Symmetric Clusters Research Articles

Effect of cryogenic thermal cycling treatment on the mechanical properties of FINEMET amorphous nanocrystalline alloy

The brittleness of FINEMENT amorphous nanocrystalline alloys caused by annealing is an urgent problem to be solved because it limits the scope of their engineering application. We are herein to investigate the rejuvenation behavior of the as-annealed samples that not only are in a relaxed state but also the nanocrystals are precipitated in the amorphous matrix. The results show that the thermal temperature for cryogenic thermal cycling (CTC) treatment should not be larger than 483 °C in order to recover the enthalpy of relaxation. Further, the more suitable temperature for CTC treatment differs with the time that the as-annealed ribbon is employed. Meanwhile, the improvement of mechanical properties of the rejuvenated samples was examined with the help of hardness, flat plate bending, and nanoindentation experiments, and the results correspond well to the maximum of the relaxation enthalpy recovery. In addition, molecular dynamics simulations were used to visualize the changes in the local structure and found that the increased local five-fold symmetric clusters have increased the amorphousness of the amorphous nanocrystalline ribbon through CTC treatment.

Analytical Solutions of Symmetric Isotropic Spin Clusters Using Spin and Point Group Projectors

Spin models like the Heisenberg Hamiltonian effectively describe the interactions of open-shell transition-metal ions on a lattice and can account for various properties of magnetic solids and molecules. Numerical methods are usually required to find exact or approximate eigenstates, but for small clusters with spatial symmetry, analytical solutions exist, and a few Heisenberg systems have been solved in closed form. This paper presents a simple, generally applicable approach to analytically solve isotropic spin clusters, based on adapting the basis to both total spin and point group symmetry to factor the Hamiltonian matrix into sufficiently small blocks. We demonstrate applications to small rings and polyhedra, some of which are straightforward to solve by successive spin-coupling for Heisenberg terms only; additional interactions, such as biquadratic exchange or multi-center terms necessitate symmetry adaptation.

Non-resonant relaxation of rotating globular clusters

The long-term relaxation of rotating, spherically symmetric globular clusters is investigated through an extension of the orbit-averaged Chandrasekhar non-resonant formalism. A comparison is made with the long-term evolution of the distribution function in action space, measured from averages of sets of $N$-body simulations up to core collapse. The impact of rotation on in-plane relaxation is found to be weak. In addition, we observe a clear match between theoretical predictions and $N$-body measurements. For the class of rotating models considered, we find no strong gravo-gyro catastrophe accelerating core collapse. Both kinetic theory and simulations predict a reshuffling of orbital inclinations from overpopulated regions to underpopulated ones. This trend accelerates as the amount of rotation is increased. Yet, for orbits closer to the rotational plane, the non-resonant prediction does not reproduce numerical measurements. We argue that this mismatch stems from these orbits' coherent interactions, which are not captured by the non-resonant formalism that only addresses local deflections.

TDDFT and the x-ray absorption spectrum of liquid water: Finding the "best" functional.

We investigate the performance of time-dependent density functional theory (TDDFT) for reproducing high-level reference x-ray absorption spectra of liquid water and water clusters. For this, we apply the integrated absolute difference (IAD) metric, previously used for x-ray emission spectra of liquid water [T. Fransson and L. G. M. Pettersson, J. Chem. Theory Comput. 19, 7333-7342 (2023)], in order to investigate which exchange-correlation (xc) functionals yield TDDFT spectra in best agreement to reference, as well as to investigate the suitability of IAD for x-ray absorption spectroscopy spectrum calculations. Considering highly asymmetric and symmetric six-molecule clusters, it is seen that long-range corrected xc-functionals are required to yield good agreement with the reference coupled cluster (CC) and algebraic-diagrammatic construction spectra, with 100% asymptotic Hartree-Fock exchange resulting in the lowest IADs. The xc-functionals with best agreement to reference have been adopted for larger water clusters, yielding results in line with recently published CC theory, but which still show some discrepancies in the relative intensity of the features compared to experiment.

Characteristics of the Vasa Gene in Silurus asotus and Its Expression Response to Letrozole Treatment.

The identification and expression of germ cells are important for studying sex-related mechanisms in fish. The vasa gene, encoding an ATP-dependent RNA helicase, is recognized as a molecular marker of germ cells and plays a crucial role in germ cell development. Silurus asotus, an important freshwater economic fish species in China, shows significant sex dimorphism with the female growing faster than the male. However, the molecular mechanisms underlying these sex differences especially involving in the vasa gene in this fish remain poorly understood. In this work, the vasa gene sequence of S. asotus (named as Savasa) was obtained through RT-PCR and rapid amplification of cDNA end (RACE), and its expression in embryos and tissues was analyzed using qRT-PCR and an in situ hybridization method. Letrozole (LT) treatment on the larvae fish was also conducted to investigate its influence on the gene. The results revealed that the open reading frame (ORF) of Savasa was 1989 bp, encoding 662 amino acids. The SaVasa protein contains 10 conserved domains unique to the DEAD-box protein family, showing the highest sequence identity of 95.92% with that of Silurus meridionalis. In embryos, Savasa is highly expressed from the two-cell stage to the blastula stage in early embryos, with a gradually decreasing trend from the gastrula stage to the heart-beating stage. Furthermore, Savasa was initially detected at the end of the cleavage furrow during the two-cell stage, later condensing into four symmetrical cell clusters with embryonic development. At the gastrula stage, Savasa-positive cells increased and began to migrate towards the dorsal side of the embryo. In tissues, Savasa is predominantly expressed in the ovaries, with almost no or lower expression in other detected tissues. Moreover, Savasa was expressed in phase I-V oocytes in the ovaries, as well as in spermatogonia and spermatocytes in the testis, implying a specific expression pattern of germ cells. In addition, LT significantly upregulated the expression of Savasa in a concentration-dependent manner during the key gonadal differentiation period of the fish. Notably, at 120 dph after LT treatment, Savasa expression was the lowest in the testis and ovary of the high concentration group. Collectively, findings from gene structure, protein sequence, phylogenetic analysis, RNA expression patterns, and response to LT suggest that Savasa is maternally inherited with conserved features, serving as a potential marker gene for germ cells in S.asotus, and might participate in LT-induced early embryonic development and gonadal development processes of the fish. This would provide a basis for further research on the application of germ cell markers and the molecular mechanisms of sex differences in S. asotus.

Three-body encounters in black hole discs around a supermassive black hole

Context. Dynamical encounters of stellar-mass black holes (BHs) in a disc of compact objects around a supermassive BH (SMBH) can accelerate the formation and coalescence of BH binaries. It has been proposed that binary–single encounters among BHs in such discs can lead to an excess of highly eccentric BH mergers. However, previous studies have neglected how the disc velocity dispersion and the SMBH’s tidal field affect the three-body dynamics. Aims. We investigate the outcomes of binary–single encounters considering different values of the disc velocity dispersion, and examine the role of the SMBH’s tidal field. We then demonstrate how their inclusion affects the properties of merging BH binaries. Methods. We performed simulations of four-body encounters (i.e. with the SMBH as the fourth particle) using the highly accurate, regularised code TSUNAMI, which includes post-Newtonian corrections up to order 3.5PN. To isolate the effect of the SMBH’s tidal field, we compared these simulations with those of three-body encounters in isolation. Results. The disc velocity dispersion controls how orbits in the disc are aligned and circular, and determines the relative velocity of the binary–single pair before the encounter. As the velocity dispersion decreases, the eccentricity of post-encounter binaries transitions from thermal to superthermal, and binaries experience enhanced hardening. The transition between these two regimes happens at disc eccentricities and inclinations of order e ∼ i ∼ 10−4. These distinct regimes correspond to a disc dominated by random motions (e ∼ i ≳ 10−4) and one dominated by the Keplerian shear (e ∼ i ≲ 10−4). The effect of the SMBH’s tidal field depends on the velocity dispersion of the disc. When the velocity dispersion is low, the resulting binaries are less eccentric compared to isolated encounters. Conversely, binaries become less eccentric compared to isolated encounters at high velocity dispersion. This also affects the number of BH mergers. Conclusions. The inclusion of the SMBH’s tidal field and the disc velocity dispersion can significantly affect the number of GW mergers, and especially the number of highly eccentric inspirals. These can be up to ∼2 times higher at low velocity dispersion, and ∼12 times lower at high velocity dispersions. The spin–orbit alignment is influenced by the tidal field exclusively at high velocity dispersions, effectively inhibiting the formation of anti-aligned binary BHs. Nonetheless, encounters in random-motion-dominated discs around a SMBH are still more effective in producing GW mergers compared to those occurring in spherically symmetric nuclear star clusters without an SMBH.

Bound states in the continuum in clusters of acoustic and elastic resonators

This study investigates the localization of waves within highly symmetric clusters of scatterers, focusing on the resonances that emerge when resonators are strategically positioned along the perimeter of a circumference. Notably, the quality factor of these resonances exhibits a noteworthy enhancement with an increasing number of resonators. We demonstrate that, specifically for flexural waves, as the number of scatterers approaches infinity, a defining condition emerges for the formation of bound states in the continuum (BICs). Additionally, we present an analytical expression for the design of these BICs. The findings extend beyond flexural waves, being applicable to various types of classical waves. To validate our theoretical framework, we conduct an experimental study in acoustics, showcasing the realization of an acoustic BIC open resonator. This resonator is constructed by coupling polygonally arranged holes within a two-dimensional waveguide. Our proposed design enables the scanning of the acoustic field, facilitating the retrieval of mode shapes and the estimation of quality factors through the analysis of signal decay rates.

A symmetric low-rank subspace clustering method for cooperative spectrum sensing in complex environments

A new cooperative spectrum sensing (CSS) method based on low-rank symmetric subspace clustering is proposed to improve spectrum sensing performance in complex environments. To align with the evolution of cellular networks and leverage spatial observations, a CSS model with multiple primary users and antennas is considered. To address the asymmetric weighting issue of data pairs, a low-rank representation with a symmetric constraint is proposed that reduces the signal dimensions. Finally, features are extracted from the symmetric coefficient matrix and clustered using the K-means algorithm. Through simulation, the effectiveness of the novel algorithm is numerically validated through comparative experiments.

Fabrication of slag/CKD one-mix geopolymer cement reinforced by low-cost nano-particles, mechanical behavior and durability performance

CKD is a byproduct of the cement industry, and its accumulation in the surrounding represents one of many issues associated with this industry. In this study, CKD was utilized in the fabrication of one-mix geopolymer cement (GP) composite as an economical and environmental solution for disposal of this byproduct. The mechanical properties and durability behavior during various deterioration actions were inspected. The obtained findings demonstrated that, replacing slag by CKD in the fabricated GP could cause an elongation in the setting times and reduction in the compression strength of approximately 50%. However, GPs containing CKD offered an accepted resistance to irradiation by γ-rays and to firing action. Reinforcing the GPs with nano Fe3O4 (NF) or nano TiO2 (NT) accelerated the geopolymerization reaction and offered mechanical properties surprising the control mix, this was related to the micro-filling and catalytic actions of the NPs which supported the formation of symmetrical and organized clusters of CSHs and CASH gel as shown in SEM micrographs. The reinforcing mixes surpassing the control mix in the protection against intrusion of sulfate ions which they could retain about 92% of their strength after 4 months of exposure while the control mix retained 80%. Furthermore, they showed a superior resistance to the destructive effect of irradiation by high dose gamma rays up to 1500 kGy and they retained ~ 75% of their strength after irradiation while the control mix was kept at only 35%. The fabricated composites are recommended for usage in many applied construction fields.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists.

Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.

Controlling the symmetry of hexamonodentate 3d-transition metal complexes through symmetry propagation from high-symmetry Ti-Mo and Zr-Mo clusters via hydrogen-bonding interactions.

The symmetry of one of the simplest hexamonodentate complexes, [M(H2O)6]2+ (M = Fe, Co, Ni, and Zn), was controlled by tuning interactions in the second coordination sphere. Highly symmetric Ti-Mo or Zr-Mo cluster cations acted as symmetry templates, imposing a crystallographic trigonal coordination geometry in the hexamonodentate complexes through intermolecular hydrogen-bonding interactions. Magnetic measurements revealed that the ideal trigonal symmetry results in weak spin-orbit coupling for high-spin FeII complexes, despite the T-term ground state in the octahedral geometry. In contrast, high-spin CoII analogues with the T-term ground state exhibited strong spin-orbit coupling. DFT studies supported that a d6 FeII ion in the D3 symmetry has a 5A1 ground state while a d7 CoII in the same symmetry has a 4E ground state. Single-ion magnet behavior was observed in the CoII complexes. These results demonstrate that incorporating a diamagnetic, highly symmetric cluster enables precise symmetry control in single-ion magnets containing only monodentate ligands.

Transition Metal Behavior of Heavier Alkaline Earth Elements in Doped Monocyclic and Tubular Boron Clusters.

Quantum chemical calculations are carried out to design highly symmetric-doped boron clusters by employing the transition metal behavior of heavier alkaline earth (Ae = Ca, Sr, and Ba) metals. Following an electron counting rule, a set of monocyclic and tubular boron clusters capped by two heavier Ae metals were tested, which leads to the highly symmetric Ae2B8, Ae2B18, and Ae2B30 clusters as true minima on the potential energy surface having a monocyclic ring, two-ring tubular, and three-ring tubular boron motifs, respectively. Then, a thorough global minimum (GM) structural search reveals that a monocyclic B8 ring capped with two Ae atoms is indeed a GM for Ca2B8 and Ba2B8, while for Sr2B8 it is a low-lying isomer. Similarly, the present search also unambiguously shows the most stable isomers of Ae2B18 and Ae2B30 to be highly symmetric two- and three-ring tubular boron motifs, respectively, capped with two Ae atoms on each side of the tube. In these Ae-doped boron clusters, in addition to the electrostatic interactions, a substantial covalent interaction, specifically the bonding occurring between (n - 1)d orbitals of Ae and delocalized orbitals of boron motifs, provides the essential driving force behind their highly symmetrical structures and overall stability.

Monte Carlo-Simulated Annealing and Machine Learning-Based Funneled Approach for Finding the Global Minimum Structure of Molecular Clusters.

Understanding the physical underpinnings and geometry of molecular clusters is of great importance in many fields, ranging from studying the beginning of the universe to the formation of atmospheric particles. To this end, several approaches have been suggested, yet identifying the most stable cluster geometry (i.e., global potential energy minimum) remains a challenge, especially for highly symmetric clusters. Here, we suggest a new funneled Monte Carlo-based simulated annealing (SA) approach, which includes two key steps: generation of symmetrical clusters and classification of the clusters according to their geometry using machine learning (MCSA-ML). We demonstrate the merits of the MCSA-ML method in comparison to other approaches on several Lennard-Jones (LJ) clusters and four molecular clusters-Ser8(Cl-)2, H+(H2O)6, Ag+(CO2)8, and Bet4Cl-. For the latter of these clusters, the correct structure is unknown, and hence, we compare the experimental and simulated fragmentation patterns, and the fragmentation of the proposed global minimum matches experiments closely. Additionally, based on the fragmentation of the predicted betaine cluster, we were able to identify hitherto unknown neutral fragmentation channels. In comparison to results obtained with other methods, we demonstrated a superior ability of MCSA-ML to predict clusters with high symmetry and similar abilities to predict clusters with asymmetrical structures.

Toroidal dipole Fano resonances driven by bound states in the continuum of lithium niobate metasurface for efficient electro-optic modulation

Free-space optical applications benefit greatly from actively reconfigurable metasurfaces. While static metasurfaces have demonstrated remarkable capabilities in manipulating optical wave characteristics, their fixed electromagnetic response faces a challenge for active device demands. This work presents an all-dielectric metasurface of lithium niobate with high Q-factor Fano resonances for enhanced electro-optic modulation. The resonance mechanism is investigated through multipole decomposition and near-field analysis, revealing that toroidal dipole contributions are dominant in all found three resonances. The strongest resonance arises from symmetry-protected bound states in the continuum (SP-BIC), and it is excited in an asymmetric metasurface by introducing a perturbation in the symmetric nanohole clusters, to enable polarization-independent operation. We numerically achieve a tuning efficiency of 0.65 nm/V and π-phase retardation driven by a bias of 0.53 V, due to the strong field confinement of quasi-BIC mode. Our work provides a scheme for compact, high-performance, and electrically tunable metasurfaces that can help in the development of spatial optical switches as well as tunable micro-lenses and displays.

Multi-Site Identification and Generalization of Clusters of Walking Behaviors in Individuals With Chronic Stroke and Neurotypical Controls

Background Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. Objectives We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: (1) identify clusters of walking behaviors in people post-stroke and neurotypical controls and (2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. Methods We gathered data from 81 post-stroke participants across 4 research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. Results We identified 4 stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. Conclusions Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

Observations on the embryonic development of the mud crab, Scylla paramamosain

To investigate the embryonic development of the mud crab Scylla paramamosain, we analyzed three critical parameters: egg color of, embryo morphology (through conventional and laser scanning confocal microscopy), and the distribution of cell divisions. During embryonic development, the egg color exhibited a progressive transition, shifting from orange to reddish-orange, then to brown, before ultimately darkening to black. Each embryo displayed a spherical shape, measuring approximately 280 μm in diameter, characterized by a smooth surface devoid of any depressions. The embryonic cell division was in the form of mixed oogenesis, comprised of complete division in the early stage, spiral oogenesis in the middle stage and surface division in the late stage. It is noteworthy that the blastopore appeared at the position where the transparent area and cell aggregation just appeared under the microscope, and the blastomere was a characteristic of the embryo entering the gastrulation stage. After entering the gastrulation stage, the cells aggregated towards the blastopore and formed two symmetrical cell clusters, which formed a V-shape with the void of the classic blastopore. When the transparent region occupied approximately 1/5 of the embryo’s volume, the embryo entered the nauplius stage, and the thoracic and abdominal armor, as well as the optic lobe and abdominal limb primordia, could be clearly distinguished. The appearance of the compound eye pigment band indicated the stage of compound eye pigment formation. At this time, the transparent area accounted for 1/4 of the embryo and a large number of ganglia appeared. The change of the compound eye pigment band from red to black was also one of the reasons for the blackening of the egg color of the crabs. The data obtained through this study have potential applications in the determination of embryonic development status and obtaining of high-quality seeds for S. paramamosain culture.

Symmetric carbon tetramers forming spin qubits in hexagonal boron nitride

Point defect quantum bits in semiconductors have the potential to revolutionize sensing at atomic scales. Currently, vacancy-related defects are at the forefront of high spatial resolution and low-dimensional sensing. On the other hand, it is expected that impurity-related defect structures may give rise to new features that could further advance quantum sensing in low dimensions. Here, we study the symmetric carbon tetramer clusters in hexagonal boron nitride and propose them as spin qubits for sensing. We utilize periodic-DFT and quantum chemistry approaches to reliably and accurately predict the electronic, optical, and spin properties of the studied defect. We show that the nitrogen-centered symmetric carbon tetramer gives rise to spin state-dependent optical signals with strain-sensitive intersystem crossing rates. Furthermore, the weak hyperfine coupling of the defect to their spin environments results in a reduced electron spin resonance linewidth that can enhance sensitivity.

Numerical modelling of the lobes of radio galaxies – Paper V: universal pressure profile cluster atmospheres

ABSTRACT We present relativistic magnetohydrodynamic modelling of jets running into hydrostatic, spherically symmetric cluster atmospheres. For the first time in a numerical simulation, we present model cluster atmospheres based upon the universal pressure profile (UPP), incorporating a temperature profile for a ‘typical’ self-similar atmosphere described by only one parameter – M500. We explore a comprehensive range of realistic atmospheres and jet powers and derive dynamic, energetic, and polarimetric data which provide insight into what we should expect of future high-resolution studies of AGN outflows. From the simulated synchrotron emission maps which include Doppler beaming we find sidedness distributions that agree well with observations. We replicated a number of findings from our previous work, such as higher power jets inflating larger aspect-ratio lobes, and the cluster environment impacting the distribution of energy between the lobe and shocked regions. Comparing UPP and β-profiles we find that the cluster model chosen results in a different morphology for the resultant lobes with the UPP more able to clear lobe material from the core; and that these different atmospheres influence the ratio between the various forms of energy in the fully developed lobes. This work also highlights the key role played by Kelvin–Helmholtz instabilities in the formation of realistic lobe aspect ratios. Our simulations point to the need for additional lobe-widening mechanisms at high jet powers, for example jet precession. Given that the UPP is our most representative general cluster atmosphere, these numerical simulations represent the most realistic models yet for spherically symmetric atmospheres.

A new intelligent modelling two-stage of hybrid fuzzy prediction approach by using computation software

With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model’s predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer.