Domain Parameters Research Articles

Nonlinear effect on stable state and snap-through bistability of square composite laminate

Bistable structures with the elastic instability caused by buckling are confirmed to perform significantly in micro-electromechanical systems, metamaterials, energy harvester, vibration isolation and morphing structures. The target of this paper is to explore the dynamic responses of cross-ply bistable composite laminate, focusing on the nonlinear effect of the single- and double-well vibration. Both theoretical and finite element (FE) methods are employed to simulate the nonlinear vibration and dynamic snap-through of bistable composite laminate under the foundation excitation at the center. In the theoretical model, the governing equations are established via Lagrange's equation based on the first-order shear deformation theory, von Karman nonlinear strain-displacement relation and Rayleigh-Ritz method. The fourth-order Runge-Kutta method is adopted to solve the governing equations, and the numerical results are validated by FE model. Subsequently, the details of the dynamic responses are analyzed to identify the nonlinear effects in the form of bifurcation diagram, phase portrait, time history, Poincare maps and amplitude spectrum. The dynamic responses are examined for a series of excitation parameters in both time and frequency domain. Through fixed frequency and frequency sweep tests, the nonlinear phenomenon of the single- and double-well vibrations are analyzed including superharmonic resonance, stiffness softening, hysteresis phenomenon, various periodic and chaotic vibration. The diverse responses to external inputs contribute significantly to efficiently predicting mechanical behaviors in real-world conditions, thereby offering indispensable theoretical support for structural design applications.

Exploiting locality in sparse polynomial approximation of parametric elliptic PDEs and application to parameterized domains

This work studies how the choice of the representation for parametric, spatially distributed inputs to elliptic partial differential equations (PDEs) affects the efficiency of a polynomial surrogate, based on Taylor expansion, for the parameter-to-solution map. In particular, we show potential advantages of representations using functions with localized supports. As model problem, we consider the steady-state diffusion equation, where the diffusion coefficient and right-hand side depend smoothly but potentially in a highly nonlinear way on a parameter y ∈ [−1, 1]N. Following previous work for affine parameter dependence and for the lognormal case, we use pointwise instead of norm-wise bounds to prove ℓp-summability of the Taylor coefficients of the solution. As application, we consider surrogates for solutions to elliptic PDEs on parametric domains. Using a mapping to a nominal configuration, this case fits in the general framework, and higher convergence rates can be attained when modeling the parametric boundary via spatially localized functions. The theoretical results are supported by numerical experiments for the parametric domain problem, illustrating the efficiency of the proposed approach and providing further insight on numerical aspects. Although the methods and ideas are carried out for the steady-state diffusion equation, they extend easily to other elliptic and parabolic PDEs.

Resting Heart Rate Variability is Independently Associated with Visceral Fat Rating Scores in Saudi Adult Males.

Visceral adipose tissue (VAT) may be a specific modifiable contributor to body composition-related autonomic impairment. To compare heart rate variability (HRV) between groups stratified by visceral fat rating (VFR) and compare associations between HRV and body composition metrics. A cross-sectional study was conducted on healthy men (n=99,age=37.8±13.4 years, body mass index [BMI]=26.9±4.6 kg/m2). HRV was derived from 5-minute electrocardiographic recordings. Body composition (body fat percentage, VFR, and muscle mass to visceral fat ratio [MMVFR]) was estimated using tetrapolar bioelectrical impedance analysis. Participants were categorized into groups according to VFR: G1 (VFR=1-8); G2(VFR=9-12); and G3(VFR>12). Age-adjusted comparisons were made between groups. Independent associations were quantified with multiple linear regressions. P <0.05 was significant. Root-mean square of successive differences (RMSSD) and standard deviation of normal RR intervals (SDNN) were higher for G1 vs. G2 and G3 (p<0.05). Low-frequency power (LF) was higher in G1 than in G2 (p<0.05). VFR and MMVFR were negatively associated with SDNN, RMSSD, LF, and HF (p<0.05). After adjusting for age, BMI, and systolic and diastolic blood pressure, VFR was significantly predictive of RMSSD, SDNN, and HF (p=0.002,-0.027), and MMVFR was significantly predictive of RMSSD and SDNN (p=0.020,-0.023). Men in the lowest VFR category had the highest HRV. VFR was more strongly associated with HRV than body fat percentage and MMVR. Time domain parameters were more sensitive to VAT than frequency domain parameters. HRV parameters could be the primary parameters of interest in tracking cardiac-autonomic status in response to interventions targeting VAT reduction.

Twin experiments and detailed investigation of data assimilation system for columnar dendrite growth in thin film

Accurately predicting dendrite growth during alloy solidification is crucial for enhancing the quality of metallic products. Recently, data assimilation has emerged as a promising tool for integrating two cutting-edge methods for studying dendritic growth, viz. in situ X-ray observation experiments and phase-field (PF) simulations, to elucidate important parameter(s) of the simulation and produce a “digital twin” of a growing dendrite. This study was conducted to evaluate the performance of a data assimilation system, employing an ensemble Kalman filter, through systematic twin experiments focused on columnar dendrite growth in a thin film of a binary alloy. The results show that the data assimilation system effectively estimates multiple parameters and dendrite morphology simultaneously. Furthermore, two approaches—domain decomposition method and parameter estimation method from a part of the domain—to reduce computational costs are investigated. Both methods can effectively reduce the computational cost of data assimilation. Additionally, data assimilation using voxel observation data of varying resolutions, mimicking actual X-ray images, is performed. The study findings discourage the direct use of voxel data owing to incompatible PF simulations. PF relaxation computation is explored as a solution for generating observation data with smooth PF profiles. The results show that while relaxation computation enhances the estimation accuracy for high-resolution voxel data, its efficacy diminishes for low-resolution data. These detailed investigations of data assimilation provide valuable insights for utilizing actual in situ X-ray observation data in dendrite growth studies.

Empirical best predictors under multivariate Fay-Herriot models and their numerical approximation

Small area estimation of multivariable non-linear domain indicators using aggregated data is addressed. By assuming that the target vector follows a multivariate Fay-Herriot model, empirical best predictors of domain parameters that are arbitrary Lebesgue-measurable functions of multiple target variables are derived. In this context, Monte Carlo and Gauss-Hermite quadrature methods for integral approximation are discussed. A parametric bootstrap algorithm for mean squared error estimation is presented. Simulation experiments are conducted to study the behaviour of the introduced methodology. Moreover, an illustrative application to real data from the Spanish labour force survey is provided. In this example, province-level unemployment rates, crossed by age classes and sex, are estimated.

Design of two-loop FOPID-FOPI controller for inverted cart-pendulum system

The inverted cart-pendulum system (ICPS) consists in having a pendulum mounted on a sliding cart, with the pivot point fixed. This real time experiment indeed looks like a rocket and its functionality is akin to a rocket. These are the launchers and the missile guidance and control as well as construction anti-seismic measures also. The control aim in these systems is to maintain the inverted pendulum vertically stable. The system is causal but unstable and, therefore, has no minimum phase. Therefore, the right half plane pole and zero are close to each other. Therefore, the stability of the system can be considered as problematic at some points. Unfortunately, linear time- invariant (LTI) classical controllers are incapable of offering suffient loop robustness for such systems. This paper aims to project a two-loop fractional order controller (2-LFOC) design to stabilize a higher-order nonlinear inverted cart-pendulum system (ICPS). The modeling, linearization, and control of ICPS are demonstrated in this work. The control target is adjusted so that the inverted pendulum stabilizes in its upright state when the cart reaches the required point. To fulfill the control objective, two-loop FOPID-FOPI controllers are proposed, and the Levenberg Marquardt algorithm (LMA) is utilized to tune the controllers. A novel nonlinear integral of time-associated absolute-error (ITAE) based fitness formula considering the settling time and rise time is used to fit the controller parameters for 2-LFOC. A performance comparison with the PID controller in terms of different time domain parameters such as rise_time (T R ), peak_time (T P ), settling_time (T S ), maximum overshoot (OS M ), maximum undershoot (US M ) and steady-state error (E SS ) are investigated. Stability analysis using Riemann surface observation of the system compensated with the proposed controller is presented in this work. The robust behavior of the two-loop FOPID-FOPI controller is verified by the application of disturbances in the system and the Reimann surface observation.

Overcompensation of transient and permanent death rate increases in age-structured models with cannibalistic interactions

There has been renewed interest in understanding the mathematical structure of ecological population models that lead to overcompensation, the process by which a population recovers to a higher level after suffering a permanent increase in predation or harvesting. Here, we apply a recently formulated kinetic population theory to formally construct an age-structured single-species population model that includes a cannibalistic interaction in which older individuals prey on younger ones. Depending on the age-dependent structure of this interaction, our model can exhibit transient or steady-state overcompensation of an increased death rate as well as oscillations of the total population, both phenomena that have been observed in ecological systems. Analytic and numerical analysis of our model reveals sufficient conditions for overcompensation and oscillations. We also show how our structured population partial integrodifferential equation (PIDE) model can be reduced to coupled ODE models representing piecewise constant parameter domains, providing additional mathematical insight into the emergence of overcompensation.

Multiple stable postures of a falling object in fluids

We present evidence revealing that an object with specific properties can exhibit multiple stable falling postures at low Reynolds numbers. By scrutinizing the force equilibrium relationship of a fixed object at various attack angles and Reynolds numbers, we introduce a methodology that can obtain the stable falling postures of the object. This method saves computational resources and more intuitively presents the results in the full parameter domain. Our findings are substantiated by free-fall tests conducted through both physical experiments and numerical simulations, which validate the existence of multiple stable solutions in accordance with the interpolation results obtained with fixed objects. Additionally, we quantify the abundance and distribution patterns of stable falling postures for a diverse range of representative shapes. This discovery highlights the existence of multiple stable solutions that are universally present across objects of different shapes. The implications of this research extend to the design, stability control and trajectory prediction of all free and controlled flights in both air and water.

Fault Detection of Rotating Machines Using poly-Coherent Composite Spectrum of Measured Vibration Responses with Machine Learning

This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations into a single spectrum, retaining amplitude and phase information while reducing background noise. The use of pCCS significantly reduces the number of extracted parameters in the frequency domain compared to using individual spectra at each measurement location. This reduction in parameters is crucial, especially for large industrial rotating machines, as processing and analysing extensive datasets demand significant computational resources, increasing the time and cost of fault detection. An artificial neural network (ANN)-based machine learning model is then employed for fault detection using these reduced extracted parameters. The methodology is developed and validated on an experimental rotating machine at three different speeds: below the first critical speed, between the first and second critical speeds, and above the second critical speed. This range of speeds represents the diverse dynamic conditions commonly encountered in industrial settings. This study examines both healthy machine conditions and various simulated fault conditions, including misalignment, rotor-to-stator rub, shaft cracks, and bearing faults. By combining the pCCS technique with machine learning, this study enhances the reliability, efficiency, and practical applicability of fault detection in rotating machines under varying dynamic conditions and different machine conditions.

Self-organizing broad network with frequency-domain analysis

The broad network (BN) based on random feature extraction has fast computational nature. However, it usually suffers from redundant features due to its randomization in dealing with massive samples, which brings the risk of overfitting. To overcome this problem, a self-organizing BN (SO-BN) is proposed in this article. First, several groups of polynomial-based fuzzy rules (P-FRs) are embedded into BN instead of the original feature nodes. Then, P-FR with the form of approximated bell functions has the capability to cope with the uncertainties of feature extraction. Second, a frequency domain parameter calculation algorithm is presented to update the parameters of P-FRs in SO-BN. Different from traditional randomization, P-FRs are shaped with frequency-domain analysis for achieving the representative features of samples. Third, an efficient self-organizing mechanism is built to adjust the structure of the enhancement layer dynamically. Then, the enhancement layer can be expanded and pruned rapidly to reduce the redundant feature as well as improve the performance of SO-BN. Finally, the proposed SO-BN is tested on two benchmark datasets and three real-world engineering applications, especially the prediction of sunspot numbers, electrical output and total phosphorus concentration. The results indicate that SO-BN can achieve superior prediction performance than other models.

A Bayesian Tensor Decomposition Method for Joint Estimation of Channel and Interference Parameters.

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input-multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time-frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation.

Using heart rate variability to evaluate the association between the autonomic nervous system and coagulation function in patients with endometrial cancer.

The incidence of endometrial cancer (EC) is increasing worldwide, but the specific mechanism of coagulation dysfunction in EC is not fully understood. The objective of the present study was to explore the relationship between autonomic nervous system function and coagulation function in patients with EC using heart rate variability (HRV) analysis. The study included 100 patients with EC who were treated at the Department of Gynecological Oncology of The First Affiliated Hospital of Bengbu Medical University (Bengbu, China) from December 2021 to March 2023. A 5-min resting electrocardiogram was collected from each patient to analyze HRV parameters, including the time domain parameters standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD), and the frequency domain parameters low-frequency power and high-frequency power (HF). Blood samples were submitted to biochemistry tests to measure coagulation markers, namely prothrombin time (PT), international normalized ratio of PT (PT-INR), prothrombin activity (PTA), activated partial thromboplastin time (APTT) and fibrinogen. Bivariate Spearman correlation analyses revealed that PT, PT-INR and APTT were significantly positively correlated with SDNN, RMSSD and HF, while PTA was significantly negatively correlated with RMSSD. Following adjustments for confounding factors, namely age, body mass index, menopause, ligation of the fallopian tubes, diabetes, hypertension, adjuvant chemotherapy and mean heart rate, linear regression analysis demonstrated that SDNN, RMSSD and HF were independent factors influencing PT and PT-INR in patients with EC. The findings of the present study indicate that certain HRV parameters correlate with coagulation markers in EC and provide new insight into the occurrence of cancer-associated coagulation dysfunction.

On the existence of Rayleigh waves with full impedance boundary condition

The existence of Rayleigh waves (propagating in isotropic elastic half-spaces) with the tangential and normal impedance boundary conditions was investigated. It has been shown that for the tangential impedance boundary condition (TIBC), there always exists a unique Rayleigh wave, while for the normal impedance boundary condition (NIBC), there exists a domain (of impedance and material parameters) in which exactly one Rayleigh wave is possible and outside this domain a Rayleigh wave is impossible. In this paper, we consider the existence of Rayleigh waves with the full impedance boundary condition (FIBC) that contains both TIBC and NIBC. It is shown that the existence picture of Rayleigh waves for this general case is more complicated. It contains domain for which exactly one Rayleigh wave exists, domain where a Rayleigh wave is impossible, and domain for which all three possibilities may occur: two Rayleigh waves exist, one Rayleigh wave exists, and no Rayleigh wave exists at all. The obtained existence results recover the existence results established previously for the cases of TIBC and NIBC. The formulas for the Rayleigh wave velocity are derived. As these formulas are totally explicit, they are very useful in various practical applications, especially in the non-destructive evaluation of the mechanical properties of structures. In order to establish the existence results and derive formulas for the Rayleigh wave velocity, the complex function method, which is based on the Cauchy-type integrals, is employed.

Hybrid-feature based spherical quasi-conformal registration for AD-induced hippocampal surface morphological changes

Background and objectiveEstablishing accurate one-to-one morphological correspondence between different hippocampal surfaces is a solid foundation for the analysis of AD-induced hippocampal morphological changes. However, owing to the large variations between hippocampal surfaces, exiting registration work either fails to obtain the accurate matching of local and overall morphological features or does not preserve the bijectivity during parametric mapping. For this reason, this study proposes a hybrid-feature based spherical quasi-conformal registration (HSQR) method that can effectively maintain the diffeomorphic property while meeting the hybrid-feature matching constraints in the spherical parameter domain. MethodsThe HSQR algorithm is primarily achieved through hippocampal surface hybrid feature extraction and spherical quasi-conformal registration. First, hybrid features for a comprehensive morphological description of the hippocampal surface were established, which included essential anatomical features (landmarks) and mean curvature (intensity) features to ensure the accuracy of surface morphology alignment. Second, spherical parameterization was applied to genus-0 closed surfaces, such as the hippocampus, which maximized the preservation of the original local surface morphology through area-preserving properties. Third, a novel spherical quasi-conformal registration algorithm that can handle large deformations is established. It transforms a 3D spherical parameter domain into a 2D plane parameter domain using iterative local stereo projection to improve the efficiency of the registration algorithm. Subsequently, by controlling the Beltramin coefficient, the hybrid morphological features could be aligned while ensuring bijection before and after registration. ResultsUsing a cohort including 161 patients with amyloid-β (Aβ) positive Alzheimer disease (AD), 234 Aβ positive mild cognitive impairment (MCI) and 266 Aβ negative cognitively unimpaired (CU) individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we set up the experiment which indicated that the HSQR-based whole bilateral hippocampal atrophy features demonstrated the stronger statistical power for group morphological differences of CU vs. MCI with q-value: 0.0453 for left hippocampus and 0.0401 for right hippocampus and group morphological differences of AD vs. MCI with q-value: 0.0282 for left hippocampus and 0.0421 for right hippocampus. ConclusionsOur registration algorithm may provide a solid foundation for the accurate quantification of hippocampal surface morphological changes for the differential diagnosis and tracking of AD.

Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies

Dependencies on the relative frequency of a state in the domain are common when modelling probabilistic dependencies on relational data. For instance, the likelihood of a school closure during an epidemic might depend on the proportion of infected pupils exceeding a threshold. Often, rather than depending on discrete thresholds, dependencies are continuous: for instance, the likelihood of any one mosquito bite transmitting an illness depends on the proportion of carrier mosquitoes. Current approaches usually only consider probabilities over possible worlds rather than over domain elements themselves. An exception are the recently introduced Lifted Bayesian Networks for Conditional Probability Logic, which express discrete dependencies on probabilistic data. We introduce functional lifted Bayesian networks, a formalism that explicitly incorporates continuous dependencies on relative frequencies into statistical relational artificial intelligence. and compare and contrast them with ifted Bayesian Networks for Conditional Probability Logic. Incorporating relative frequencies is not only beneficial to modelling; it also provides a more rigorous approach to learning problems where training and test or application domains have different sizes. To this end, we provide a representation of the asymptotic probability distributions induced by functional lifted Bayesian networks on domains of increasing sizes. Since that representation has well-understood scaling behaviour across domain sizes, it can be used to estimate parameters for a large domain consistently from randomly sampled subpopulations. Furthermore, we show that in parametric families of FLBN, convergence is uniform in the parameters, which ensures a meaningful dependence of the asymptotic probabilities on the parameters of the model.

Full-Speed Domain Parameter Identification of Surface-Mounted PMSLM Based on Dual Axis Injection

Full-Speed Domain Parameter Identification of Surface-Mounted PMSLM Based on Dual Axis Injection

Mesh Parameterization Meets Intrinsic Triangulations

AbstractA parameterization of a triangle mesh is a realization in the plane so that all triangles have positive signed area. Triangle mesh parameterizations are commonly computed by minimizing a distortion energy, measuring the distortions of the triangles as they are mapped into the parameter domain. It is assumed that the triangulation is fixed and the triangles are mapped affinely. We consider a more general setup and additionally optimize among the intrinsic triangulations of the piecewise linear input geometry. This means the distortion energy is computed for the same geometry, yet the space of possible parameterizations is enlarged. For minimizing the distortion energy, we suggest alternating between varying the parameter locations of the vertices and intrinsic flipping. We show that this process improves the mapping for different distortion energies at moderate additional cost. We also find intrinsic triangulations that are better starting points for the optimization of positions, offering a compromise between the full optimization approach and exploiting the additional freedom of intrinsic triangulations.

Analyzing risk influencing factors of ship collision accidents: A data-driven Bayesian network model integrating physical knowledge

The interaction and propagation effects among risk-influencing factors (RIFs) often lead to maritime accidents. Therefore, to support maritime risk management in decision-making, it is imperative to conduct a quantitative risk assessment (QRA) of these accidents, which requires a methodology capable of capturing causal relationships in limited cases. To achieve this goal, this study proposes a data-driven Bayesian network (BN) model that integrates physical knowledge with the QRA. Based on the collected data, a combination of domain knowledge, structure learning, and parameter learning is employed to construct the model. In the data-driven phase, three structural learning algorithms including Bayesian search (BS), Greedy thick thinning (GTT), and Peter-Clark (PC) algorithms, were used in combination with the expectation maximization algorithm of parameter learning. Then, using four indices calculated by the confusion matrix, the most fitting algorithm for structure learning was chosen, and the confusion matrix was obtained by five-fold cross-validation. Finally, network propagation impact (NPI) is introduced to prioritize RIFs. Preventive measures and emergency plans are formulated based on the network resilience metric (NRM) and network vulnerability metric (NVM). This approach leverages the objectivity of a data-driven BN and integrates domain expertise to enhance model validity. A case study of collisions was conducted to demonstrate the applicability of the model. The data were sourced from 327 collision accident reports provided by the China Maritime Safety Administration, and the PC algorithm was chosen. Findings indicate that the “Management system”, “Supervision”, and “Crew training” RIFs are prioritized for preventive measures, whereas “Communication” receives more resources for emergency handling. The results suggest the formulation of risk management strategies for practitioners to improve maritime safety.

Influence of shift work on sleep quality and circadian patterns of heart rate variability among nurses.

Shift work implementation is essential for providing continuous patient care in hospitals. However, working in shifts on a routine basis may disrupt the circadian pattern and alter the sleep-wakefulness cycle in nurses. Stress due to shift work can influence the adaptability of the cardiovascular system, produce psychophysiological strain and deteriorate work performance in female nurses. This study investigated the effect of morning and night shift work on sleep quality and circadian patterns governing heart rate variability (HRV) in female nurses working in a tertiary care hospital. Thirty-eight healthy female nurses were recruited. Frequency and time domain parameters of HRV were recorded as markers of cardiac autonomic function. A student t-test was used to investigate differences in HRV between morning and night shift workers. Mann-Whitney non-parametric test was applied for the difference between Pittsburgh Sleep Quality Index (PSQI) scores in the two groups. Standard deviation of the normal-to-normal interval (SDNN) (msec), total power (ms2) and high-frequency (HF) band power (ms2) were significantly reduced in night shift nurses than in morning shift nurses. The low-frequency (LF)/HF ratio was significantly increased in night shift nurses. The differences in standard deviation of the averages of NN intervals (SDaNN) (msec), root mean square of successive differences between adjacent NN intervals (RMSSD), mean NN, very low-frequency (VLF) band power (ms2) and LF band power (ms2) were not statistically significant. The global PSQI score was significantly higher among night shift workers than in morning shifts. Inadequate sleep can disrupt the body's ability to regulate heart rhythm and increase the risk of cardiovascular diseases and mortality. The research suggests a propensity for autonomic imbalance in night shift workers when compared to their counterparts on morning shifts.

Investigation of decay mechanisms and associated aspects of exotic Nobeliumisotopes using the Skyrme energy density formalism

Abstract Persistent theoretical and experimental attempts have been made to investigate the&amp;#xD;heavy ion induced reactions and their subsequent decay mechanisms in superheavy mass region.&amp;#xD;In addition, the region of transfermium elements is itself of great interest because of the neutron&amp;#xD;/ proton shell effects. Here, we aim to study the subsequent decay mechanisms of two isotopes of&amp;#xD;Z = 102 nucleus, i.e. 248No and 250No.&amp;#xD;The dynamical cluster-decay model (DCM) based on Quantum Mechanical Fragmen-&amp;#xD;tation Theory (QMFT), is employed to conduct a comprehensive analysis of compound nucleus&amp;#xD;(CN) and non-compound nucleus (nCN) mechanisms such as fusion-fission (ff), Quasi fission (QF)&amp;#xD;and fast fission (FF), the role of centre of mass energy (Ec.m.) and angular momentum (ℓ) for&amp;#xD;248No and 250No isotopes. The nuclear interaction potential is obtained using the Skyrme energy&amp;#xD;density formalism (SEDF) in the domain of GSkI force parameters. The probability of compound nucleus formation&amp;#xD;(PCN ) is determined using a function that depends on the centre of mass energy. The lifetimes for&amp;#xD;the fusion-fission (ff) quasi fission (QF) channels are explored.&amp;#xD;Here, CN and nCN decay mechanisms for two isotopes of Z = 102 nobelium are studied&amp;#xD;over the wide range of centre-of-mass (Ec.m.) by including the quadrupole deformation (β2) and&amp;#xD;optimum orientations (θopt.) of decaying fragments. The fragmentation potential, preformation&amp;#xD;probability, neck length parameter and reaction cross-sections are explored. Further, the calcula-&amp;#xD;tions are done for PCN in order to identify the decay modes of 248No and 250No isotopes. The&amp;#xD;fusion-fission lifetimes and quasi fission lifetimes are compared with the dinuclear system (DNS)&amp;#xD;approach. The most probable fragments such as 122Sn and 128T e are observed near to the magic shell closure Z = 50 and&amp;#xD;N = 82. The ff and qf lifetime decreases with increase in the excitation energy.