Initial Point Research Articles

High-quality 3D Object Detection Based on Instance-aware Sampling

Most existing 3D object detection that is researched on point clouds uses random down-sampling algorithms, which preserve the primary shape and feature information of the point clouds and extract robust features of sampling point clouds to decrease the consumption of computing resources and improve processing inference efficiency. On the other hand, this method of sampling may disregard significant areas within the point clouds, thereby diminishing the precision of detecting comparatively minute entities such as pedestrians and cyclists. In this research, an instance perception-based object detection method is presented that adaptively chooses additional foreground points in response to the properties of smaller objects in the initial point clouds. In addition, it offers end-to-end training and addresses the issue that the typical hand-designed multi-scale grouping receptive fields do not accurately reflect the size of the item. To effectively enhance the recall rate of foreground points, we present an instance-aware algorithm, which relies on farthest point sampling. Specifically, we employ dynamic gating networks to attain instance perception. This approach enables sample candidates to encompass a larger number of foreground objects. Then we use an aggregation layer to efficiently capture robust contextual features with rich spatial information. Through experiments performed on the KITTI dataset, we substantiate the effectiveness of our IAS when compared with current advanced 3D object detection methods. We have achieved a notable enhancement, especially in identifying diminutive objects.

Effect of oxidation time on fracture behavior of C/SiC composites at 800 °C in air

The fracture behavior of C/SiC composites after oxidation treatment for different time at 800 °C in air was studied by in-situ test technology combining DIC, AE and X-CT. From results of fracture toughness tests, it can be found that with the increase of oxidation time, the stress intensity factor and the work of fracture show different trends, and the fracture mode after oxidation changes from quasi-brittle to ductile. Combined with AE clustering analysis and X-CT images obtained through in-situ load tests, the mechanism of the coupling effect of oxidation time, toughening behavior and C-phase consumption on energy dissipation is explained. In addition, long-term oxidation causes the crack initiation point to deviate from the prefabricated notch tip and the delamination damage, which may lead to the change of failure position and unexpected damage mode in the engineering structures.

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning.

Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings. To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy. A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02±2.60dB and the structure similarity index measure was 0.98±0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41±0.77mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1mm in all three orthogonal directions. A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

Remaining useful life (RUL) prediction of bearings is significantly important to ensure reliable operation of bearings. In practice, it is routinely impossible to obtain the full life cycle degradation data of bearings that needs to be used in prediction. The accuracy of the RUL prediction of bearings is often affected by incomplete degradation data. Regarding this situation, this paper proposes a multi-sensor three-stage RUL prediction framework based on the one-dimensional convolutional ordered neuron long short-term memory (1DCNN-ON-LSTM) neural network. Firstly, 1DCNN is used to extract spatial features adaptively from multi-sensor’s data and fuse them into one-dimensional feature. Next, the unsupervised hierarchy mechanism of time series information based ON-LSTM is developed to determine the ‘initial degradation stage point’ and ‘rapid degradation stage point’ of the bearing from the one-dimensional feature. Once the signal features collected by sensors input to the model reach the degradation stage point, select the corresponding sensitive features as input and construct the 1DCNN-ON-LSTM model that performs RUL prediction after the degradation stage point to improve the prediction accuracy of the model. Based on the proposed hierarchy mechanism, the bearings’ operation process is divided into three operation stages: normal stage, initial degradation stage and rapid degradation stage. Finally, the experiments verify that the proposed method can effectively divide the operation stages of bearings to predict the RUL and improve the generalization ability and prediction accuracy of the model.

An optimized synchronous approach to DC/DC droop backstepping control considering voltage compensation in DC microgrids

This paper addresses the impact of widely distributed power electronic loads and their constant power operations on the stability of DC microgrids. A backstepping controller, based on droop control, is proposed as a solution for mitigating the issues of constant power load in these grids. The unique feature of this controller is its independence from inter-device communication, which enhances the expandability and scalability of the DC microgrid. To tackle the challenge of power side converters needing a subsequent level DC/DC converter for voltage adjustment, a synchronous optimization method is introduced. This method, incorporating voltage compensation, keeps track of local output power during system transients. It identifies when the droop coefficient ratio deviates from proportionality, using this deviation as the initiation point for algorithm optimization. This approach modifies the local droop curve, enabling synchronous optimization. Before being optimized, the controller under heavy load conditions cannot meet the requirements of maintaining bus voltage and accurately allocating power simultaneously. However, after optimization, the system can maintain bus voltage while still ensuring accurate power allocation after disturbances. Moreover, the controller before optimization requires a secondary DC/DC converter to transfer electrical energy from the variable DC bus to the main bus. The optimized controller can effectively eliminate voltage offset and directly connect the DC/DC converter to the main bus, eliminating the cost problem caused by the secondary converter.

Three-Dimensional Surface Reconstruction from Point Clouds Using Euler’s Elastica Regularization

Euler’s elastica energy regularizer, initially employed in mathematical and physical systems, has recently garnered much attention in image processing and computer vision tasks. Due to the non-convexity, non-smoothness, and high order of its derivative, however, the term has yet to be effectively applied in 3D reconstruction. To this day, the industry is still searching for 3D reconstruction systems that are robust, accurate, efficient, and easy to use. While implicit surface reconstruction methods generally demonstrate superior robustness and flexibility, the traditional methods rely on initialization and can easily become trapped in local minima. Some low-order variational models are able to overcome these issues, but they still struggle with the reconstruction of object details. Euler’s elastica term, on the other hand, has been found to share the advantages of both the TV regularization term and the curvature regularization term. In this paper, we aim to address the problems of missing details and complex computation in implicit 3D reconstruction by efficiently using Euler’s elastica term. The main contributions of this article can be outlined in three aspects. Firstly, Euler’s elastica is introduced as a regularization term in 3D point cloud reconstruction. Secondly, a new dual algorithm is devised for the proposed model, significantly improving solution efficiency compared to the commonly used TV model. Lastly, numerical experiments conducted in 2D and 3D demonstrate the remarkable performance of Euler’s elastica in enhancing features of curved surfaces during point cloud reconstruction. The reconstructed point cloud surface adheres more closely to the initial point cloud surface when compared to the classical TV model. However, it is worth noting that Euler’s elastica exhibits a lesser capability in handling local extrema compared to the TV model.

Understanding Political Will and Why It Matters to Confront Corruption in Zimbabwe

When national programmes, development plans or policy issues are discussed, more often, their success, growth or failure is imputed on ‘political will’ or lack of it. The absence of political will is typically associated with underperformance in national programmes or strategies. It has become increasingly evident that domestic political realities influence the general attitude towards combating corruption and the continued shortcomings of political will as the traditional and initial point of focus. Despite being used to analyse or criticize the unsatisfactory outcome of government programmes and strategies, ‘political will’ has been poorly defined and understood. It remains unclear what political will actually mean, as the term has been widely misused as a catch-all concept. Assessments on political will are more often retrospective, focusing on failed national policies, plans and programmes. Nevertheless, the application of political will as a model illustrates a category of all action-based and orchestrated components that are observable, assessable and conducive to outward strengthening and consolidation. In other words, the application clarifies the extent to which political will can be attained. Considering the political realities in Zimbabwe, effective comprehending and combating corruption also necessitates a hands-on approach and sound understanding of the idea of ‘political will’. Zimbabwe is one of the countries experiencing rampant corruption in the world. Over the years, corruption has manifested itself through deteriorating socio-economic conditions across the country. Therefore, the main objective of this paper is to comprehend political will in order to confront corruption in Zimbabwe. The narrow approach to political will was preferred because it illuminates key issues, which inform the current Zimbabwean government’s failure to combat corruption.

MODIFIED HOUSEHOLDER METHOD OF FIFTH ORDER OF CONVERGENCE AND ITS DYNAMICS ON COMPLEX PLANE

In this paper, a modified Householder method of fifth order is proposed for solving nonlinear equations. The modification is done by adapting a cubic interpolation polynomial to approximate the second derivative in the Householder method. Weprovide a theorem to prove the order of convergence of the proposed method. The simulations reveal that the proposed method needs fewer iterations, even with challenging initial guesses, and excels in sending a large portion of initial points to convergence and exhibits rapid convergence.

Коммуникативная функция ольфакторных образов в романах П. Зюскинда «Парфюмер» и Т. Салиха «Сезон миграции на Север»

The article deals with the comparison of olfactory images in Patrick Suskind’s novel Perfume and Tayeb Salih’s The Season of Migration to the North in the communication context of Arab and European cultures. The initial theoretical point, based on the existing developments in the field of literary studies and cultural studies, is the idea of olfactory images as an essential component of the poetics of a work of art. The role played by olfactory images in revealing the characters of the two heroes Jean-Baptiste Grenouille and Mustafa Said is considered. Both heroes share similarities: on the one hand, they have a gift, and on the other, they are alienated from society: they feel lonely, their position in the world is that of an outcast. However, for Grenouille, this alienation is due to his personal features, while Mustafa Said’s psychological problems are connected with the colonial past of his homeland, Sudan. With the help of fragrances, both heroes are trying to restore the destroyed communication links – to evoke love to themselves and assert themselves. The article examines the relationship between olfactory images and the image of the homeland of both heroes. In the novel Perfume, the olfactory image of Paris is represented exclusively in a repulsive way. In the novel The Season of migration to the North, smells, by contrast, return the hero to his childhood, help him achieve harmony with himself and with nature. Olfactory images play an important role in revealing the problem of the protagonist’s communication with women. Grenouille artificially creates a fragrance that causes erotic temptation in the people around him. Salih’s novel also shows the connection between olfactory images and the motives of seduction, however, Mustafa Said does not create artificial smells, but uses natural smells to arouse the interest of his potential victims. The article concludes that the communicative functions of olfactory images in both novels are similar, but their differences are determined by the specific features of Arab and European culture.

A Reinforcement Learning Approach for Continuum Robot Control

Rigid joint manipulators are limited in their movement and degrees of freedom (DOF), while continuum robots possess a continuous backbone that allows for free movement and multiple DOF. Continuum robots move by bending over a section, taking inspiration from biological manipulators such as tentacles and trunks. This paper presents an implementation of a forward kinematics and velocity kinematics model to describe the planar continuum robot, along with the application of reinforcement learning (RL) as a control algorithm. In this paper, we have adopted the planar constant curvature representation for the forward kinematic modeling. This choice was made due to its straightforward implementation and its potential to fill the literature gap in the field RL-based control for planar continuum robots. The intended control mechanism is achieved through the use of Deep Deterministic Policy Gradient (DDPG), a RL algorithm that is suited for learning controls in continuous action spaces. After simulating the algorithm, it was observed that the planar continuum robot can autonomously move from any initial point to any desired goal point within the task space of the robot. By analyzing the results, we wanted to recommend a future direction for research in the field of continuum robot control, specifically in the application of RL algorithms. One potential area of focus could be the integration of sensory feedback, such as vision or force sensing, to improve the robot’s ability to navigate complex environments. Additionally, exploring the use of different RL algorithms, such as Proximal Policy Optimization (PPO) or Trust Region Policy Optimization (TRPO), could lead to further advancements in the field. Overall, this paper demonstrates the potential for RL-based control of continuum robots and highlights the importance of continued research in this area.

Impact of Emergency Department-Initiated Buprenorphine on Repeat Emergency Department Utilization.

Recent studies have demonstrated the promise of emergency department (ED)-initiated buprenorphine/naloxone (bup/nx) for improving 30-day retention in outpatient addiction care programs for patients with opioid use disorder (OUD). We investigated whether ED-initiated bup/nx for OUD also impacts repeat ED utilization. We performed a retrospective chart review of ED patients discharged with a primary diagnosis of OUD from July 2019-December 2020. Characteristics considered included age, gender, race, insurance status, domicile status, presence of comorbid Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnosis, presenting chief complaint, and provision of a bup/nx prescription and/or naloxone kit. Primary outcomes included repeat ED visit (opioid or non-opioid related) within 30 days, 90 days, and one year. Statistical analyses included bivariate comparison and Poisson regression. Of 169 participants, the majority were male (67.5%), White (82.8%), uninsured (72.2%), and in opioid withdrawal and/or requesting "detox" (75.7%). Ninety-one (53.8%) received ED-initiated bup/nx, which was independent of age, gender, race, insurance status, presence of comorbid DSM-5 diagnosis, or domicile status. Naloxone was more likely to be provided to patients who received bup/nx (97.8% vs 26.9%; P < 0.001), and bup/nx was more likely to be given to patients who presented with opioid withdrawal and/or requested "detox" (63.3% vs 36.7%; P < 0.001). Bup/nx provision was associated with decreased ED utilization for opioid-related visits at 30 days (P = 0.04). Homelessness and lack of insurance were associated with increased ED utilization for non-opioid-related visits at 90 days (P = 0.008 and P = 0.005, respectively), and again at one year for homelessness (P < 0.001). When controlling for age and domicile status, the adjusted incidence rate ratio for overall ED visits was 0.56 (95% confidence interval [CI] 0.33-0.96) at 30 days, 0.43 (95% CI 0.27-0.69) at 90 days, and 0.60 (95% CI 0.39-0.92) at one year, favoring bup/nx provision. Initiation of bup/nx in the ED setting was associated with decreased subsequent ED utilization. Socioeconomic factors, specifically health insurance and domicile status, significantly impacted non-opioid-related ED reuse. These findings demonstrate the ED's potential as an initiation point for bup/nx and highlight the importance of considering the social risk and social need for OUD patients.

A new method for predicting multi-crack fracture mechanism in the finite plate of red sandstone

A novel theoretical approach was put forth to forecast multi-crack propagation trajectories in a finite plate. First, a weighted residual approach and complex function are used to create the formula for computing the maximum (stress intensity factor) SIF of multi-crack in the finite plate. And then, three cracks were then chosen as a model for computation to determine how the plate size affects the SIF. The formula of multi-branch crack in finite plate is derived and the crack initiation and propagation criterion of multi-crack in finite plate was established. Finally, the propagation paths of three cracked red sandstone in a finite plate were predicted and verified by the uniaxial compression test. It demonstrates that the SIF of multiple cracks in a finite plate is consistently larger than that of an infinite plate. Under the uniaxial compression test, the first crack initiation point is often A2 or B2, and each specimen’s first crack initiation time increases and subsequently reduces when the relative horizontal spacing (Ds’) of cracks varies. The crack initiation stress increases and the unstable stress first increases and then decreases with Ds’. The total crack extension length initially goes down and then goes up. All of the fracture initiation and propagation mechanisms are Mode I. The effectiveness of the multi-crack propagation theory in finite plates is established by the good agreement between predicted and test findings.

A systematic narrative review of pathways into, desistance from, and risk factors of financial-economic cyber-enabled crime

Financial-economic cyber-enabled crime (hereinafter: financial cybercrime) has increased dramatically over the past years. However, research on financial cybercrime is still underdeveloped and highly heterogeneous, especially regarding the processes of initiation to and desistance from crime. This paper synthesizes existing knowledge on pathways into, desistance from, and risk factors related to financial cybercrime, and identifies research gaps. Adhering to PRISMA-ScR guidelines, we executed a systematic search and identified 37 eligible documents published as of February 2022, indicating two initiation points into financial cybercrime: involvement in traditional crime, and experiencing strain. Through social learning, individuals then learn the necessary skills and knowledge and engage in financial cybercrime, after which the decision to desist is influenced by a cost-benefit analysis, the use of neutralization techniques, and maturing. As for risk factors, we identified 33, with being male, unemployed, having low self-control and deviant peers, and wanting to earn money quickly being of potential importance. Regarding research gaps, there is a dearth of research related to the initiation and desistance processes of financial cybercrime, and the identified studies lacked a robust research designs, with 76 percent being of low or medium quality. More quality research is needed to address these issues.

Immune checkpoint inhibitors with chemotherapy for primary advanced mismatch repair-deficient endometrial cancer: A cost-effectiveness analysis

ObjectiveThe addition of immune checkpoint inhibitors (ICIs), pembrolizumab or dostarlimab, to paclitaxel and carboplatin (TC) has shown better response rates and survival outcomes for patients with primary advanced mismatch repair-deficient (MMRd) endometrial cancer (EC) in NRG-GY018 and RUBY, respectively. Nonetheless, the high cost of ICIs remains a major concern when implementing this strategy in the real world. This study aimed to determine the cost-effectiveness of pembrolizumab and dostarlimab with chemotherapy compared to TC for primary advanced MMRd EC. MethodsWe developed a Markov model including 6600 patients with primary advanced MMRd EC to simulate treatment outcomes. The initial decision points in the model were treatment with pembrolizumab with TC (PEM-TC), dostarlimab with TC (DOS-TC), and TC. Model probabilities, costs, and health utility values were derived with assumptions from published literature. Effectiveness was determined as average quality-adjusted life years (QALYs) gained. The primary outcome was the incremental cost-effectiveness ratio (ICER). ResultsTC was the least costly strategy, whereas PEM-TC was the most effective strategy for primary advanced MMRd EC. TC was cost-effective based on a willingness-to-pay (WTP) threshold of $100,000/QALY compared with PEM-TC (ICER, $377,718/QALY), and DOS-TC exhibited absolute dominance (ICER, $401,859/QALY). PEM-TC was cost-effective when the cost of pembrolizumab 200 mg was reduced to $4361 (61% reduction). PEM-TC was selected in 16.5% with a WTP threshold of $300,000/QALY, but in <1% with a WTP threshold range of $100,000-200,000/QALY. ConclusionPEM-TC can become cost-effective for primary advanced MMRd EC when the cost of pembrolizumab substantially decreases.

Natural history and comorbidities of generalised and partial lipodystrophy syndromes in Spain.

The rarity of lipodystrophies implies that they are not well-known, leading to delays in diagnosis/misdiagnosis. The aim of this study was to assess the natural course and comorbidities of generalised and partial lipodystrophy in Spain to contribute to their understanding. Thus, a total of 140 patients were evaluated (77.1% with partial lipodystrophy and 22.9% with generalised lipodystrophy). Clinical data were collected in a longitudinal setting with a median follow-up of 4.7 (0.5-17.6) years. Anthropometry and body composition studies were carried out and analytical parameters were also recorded. The estimated prevalence of all lipodystrophies in Spain, excluding Köbberling syndrome, was 2.78 cases/million. The onset of phenotype occurred during childhood in generalised lipodystrophy and during adolescence-adulthood in partial lipodystrophy, with the delay in diagnosis being considerable for both cohorts. There are specific clinical findings that should be highlighted as useful features to take into account when making the differential diagnosis of these disorders. Patients with generalised lipodystrophy were found to develop their first metabolic abnormalities sooner and a different lipid profile has also been observed. Mean time to death was 83.8 ± 2.5 years, being shorter among patients with generalised lipodystrophy. These results provide an initial point of comparison for ongoing prospective studies such as the ECLip Registry study.

Weld line structured light stripe center extraction approach based on data dimensionality reduction

In order to improve the accuracy and speed of extracting the center of the line structured light stripe when using the vision measurement technology to get the 3D information of the weld sample, this paper proposes a center extraction approach based on the idea of data dimensionality reduction. First, use the weighted gray centroid approach to get the initial center point of the light stripe. Then, build a 7×7 template centered on the pixel block where the initial point is located and performs singular value decomposition on the 49×3 matrix within the template to get the normal direction of the initial point. Finally, calculate the stripe's exact center points through second-order Taylor expansion and smooth repair by cubic spline interpolation of breakpoints. The comparison experimental results show that this approach can effectively reduce the influence of the noise outside the light stripe on the center extraction, reduce the extraction time while ensuring the measurement accuracy, which can lay a good foundation for the point cloud data processing and surface reconstruction.

An in-depth analysis of how strain impacts the electronic, optical, and output performance of the Ca3NI3 novel inorganic halide perovskite

The incredible optical, structural, and electronic behaviors of inorganic type perovskite compounds have recently attracted considerable interest in the area of solar innovation. This study thoroughly investigated how it affects both tensile and compressive strain on the physical, optical, as well as electronic behaviors that exist in the cubic inorganic Ca3NI3 perovskite using FP-DFT, or first-principles density functional theory. The planar structure of the unstrained Ca3NI3 molecule at the location revealed a direct bandgap of 1.077 eV/1.61 eV using the PBE/HSE technique. The bandgap of the Ca3NI3 perovskite was reduced to 0.827 eV by taking into account the impact of spin-orbit coupling (SOC). Additionally, the bandgap of the framework showed a tendency to decrease under compressive pressure (0.932 eV in −4% strain) and slightly increase under tensile strain (1.187 eV in +4 % strain). According to an analysis of the band characteristics, visible light can be significantly absorbed by the substance as shown by optical parameters like absorption coefficients, reflectivity, dielectric functions, and electron loss functions. The static dielectric constant, ε1(0) of Ca3NI3 is 6.96, the location of the initial critical point in, ɛ2(ω) is 1.07 eV, the energy range of the large absorption peak is 7.2–7.6 eV, peak location of loss function is 8.7–9.3 eV and reflectivity at 0 eV is 4.8. The Ca3NI3 dielectric constant spikes shifted to lower photon energy as a result of a redshift brought on by an increase in compressive strain. On the other hand, as tensile strain increased, the material showed a blue shift, resulting in an increase in photon energy. Finally, using the SCAPS-1D simulator, the photovoltaic (PV) performance of novel Ca3NI3 absorber-based cell architectures with SnS2 as the Electron Transport Layer (ETL) was thoroughly examined under varied compressive and tensile strain. The greatest power conversion efficiency (PCE) was found to be 31.35 % with JSC of 39.43 mA/cm2, FF of 85.47 %, VOC of 0.9301 V for maximum 4 % tensile strain. These findings suggest that the Ca3NI3 perovskite may be suitable for solar cell applications including energy production and light management in near future.

Optimal Fuzzy-FOPID, Fuzzy-PID Control Schemes for Trajectory Tracking of 3DOF Robot Manipulator

The present study explores the guidance of a robotic arm along a predefined path by implementing an optimal fuzzy fractional order PID controller-based control strategy. This method serves as a means to address the nonlinearity and unpredictability of the robotic manipulator, contingent upon the fuzzy logic controller's specifications and the employment of a clonal selection algorithm. The dynamic equation of the manipulator was considered as an initial point, followed by designing a fuzzy controller for this purpose. To validate the effectiveness of this approach, it was compared to other techniques, such as Fuzzy, Fuzzy-PID, and fuzzy-FOPID controllers, with PID and FOPID controller parameters optimized using clonal selection algorithms. Simulation results reveal that the fuzzy-FOPID variant outperformed other methods under varying load conditions and model uncertainties, using SIMULINK/MATLAB 2014a.

Design and Research of Heat Storage Enhancement by Innovative Wave Fin in a Hot Water–Oil-Displacement System

Energy storage technology provides a new direction for the utilization of renewable and sustainability energy. The objective of this study is to introduce a novel, wavy, longitudinal fin design, which aims to improve heat transfer in the melting process of a Latent Heat Thermal Energy Storage (LHTES) unit. The main goal is to mitigate the negative effects caused by the refractory zone at the end of the melting phase. A two-dimensional numerical model of LHTES unit is established by using the enthalpy porosity method and verified by experimental data. Through the quantitative comparison between the traditional rectangular fin and the innovative wave fin, the influence of wave fin on the heat transfer mechanism in the heat storage process is revealed. The results show that the average heat storage rate of five and six wave fins is 3.70% and 12.98% higher than that of conventional rectangular fins, respectively, and the average temperature response of six wave fins is 17.78% higher than that of conventional rectangular fins. The addition of the wave fin weakens the negative effect of the refractory zone, but prolongs the heating time of the initial melting point.

A fixed-time converging neurodynamic approach with time-varying coefficients for l1-minimization problem

In this paper, we propose a novel neurodynamic network to deal with l1-minimization problem. In the framework of the fixed-time converging neurodynamic network (FxNN), time-varying coefficients are introduced to design the time-varying fixed-time converging neurodynamic network (TFxNN). It is shown that the fixed-time stability of the proposed TFxNN via the Lyapunov stability conditions. It is further shown that the proposed TFxNN trajectories from any initial points quickly converge to the unique equilibrium solution in fixed-time. A distinctive feature of the proposed TFxNN is its flexibility to choose time-varying coefficients to accelerate convergence. Simulation results based on signal and image reconstruction are presented that the proposed neurodynamic network is feasible and effective.