Balance Method Research Articles

Introduction of Local Resonators to a Nonlinear Metamaterial With Topological Features

Abstract Recent work in nonlinear topological metamaterials has revealed many useful properties such as amplitude dependent localized vibration modes and nonreciprocal wave propagation. However, thus far, there have not been any studies to include the use of local resonators in these systems. This work seeks to fill that gap through investigating a nonlinear quasi-periodic metamaterial with periodic local resonator attachments. We model a one-dimensional metamaterial lattice as a spring-mass chain with coupled local resonators. Quasi-periodic modulation in the nonlinear connecting springs is utilized to achieve topological features. For comparison, a similar system without local resonators is also modeled. Both analytical and numerical methods are used to study this system. The dispersion relation of the infinite chain of the proposed system is determined analytically through the perturbation method of multiple scales. This analytical solution is compared to the finite chain response, estimated using the method of harmonic balance and solved numerically. The resulting band structures and mode shapes are used to study the effects of quasi-periodic parameters and excitation amplitude on the system behavior both with and without the presence of local resonators. Specifically, the impact of local resonators on topological features such as edge modes is established, demonstrating the appearance of a trivial bandgap and multiple localized edge states for both main cells and local resonators. These results highlight the interplay between local resonance and nonlinearity in a topological metamaterial demonstrating for the first time the presence of an amplitude invariant bandgap alongside amplitude dependent topological bandgaps.

Fish oil replacement with different vegetable oils in Onychostoma macrolepis: Effects on fatty acid metabolism based on whole-body fatty acid balance method and genes expression.

To evaluate the fatty acid (FA) metabolism status and possibility as a DHA source of farmed Onychostoma macrolepis, a total of 168 fish (2.03 ± 0.23 g) were fed four diets supplemented with fish oil (FO), linseed oil (LO), soybean oil (SO), and a mixture of LO and SO oil (MO), respectively, for 70 days. Body FA compositions were modified reflecting dietary FAs. Comparing liver and intestine fatty acids with fish fed four diets, the content of ARA in fish fed SO was significantly higher than others (P < 0.05), but showed no difference in muscle. The tissue FA profile showed that the FO-fed group successfully deposited DHA, while the LO-fed group converted ALA to DHA effectively, as well as the liver and intestine EPA was notably highest in the FO group, whereas no difference between the FO and LO group in the muscle. The FA results showed that the DHA contents in the muscle of Onychostoma macrolepis are at a medium-high level compared with several other fish species with the highest aquaculture yield. Correspondingly, in the fish fed diet with LO, SO, and MO, the genes of most FA biosynthesis, transportation, and transcriptional regulation factors were increased in the liver and muscle, but no significant difference was observed in the gene expression of Elovl4b, FATP1, and FABP10 in the muscle. In addition, the enzyme activity involved in PUFA metabolism was higher in fish fed vegetable oil-based diets, corroborating the results of the gene expression. Increased in vivo elongase and desaturase (Δ5, Δ6, and Δ9) activities were recorded in fish fed fish oil-devoid diets, which resulted in the appearance of products associated with elongase and desaturase activities in fish. Besides, as the specific n-3 PUFA synthesis substrate, the dietary supplementation of ALA not only retains most of the nutrition value but also ensures the muscular texture, such as fiber diameter and density. It is concluded that farmed O. macrolepis owns strong n-3 LC-PUFA biosynthetic capacity and high DHA contents so it can be a good DHA source for the population.

A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

An intelligent assessment method of criminal psychological attribution based on unbalance data

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders’ attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance.

Distributed control of vehicle suspension system based on nonlinear energy sink

Vehicle riding comfort is deteriorated not only by low-order but also by high-order resonance in the suspension system. This paper proposes to arrange the nonlinear energy sink (NES) on the tire to suppress high-order resonance. Two modal resonances can be controlled by a vibration control scheme of the distributed NESs. A dynamic model of a 2-degree-of-freedom quarter-vehicle (2-DOFQV) suspension system integrated with two NESs is established. Then, the model is analyzed approximately analytically by the harmonic balance method (HBM) and verified numerically. Next, the vibration reduction mechanism of the new suspension system is revealed from the perspective of transferred energy. In addition, the vibration reduction effects of the parameters of two NESs are studied. The results of the study show that high-order modal resonance in the suspension system can be effectively controlled by placing an NES of small mass on the tire. Thus, the novelty of this paper is to propose a distributed control strategy for the vehicle suspension system, which can reduce the high-order resonance at the cost of a small mass.

Analysis of the Mathematical Models for Identifying the Thickness of the Fouling Layer in Natural Gas Coolers

This article presents an analysis of three different approaches to the identification of the thickness of the fouling layer inside the pipes of natural gas (NG) coolers. At present, there is no existing simple analytical procedure for the identification of the fouling layer thickness. The authors of this article describe in detail the balance method, which required the use of a large number of physical parameters, changes in their sizes depending on the output temperature of the gas, the temperature of the cooling air, the air quantity, as well as the physical properties of both media. The computational model was robust, and its disadvantage was the iterative computation. The second analysed method was a dimensional analysis. It was applied using the Buckingham’s theorem to express the individual similarity criteria. In this method, 10 simplexes and two complexes were created. The fouling layer thickness, expressed using a derived criterial equation, exhibited real results. The third analysed method was based on analysing selected physical parameters with the use of a multiple regression analysis in MinitabX 18 software. The analysis showed that the fouling layer thickness depended on fewer parameters than the number of parameters assumed in the dimensional analysis or the balance method. The standard deviation that was identified in the multiple linear regression for a double crossflow cooler was 0.0667 and the value of reliability (the coefficient of determination of the multiple linear regression) R2 was 0.9985.

Origami-inspire quasi-zero stiffness structure for flexible low-frequency vibration isolation

As an emerging technology, origami exhibits rich nonlinear static and dynamic properties. However, simulating creases remains a key challenge hindering practice application, especially for volumetric (deformable) origami. Inspired by the bistable behavior and axial-rotational coupling mechanism of triangulated cylindrical origami (TCO), this paper proposes a novel combined truss-spring structure with additional inertial mechanism (TCTS-RIM) for low-frequency vibration isolation. Unlike pure TCO, the TCTS-RIM uses elastic linkages to simulate all creases and modifies crease positions, enhancing physical realization in engineering applications and offering tunable stiffness and inertia on demand. Static analysis shows the modified truss-spring structure in TCTS-RIM has superior quasi-linear negative stiffness compared to pure TCO, which enables a very wide quasi-zero stiffness (QZS) range via linear spring compensation. The wide QZS range for different loads can be realized by setting appropriate structural parameters. From system dynamic equations, nonlinear inertia, nonlinear quadratic force, and nonlinear damping due to RIM are identified and discussed. The Alternating frequency–time harmonic balance method is used to obtain the displacement transmissibility, and its parametric influencing investigations verify that the proposed TCTS-RIM has excellent low-frequency vibration isolation performance and rich tunability. Comparative discussions show the wide QZS range significantly reduces the resonance frequency, broadens the isolation frequency band, and yields superior dynamic performance versus pure TCO isolators. Moreover, equivalent mass, anti-resonance and softening behavior induced by the rotational inertia mechanism further enhance low-frequency vibration isolation performance. An experimental prototype is built, and experimental results validate the correctness of the theoretical analyses. This work presents a simple mechanical implementation of volumetric origami, which may progress the engineering applications of origami and provide a viable approach to low-frequency vibration control.

Static characteristics of a biomimetic robot module driven by water hydraulic artificial muscles resisted with helical spring

Soft modular biomimetic robots, driven by flexible actuators, are extensively used in various fields due to their excellent flexibility, environmental adaptability, and isomorphism. However, existing flexible modules typically possess no more than two degrees of freedom for structural limitations. This paper proposes a biomimetic robot module supported by a spring and actuated by water hydraulic artificial muscles (WHAMs). Combining the mechanical properties of WHAMs and spring, the static model of the soft module was established by force balance method. Moreover, the contraction force coefficient and contraction ratio coefficient of WHAMs were identified through the static characteristics test. The rod length coefficient and stress coefficient of the spring were also identified through the spring bending test. The errors between the experimental results and the model are analyzed and discussed, uncovering the main reasons. The influence of spring pre-compression, spring elasticity coefficient, initial working pressure of WHAM, and the installation distance of WHAM on the motion space of module were analyzed and discussed. This work serves as a guide for the structural design of flexible modules to meet specific requirements and lays the foundation for future research on integrated flexible biomimetic robots driven by WHAMs.

Rational load balancing in collaborated cloud computing environments *

Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.

Dynamic balancing method of power distribution and consumption tasks based on state iterative prediction and resource peaking shifting

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the first-order difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation.

Dynamical analysis of a stay cable with a nonlinear energy sink and time-delayed feedback control

Stay cables exist commonly in engineering and can exhibit sustainable and large vibrations under external excitations. The unexpected oscillations always lead to the fatigue and damage of structures, resulting in the economical loss. This paper studies the vibration control of a stay cable system with a nonlinear vibration absorber and time delay control. The governing equations of motion of the stay cable system under harmonic excitations are given. The stability and different bifurcations of the trivial equilibrium point of the system are analyzed. The bifurcation diagrams with respect to the control parameters are illustrated. The frequency responses of the forced system are obtained by using the harmonic balance method. Numerical simulations are performed to validate the obtained results and various dynamical phenomena are shown, such as the multi-period oscillations, quasi-periodic responses and chaotic motions. The effects of the time-delayed feedback control on the nonlinear dynamics and vibration reduction of the system are extracted. It is shown that the proper values of the time delay and feedback gain can improve the vibration reduction performance of the system, such as the suppression of the resonance peaks and elimination of the complex responses.

Quantification of surface layer turbulence using sensible heat values from energy balance versus aerodynamic methods

Surface layer optical turbulence values in the form of the refractive index structure function C n 2 are often calculated from surface layer temperature, moisture, and wind characteristics and compared to measurements from sonic anemometers, differential temperature sensors, and imaging systems. A key derived component needed in the surface layer turbulence calculations is the sensible heat value. Typically, the sensible heat is calculated using the bulk aerodynamic method that assumes a certain surface roughness and a friction velocity that approximates the turbulence drag on temperature and moisture mixing from the change in the average surface layer vertical wind velocity. These assumptions/approximations generally only apply in free convection conditions. To obtain the sensible heat, a more robust method, which applies when free convection conditions are not occurring, is via an energy balance method such as the Bowen ratio method. The use of the Bowen ratio––the ratio of sensible heat flux to latent heat flux––allows a more direct assessment of the optical turbulence-driving surface layer sensible heat flux than do more traditional assessments of surface layer sensible heat flux. This study compares surface layer C n 2 values using sensible heat values from the bulk aerodynamic and energy balance methods to quantifications from sonic anemometers posted at different heights on a sensor tower. The research shows that the sensible heat obtained via the Bowen ratio method provides a simpler, more reliable, and more accurate way to calculate surface layer C n 2 values than what is required to make such calculations from bulk aerodynamic method-obtained sensible heat.

Theoretical and experimental study of a stable state adjustable nonlinear energy sink

Due to high sensitivity to the changes in external excitation intensity, the implementation of nonlinear energy sinks (NESs) have been greatly limited in practical applications. This study presents a stable state adjustable NES (SA-NES) structure. By adjusting the structural geometric relationship, the SA-NES structure can easily switch its stable state among monostable, bistable, and tristable. The governing equation of the coupled system is obtained by Hamilton's principle. The approximate analytical solution is obtained by the harmonic balance method, and the slow invariant manifold is obtained complexification-averaging method, which are validated through numerical analysis. Through theoretical analysis and experimental validation, the dynamic response of the SA-NES and vibration reduction performance are studied under different levels of excitations. The obtained results show that the designed SA-NES can effectively suppress the vibration under different level excitation intensities. It is also shown that the SA-NES structure can be adjusted to the monostable state, and the vibration of the linear oscillator can be effectively suppressed by the strong modulation response under a certain excitation intensity. When the excitation intensity is weak or strong, the SA-NES can be adjusted to the bistable state and the tristable state, respectively, thus effectively suppressing the vibration through chaotic inter-well oscillation. This study reveals the vibration suppression mechanism of the SA-NES, and introduces a new NES configuration to suppress different levels of engineering excitations.

Multi-Passage Harmonic Balance Method for Flutter Prediction in Multi-Row Configurations

A multi-passage harmonic balance method is presented for flutter prediction in multi-row configurations, and particular focus is on simulation of more than two blade rows. The unsteady flows in such configurations contain two kinds of multiple perturbations: one refers to those who have different frequencies and the other one refers to those who have the same frequency but different inter-blade phase angles (IBPAs). Most of current harmonic balance methods only consider the former one and the applications have been restricted to two-blade-row configurations. In the few studies that consider more rows, a one-to-one correspondence assumption between frequency and IBPA is usually made putting extra limitation on the used modes. This motivates our work, and a simple but effective idea is proposed by adding passages adopted per row. The theoretical basis of this work is provided by the spinning mode analysis. The present method is developed based on the time-spectral and multi-frequential form of harmonic balance. The treatment of pitchwise and inter-row boundary conditions in the case of multi passages are particularly addressed. A recently-developed Jacobian-free Newton-Krylov method with a simplified LU-SGS preconditioner is employed to solve the resultant harmonic balance equations. For validation, two typical multi-row flutter cases are investigated. The numerical results show the validity and accuracy of the developed method.

A nonlinear low frequency quasi zero stiffness vibration isolator using double-arc flexible beams

There is an increasing demand for integrated small vibration isolators in precision sensing instruments and medical equipment. Therefore, a quasi-zero stiffness (QZS) vibration isolator with double-arc flexible beams (D-AFB) is proposed. The QZS isolator consists of D-AFB units, the base, platform, fixture, guide rail, and linear bearing. The static performance of D-AFB is analyzed using the FEA method, and the QZS condition is obtained. The harmonic balance method (HBM) and the pseudo arc length method (PALM) are used to solve the dynamic equations of the nonlinear system, and the analytical solutions of displacement transmissibility and Force transmissibility are derived. In theory, the initial frequency is 77.6 % lower than that of the equivalent linear system. The QZS characteristic of D-AFB is verified by static experiment, and the low-frequency isolation performance of the QZS isolator is verified by dynamic experiment under constant amplitude and constant acceleration excitation. The D-AFB has a simple structure and can easily satisfy QZS conditions. So the D-AFB can be customized according to load requirements. Multiple vibration isolation loads can be obtained by adjusting the assembly of the D-AFB. The QZS isolator can adapt to load changes within 10 % and has an isolation effect on vibrations above 4 Hz. The idea of designing a QZS isolator with D-AFB as an array unit has a good research prospect and great application value in meeting the requirements of miniaturization and integration.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial

IntroductionHematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features. MethodsData from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models. ResultsAmong 1000 patients included in the ATACH-2 trial, 924 had the complete parameters which were included in the analytical cohort. The median [interquartile range (IQR)] initial hematoma volume was 9.93.mm3 [5.03–18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631–0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641–0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models. ConclusionWe developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.

MULTISENSORY METHODS AS TEACHERS' EFFORTS IN DEALING WITH DYSLEXIC CHILDREN IN ELEMENTARY SCHOOL: LITERATURE REVIEW

Dyslexic children of primary school age feel unable to keep up with learning, because they have different ways to learn. Teachers in school need to strategize a method for dyslexic children can follow the lesson well. This study aims to determine the efforts of teachers in dealing the dyslexic children with multisensory methods in elementary school. The research method used is integrative literature review with a five-stage framework, namely problem identification, literature search, data evaluation, data analysis, and presentation of findings. The electronic databases used are Sinta, ResearchGate, Google Scholar, Garuda, and Rama Repository, producing amount 38,649 articles over the past 10 years. Using search keywords, 7 journals were identified as meeting the inclusion criteria using the CASP (Critical Appraisal Skill Program) instrument. The result of this literature review is that multisensory methods are proven to improve the reading ability and learning quality of dyslexic children. Children experience an improvement in their ability to recognize words accurately although not significantly. Teachers need to provide remedial to overcome the difficulties and learning barriers they experience at school, and discuss with parents in order to children get a balanced teaching method between home and school.

Coprime Factor Model Reduction of Multidimensional Systems

In this work, the right coprime factor for balancing and truncating unstable systems is extended to parameter-varying multidimensional systems employing recent works on coprime factor model reduction of one-dimensional uncertain systems. Since the balanced truncation method cannot be applied directly to unstable systems, state feedback gains should be computed and incorporated in order to stabilize the given system and to be able to apply the balanced truncation technique via defining the so-called stable coprime factor, as coprime factorization overcomes the stability condition required for model reduction. Parameter-dependent state feedback and parameter-dependent Gramians are considered in this work yielding less conservative techniques. In addition, the Gramians are defined as block diagonal matrices, which are partitioned according to the structure of the multidimensional systems. The application to a simulation example demonstrates the applicability and validity of the proposed reduction approach leading to small error bounds between the full and the reduced models.

Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.

SMART: Spectral energy distributions Markov chain Analysis with Radiative Transfer models

Abstract In this paper we present the publicly available open-source spectral energy distribution (SED) fitting code SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models). Implementing a Bayesian Markov chain Monte Carlo (MCMC) method, SMART fits the ultraviolet to millimetre SEDs of galaxies exclusively with radiative transfer models that currently constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components. An important novelty of SMART is that, although it fits SEDs exclusively with radiative transfer models, it takes comparable time to popular energy balance methods to run. Here we describe the key features of SMART and test it by fitting the multi-wavelength SEDs of the 42 local ultraluminous infrared galaxies (ULIRGs) that constitute the HERschel Ultraluminous Infrared Galaxy Survey (HERUS) sample. The Spitzer spectroscopy data of the HERUS ULIRGs are included in the fitting at a spectral resolution, which is matched to that of the radiative transfer models. We also present other results that highlight the performance and versatility of SMART. SMART promises to be a useful tool for studying galaxy evolution in the James Webb Space Telescope (JWST) era. SMART is developed in Python and is available at https://github.com/ch-var/SMART.git.