997. Utilizing the Hertz–Mindlin with JKR bonding model, three EDEM particle models were developed, each with distinct particle sizes, based on the particle size distribution of sheep dung. The JKR surface energy was determined using the Plackett–Burman test, the steepest-climbing test, and the Box–Behnken test using a set of 10 parameters. A subsequent study was conducted on the JKR surface energy, rolling friction factor, and static friction factor utilizing the Plackett–Burman test and Box–Behnken test. A parameter model for stacking angle–discrete elements was developed that achieved a p-value below 0.0001 and a relative inaccuracy of 3.46% or less. The regression model for the water content–discrete element parameter was derived by combining the water content–stacking angle model with the stacking angle–discrete element parameter model. Validation of this model was conducted using both the pumping plate technique and the hopper approach, resulting in a relative error of 4.89% or less. The findings demonstrate that the specific characteristics of sheep manure may be accurately anticipated by considering its water content. This approach offers a valid and universally applicable way of predicting the specific characteristics of sheep dung in the simulation of composting equipment.

A coupled multi-factor synergistic graph network model for traffic prediction

Abstract Since only the spatio-temporal structure itself is considered in the current traffic prediction process, and there is a lack of multi-factor information about the scene, which leads to problems such as lack of universality and authenticity in the spatio-temporal scene, we proposed a coupled multi-factor synergistic graph network (CM-SGN). For this network, this paper firstly adopts a fusion mechanism to fuse traffic features with coupled scene features, constructs a complex feature correlation matrix, and enhances the multi-feature fusion effect of spatio-temporal scenes; secondly, it introduces a spatio-temporal feature network constrained by an attention mechanism to ensure the spatio-temporal stability of the fused features; and lastly, it reduces the sensitivity of the scene-traffic interdependent targets by using the regression layer combinations of the loss function to improve the prediction generalization capability. Experiments were conducted on two real datasets. Experiments were conducted on two real datasets, and the results show that the model proposed in this paper meets the complex spatio-temporal scenarios in traffic prediction, effectively captures the intrinsic spatio-temporal dependencies, and has good stability and generalization ability.

A Multi-Scale Deep Learning Algorithm for Enhanced Forest Fire Danger Prediction Using Remote Sensing Images

Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, squeeze-excitation spatial multi-scale transformer learning (SESMTML), which is designed to extract multi-scale fire danger features from remote sensing images. SESMTML includes several key modules: the multi-scale deep feature extraction module (MSDFEM) captures global visual and multi-scale convolutional features, the multi-scale fire danger perception module (MFDPM) explores contextual relationships, the multi-scale information aggregation module (MIAM) aggregates correlations of multi-level fire danger features, and the fire danger level fusion module (FDLFM) integrates the contributions of global and multi-level features for predicting forest fire danger. Experimental results demonstrate the model’s significant superiority, achieving an accuracy of 83.18%, representing a 22.58% improvement over previous models and outperforming many widely used deep learning methods. Additionally, a detailed forest fire danger prediction map was generated using a test study area at the junction of the Miyun and Pinggu districts in Beijing, further confirming the model’s effectiveness. SESMTML shows strong potential for practical application in forest fire danger prediction and offers new insights for future research utilizing remote sensing images.

Physical description and model validation of welded joint fatigue damage evolution

AbstractFor welded structures, the concept of damage is highly abstract and difficult to describe, leading to a lack of physical foundation and universality for existing fatigue damage accumulation models. A new fatigue damage parameter definition based on remaining bearing capacity is proposed in this paper, which is constructed by the equivalent stress on the remaining bearing surface and the equivalent stress intensity factors along the crack front curve. Tension and three‐point bending fatigue tests under two‐level loading block sequences were conducted, and the real fatigue damage evolution processes were obtained by combining measured crack front information and FEA simulation. Five damage accumulation models were utilized to estimate the fatigue damage evolution processes of the two specimens. Results show that there are differences in describing the damage evolution processes and predicting the early fatigue lives. The Rege model is most consistent with the measured data, demonstrating its applicability and effectiveness.

Structural Design and Analysis of Portable Intelligent Wheelchair for Knee Rehabilitation

In order to address the issues of inconvenience, high medical costs, and lack of universality associated with traditional knee rehabilitation equipment, a portable intelligent wheelchair for knee rehabilitation was designed in this study. Based on the analysis of the knee joint's structure and rehabilitation mechanisms, an electric pushrod-driven rehabilitation institution was developed. A multi-functional module was designed with a modular approach, and the control of the wheelchair body and each functional module was implemented using an STM32 single-chip microcomputer. A three-dimensional model was established using SolidWorks software. In conjunction with Adams and Ansys simulation software, kinematic and static analyses were conducted on the knee joint rehabilitation institution and its core components. A prototype was constructed to verify the equipment's actual performance. According to the prototype testing, the actual range of motion for the knee joint swing rod is 15.1°~88.9°, the angular speed of the swing rod ranges from -7.9 to 8.1°/s, the angular acceleration of the swing rod varies from -4.2 to 1.6°/s², the thrust range of the electric pushrod is -82.6 to 153.1 N, and the maximum displacement of the load pedal is approximately 1.7 mm, with the leg support exhibiting a maximum deformation of about 1.5 mm. The intelligent knee joint rehabilitation wheelchair meets the designed functions and its actual performance aligns with the design criteria, thus validating the rationality and feasibility of the structural design.

A Single-Camera-Based Three-Dimensional Velocity Field Measurement Method for Granular Media in Mass Finishing with Deep Learning.

Surface treatment processes such as mass finishing play a crucial role in enhancing the quality of machined parts across industries. However, accurate measurement of the velocity field of granular media in mass finishing presents significant challenges. Existing measurement methods suffer from issues such as complex and expensive equipment, limited to single-point measurements, interference with the flow field, and lack of universality in different scenarios. This study addresses these issues by proposing a single-camera-based method with deep learning to measure the three-dimensional velocity field of granular flow. We constructed a complete measurement system and analyzed the accuracy and performance of the proposed method by comparing the measurement results with those of the traditional DIC algorithm. The results show that the proposed method is very accurate in measuring spatial displacement, with an average error of less than 0.07 mm and a calculation speed that is 1291.67% of the traditional DIC algorithm under the same conditions. Additionally, experiments in a bowl-type vibratory finishing machine demonstrate the feasibility of the proposed method in capturing the three-dimensional flow of granular media. This research not only proposed a novel method for three-dimensional reconstruction and velocity field measurement using a single-color camera, but also demonstrated a way to combine deep learning with traditional optical techniques. It is of great significance to introduce deep learning to improve traditional optical techniques and apply them to practical engineering measurements.

Experimental and prediction model of axial compressive responses of corroded RC cylinders strengthened with CFRP

Currently, the capacity of corroded RC cylinders has been proven to significantly improve after being strengthened with carbon fiber reinforced polymer (CFRP). However, existing experimental data remains insufficient, resulting in few models available for predicting the post-strengthening capacity of corroded cylinder. The current understanding of the mechanism by which corrosion affects the mechanical properties of CFRP strengthened cylinders is also not yet clear. Against this background, this study conducted monotonic axial compression tests on CFRP-strengthened corroded RC cylinders. The experimental results indicate that while the CFRP layer predominantly controls the specimen's load-bearing capacity, the reduction in CFRP rupture strain due to increased corrosion rates should not be overlooked. When the corrosion rate reaches 35 %, the experimentally measured average rupture strain was only 65 % of the theoretical value, marking a 30 % decrease compared to non-corroded specimens. Subsequently, a predictive model for peak stress and strain of CFRP-confined corroded RC cylinders was proposed, which considers the confinement mechanisms of stirrups and CFRP as well as the degradation of CFRP rupture strain. Leveraging the existing test data, a dataset comprising 80 CFRP-strengthened corroded RC cylinder specimens was established to calibrate the key parameters in the proposed prediction model. Comparative analysis with existing models indicates that the peak stress and strain prediction model proposed in this study demonstrated the highest forecasting accuracy (the average errors for peak stress and strain were 14 % and 32.6 %, respectively), while existing other predictive models based on limited datasets suffer from a lack of universality. The experimental data and research conclusions of this article can provide reference for engineering practice and the establishment of models in subsequent research.

Goldfish phenomics reveals commonalities and a lack of universality in the domestication process for ornamentation

Abstract Domestication process effects are manifold, affecting genotype and phenotype, and assumed to be universal in animals by part of the scientific community. While mammals and birds have been thoroughly investigated, from taming to intensive selective breeding, fish domestication remains comparatively unstudied. The most widely bred and traded ornamental fish species worldwide, the goldfish, underwent the effect of long-term artificial selection on differing skeletal and soft tissue modules through ornamental domestication. Here, we provide a global morphological analysis in this emblematic ornamental domesticated fish. We demonstrate that goldfish exhibit unique morphological innovations in whole-body, cranial, and sensory (Weberian ossicles and brain) anatomy compared to their evolutionary clade, highlighting a remarkable morphological disparity within a single species comparable to that of a macroevolutionary radiation. In goldfish, as in the case of dogs and pigeons in their respective evolutionary contexts, the most ornamented varieties are extremes in the occupied morphological space, emphasizing the power of artificial selection for nonadaptive traits. Using 21st century tools on a dataset comprising the 16 main goldfish breeds, 23 wild close relatives, and 39 cypriniform species, we show that Charles Darwin’s expressed wonder at the goldfish is justified. There is a commonality of overall pattern in the morphological differentiation of domesticated forms selected for ornamental purposes, but the singularity of goldfish occupation and extension within (phylo)morphospaces, speaks against a universality in the domestication process.

Mechanism of formation and evolution of bound states in the continuum of split-hole all-dielectric metasurface under asymmetric displacement perturbation

Bound states in the continuum (BICs) with ultra-high Q properties have attracted much attention for their perfect localization in the continuous spectral range coexisting with extended waves. In this study, breaking the traditional excitation form of structure breakage or excitation field asymmetry, a monolithic silicon nanodisk array with relative displacement generated by the complete splitting of square nanopores is proposed based on the unique electromagnetic properties of all-dielectric metamaterials. During the introduction of perturbations by asymmetric displacements of splitting holes, it is shown by numerical simulations that two BICs at different wavelengths can be realized. Combined with eigenmodes of group theory, the symmetric matching relationship between the symmetry-protected BICs and the free-space radiation during the evolution process is analytically demonstrated, and the formation mechanism and the evolution law of the BICs excited by this metasurface are deeply investigated. meanwhile, it also provides a theoretical basis for the polarization dependence of quasi-BICs excitation and the ultra-high Q factor expression of BICs. Furthermore, near-field distribution and multipole decomposition show that the field distribution and surface currents support the excitation of BIC-driven toroidal dipole and magnetic quadrupole dual modes. This study not only provides an effective reference for the stability of high-Q resonance wavelengths, but also solves the problem of the lack of universality in analyzing the resonance mechanism based on resonance phenomena, and provides solid theoretical support for the study of displacement-mediated BICs resonance excitation and evolution.

Sports Video Classification Method Based on Improved Deep Learning

Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance video quality. The model then uses a bifurcated neural network to extract detailed features, leading to a densely connected neural network with a specific activation function for categorizing videos. We tested our model on a High-Definition Sports Video Dataset covering over 20 sports and a low-resolution dataset. Our model outperformed established classifiers like DenseNet, VggNet, Inception v3, and ResNet-50. It achieved high precision (0.9718), accuracy (0.9804), F-score (0.9761), and recall (0.9723) on the high-resolution dataset, and significantly better precision (0.8725) on the low-resolution dataset. Correspondingly, the highest values on the matrix of four traditional models are: precision (0.9690), accuracy (0.9781), F-score (0.9670), recall (0.9681) on the high-resolution dataset, and precision (0.8627) on the low-resolution dataset. This demonstrates our model’s superior performance in sports video classification under various conditions, including rapid motion and low resolution. It marks a significant step forward in sports data analytics and content categorization.

СОВРЕМЕННЫЙ ПОДХОД К ОЦЕНКЕ УРОВНЯ ЭКОНОМИЧЕСКОЙ БЕЗОПАСНОСТИ РЕГИОНА

The economy of the region is a key link in the national economic system, and its sustainable development is achieved through increasing the level of economic security. The article aims to develop a methodological approach to assessing the level of economic security of the region. The objectives of the study are to improve and test a systemic assessment of the economic security of the region, taking into account the specific level of development of the main spheres of the economy in the contextof functional areas of activity. The article used economic and statistical methods, methods of comparison and analogies. The study systematizes and identifies the limitations of existing approaches to assessing the level of economic security. It also notes the lack of universality of the widely used index method, and the insufficiency of methods for correctly taking into account the dynamics of regional economic development. The paper proposes an improved approach assessing the total level of economic security of the region, identifying the functional areas of development and specifying the system of calculation indicators, establishing the feasibility of using standardization of the indicators used. Attention has also been focused on positioning the calculation results in the context of a scale of “threshold” values, and the need for a comprehensive assessment system related not only to the size of the gross regional product. The proposed methodological approach was tested using the example of federal subjects of the Ural Federal District, and it was established that there is a need to update measures to increase the level of their economic security. General practical recommendations are given to increase the level of regional development as the foundation for the region’s sustainability.

The influence of elongation-induced concentration fluctuations on segmental friction in polymer blends.

Recent experimental studies have revealed a lack of universality in the extensional behavior of linear polymers, which is not envisioned by classical molecular theories. These surprising findings, particularly the sharp contrast between polymer melts and solutions, have catalyzed the development of new theoretical ideas, including the concept of friction reduction in highly stretched polymer melts. By presenting evidence from rheology and small-angle neutron scattering, this work shows that deformation-induced demixing, which is due to the viscoelastic asymmetry in binary mixtures, contributes to the observed nonuniversality. In the case of polystyrene/oligostyrene blends, demixing increases the effective glass transition temperature of the long chain, leading to an apparent friction enhancement. On the other hand, the opposite case is found for the polystyrene/poly(α-methylstyrene) blend. These results highlight the important influence of deformation-induced concentration fluctuations on polymer segmental friction.

Design and Empirical Study of PSC Course Model Based on AI Program

With the development trends of reform and opening up, modernization of education, and globalization of the economy, the importance of teaching Mandarin proficiency test (PSC) has become increasingly significant. At the same time, the development of course models for Mandarin proficiency test systems assisted by computer networking, AI technology, and mobile applications has emerged. Existing offline and online course platforms suffer from issues such as unsuitability of form, poor interactivity, and lack of universality, which hinder the widespread implementation of PSC courses. In light of this, based on thorough investigation and research, this paper, taking the “Special Textbook for Mandarin Proficiency Test” as a blueprint, combined with Mandarin teaching theory, designed and developed a PSC course model based on AI programs. The learning effectiveness of this model was experimentally tested, and statistical research methods were used to compare and analyze the experimental data. The experimental data showed significant improvement, which was in line with the expected results.

“Experiential Learning” Is What Faculty Makes of It: Creating New Models of Understanding for Experiencing and Interpreting on Faculty-Led Short-Term Study Abroad Programs

Short-term faculty-led study abroad programs have gained in popularity amongst undergraduate students in the United States (IIE 2022). Yet, little research has investigated how educators on these programs perceive experiencing and interpreting, even though they constitute two key modules in experiential learning. Through semi-structured interviews with faculty at Generic University on how they conceive of experiencing and interpreting, the authors conclude that there is little commonality on those concepts. This divergence leads to different experiential learning study abroad programs. The authors place faculty interpreting and experiencing on a spectrum of understanding and demonstrate how that influences the pedagogical design of their study abroad program models. From the faculty members’ conceptions, the authors create two program models, which they have coined “Mobile Classroom Model” (MC) and “Home Base Model” (HB). The purpose of this article is to begin a conversation about how scholarship fails to address the lack of universality in the concept of experiential learning.

Data-driven prediction of the equivalent sand-grain roughness

Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness {k}_{s}. It is essential to estimate {k}_{s} for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict {k}_{s} in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict {k}_{s} based on the selected surface parameters from previous DNS, LES, and experimental results for flow behavior over various surface roughness. The PSO-BP model’s ability to predict {k}_{s} in the fully rough region is evaluated and compared with both the existing roughness correction formulas as well as the traditional BP model. An optimized polynomial function is also proposed to serve as a ‘white box’ for predicting {k}_{s}. It turns out that the PSO-BP method has better performance in the evaluation metrics compared to other methods, yielding a Mean Absolute Error (MAE) of 0.0390, a Mean Squared Error (MSE) of 0.0026 and a Mean Absolute Percentage Error (MAPE) of 28.12%. This novel approach for estimating {k}_{s} has practical applicability and holds promise for improving the precision and efficiency of calculations related to equivalent sand-grain roughness, and thus provides more accurate and effective solutions for CFD and other engineering applications.

Social media use and performance of small businesses: a customer-centric perspective

PurposeSocial media is widely regarded as a strategic resource to improve firm performance. However, there are mixed findings on how businesses can use social media for better performance. This study aims to propose and test a mechanism through which social media can influence firm performance.Design/methodology/approachA survey approach was used to collect data from 262 small businesses in South Africa. The data were analysed using structural equation modelling (SEM) to assess the hypothesised relationships.FindingsThe findings support the significant role of social media in fostering firm performance. It is observed that the use of social media influences firm performance through three key customer-centric constructs: the strength of customer–firm relationships, customer orientation and customer co-creation. Additionally, the relationship between the strength of customer–firm relationships and firm performance is moderated by customer co-creation.Originality/valueThe study provides new insights into the mechanism through which social media fosters firm performance. Due to a lack of universality in establishing the direct effect of social media use on firm performance, providing evidence of an indirect path becomes vital for advancing knowledge on social media use in business. As such, this study contributes to the literature on social media and entrepreneurship by demonstrating a novel mechanism through which social media influences firm performance.

Knowledge‐guided digital twin modeling method of generating hierarchical scenes for a high‐speed railway

AbstractChina's railway construction is rapidly transitioning toward integrated management of “stakeholders, management elements, and management processes”. Therefore, comprehensive and whole‐process digital twin scene modeling is urgently needed for intelligent railway construction. However, the requirements of three‐dimensional scenes in different stages vary hierarchically, resulting in a lack of construction semantics and limited universality in modeling. This article proposes a knowledge‐guided digital twin modeling method of hierarchical scenes for a high‐speed railway. We first build a knowledge graph of “knowledge‐model‐data” to achieve an accurate and hierarchical description of railway scenes. We then establish a parameter‐driven modeling method that integrates knowledge guidance and primitive combination to generate a display scene and a virtual design scene automatically. Third, we propose joint linkage and model growth methods for construction action modeling, which are used to generate a virtual construction scene. Finally, in response to the hierarchical scene‐generating requirements in different stages, we conduct intelligent modeling experiments for the entire design and construction process. The knowledge graph of the hierarchical semantic description mode significantly improves the flexibility and universality of the modeling method. The proposed modeling method for the entire process contributes to the rapid representation of design data, in‐depth design, visual exploration, and dynamic optimization of the construction process. This article provides a reliable digital twin modeling solution for the entire process to improve design and construction quality.

Strongly adhesive zwitterionic composite hydrogel paints for surgical sutures and blood-contacting devices.

Hydrogels show eminent advantages in biomedical and pharmaceutical fields. However, their application as coating materials for biomedical devices is limited by several key challenges, such as lack of universality, weak mechanical strength, and low adhesion to the substrate. Here we report versatile and tough adhesion composite hydrogel paints (CHPs), which consist of zwitterionic copolymers and microgels, both with reactive groups. The CHPs exhibit tunable rheology and thickness, hydrophilicity, biofouling resistance, durability, and convenient fabrication on metal, polymer, and inorganic surfaces with arbitrary shapes. As a proof-of-concept, the CHP-surgical sutures demonstrate exceptional lubrication, drug delivery, anti-infection, and anti-fibrous capsule properties. Moreover, the CHP-PVC tubing effectively prevents thrombus formation in vitro and ex vivo rabbit blood circulation without anticoagulants. This work provides valuable insights for enhancing and developing integrated hydrogel technologies for biomedical devices. STATEMENT OF SIGNIFICANCE: The combination of hydrogel and biomedical devices can enable numerous existing applications in medicine. In this study, inspired by the principle of microgel reinforcement in industrial paints, we propose a simple and versatile zwitterionic composite hydrogel paints (CHPs) strategy, which can be easily applied to diverse substrates with arbitrary shapes by covalent grafting between complementary groups by brush, dip, or spray. The CHPs integrated universality, tough adhesion, mechanical durability, and anti-biofouling properties because of their unique chemical composition and coating structure design. This strategy provides a simple and versatile route for surface modification of biomedical devices.

Pure-Attention-Based Multifunction Memristive Neuromorphic Circuit and System

The use of memristive neuromorphic circuit and system is a promising solution for next-generation Artificial Intelligence (AI) computing, as it offers possibilities that go beyond conventional GPU-based artificial neural network computing platforms. However, most of the existing memristive neuromorphic circuits and systems are designed for the specific networks, which is lack of universality and flexibility. Therefore, this paper proposes a universal memristive circuit and system framework for pure-attention-based transformer networks to implement multifunction applications on edge devices. Furthermore, the verification of image recognition and speech recognition was achieved by extending the size of the memristor crossbar array macros and reconfiguring the memristor weights without changing the memristive transformer circuit and framework. This paper not only provides a universal edge implementation framework for multifunction applications of the transformer, but also offers a low-power and promising solution for the application of pure-attention-based transformers on edge devices.