Temporal Behavior Research Articles

Measurement of 2p-3d absorption in a hot molybdenum plasma

We present measurements of the 2p-3d transition opacity of a hot molybdenum–scandium sample with nearly half-vacant molybdenum M-shell configurations. A plastic-tamped molybdenum–scandium foil sample is radiatively heated to high temperature in a compact D-shaped gold Hohlraum driven by ∼30 kJ laser energy at the SG-100 kJ laser facility. X rays transmitted through the molybdenum and scandium plasmas are diffracted by crystals and finally recorded by image plates. The electron temperatures in the sample in particular spatial and temporal zones are determined by the K-shell absorption of the scandium plasma. A combination of the IRAD3D view factor code and the MULTI hydrodynamic code is used to simulate the spatial distribution and temporal behavior of the sample temperature and density. The inferred temperature in the molybdenum plasma reaches a average of 138 ± 11 eV. A detailed configuration-accounting calculation of the n = 2–3 transition absorption of the molybdenum plasma is compared with experimental measurements and quite good agreement is found. The present measurements provide an opportunity to test opacity models for complicated M-shell configurations.

Improving the hepatitis viral transmission model’s dynamics by vaccination and contrasting it with the fractional-order model

We investigate mathematical models of the Hepatitis B and C viruses in the study, considering vaccination effects into account. By utilising fractional and ordinary differential equations, we prove the existence of equilibrium and the well-posedness of the solution. We prove worldwide stability with respect to the fundamental reproduction number. Our numerical techniques highlight the biological relevance and highlight the effect of fractional derivatives on temporal behaviour. We illustrate the relationships among susceptible, immunised, and infected populations in our epidemiological model. Using comprehensive numerical simulations, we analyse the effects of fractional derivatives and highlight solution behaviours. Subsequent investigations will examine the impact of regional heterogeneity, providing significant perspectives for epidemiological research.

Methylphenidate, but not citalopram, decreases impulsive choice in rats performing a temporal discounting task.

Drugs targeting monoamine systems remain the most common treatment for disorders with impulse control impairments. There is a body of literature suggesting that drugs affecting serotonin reuptake and dopamine reuptake can modulate distinct aspects of impulsivity - though such tests are often performed using distinct behavioral tasks prohibiting easy comparisons. Here, we directly compare pharmacologic agents that affect dopamine (methylphenidate) vs serotonin (citalopram) manipulations on choice impulsivity in a temporal discounting task where rats could choose between a small, immediate reward or a large reward delayed at either 2 or 10s. In control conditions, rats preferred the large reward at a small (2s) delay and discounted the large reward at a long (10s) delay. Methylphenidate, a dopamine transport inhibitor that blocks reuptake of dopamine, dose-dependently increased large reward preference in the long delay (10s) block. Citalopram, a selective serotonin reuptake inhibitor, had no effect on temporal discounting behavior. Impulsive behavior on the temporal discounting task was at least partially mediated by the nucleus accumbens shell. Bilateral lesions to the nucleus accumbens shell reduced choice impulsivity during the long delay (10s) block. Following lesions, methylphenidate did not impact impulsivity. Our results suggest that striatal dopaminergic systems modulate choice impulsivity via actions within the nucleus accumbens shell, whereas serotonin systems may regulate different aspects of behavioral inhibition/impulsivity.

Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer‘s disease

Understanding the psychiatric symptoms of Alzheimer s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is not well-known. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored.This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory and composite scores for memory, executive function, and language, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated, controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define “asymmetry-extreme” groups (individuals with laterality indices greater than two standard deviations from the mean).695 individuals (age = 73.9 ± 7.6 years, 372 (53.5 %) females) were included, comprising 281 (40%) cognitively unimpaired (CU) amyloid negative, 185 (27%) CU amyloid positive, and 229 (33%) impaired (CI) amyloid positive participants. In the full cohort analysis, right temporal tau was associated with worse behavior (B = 8.14, p-value = 0.007), and left temporal tau was associated with worse language (B = 1.4, p-value < 0.001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, there was additional heterogeneity along the anterior-posterior dimension.Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Wide multi-cultural implementation of social cognition measures is needed to understand right-sided asymmetry in AD.

Stability of convective rolls in a horizontal layer rotating about an inclined axis

We present three results on stability of rolls in Boussinesq convection in a plane horizontal layer with rigid boundaries that is rotating about an inclined axis with the angular velocity Ω = ( Ω 1 , Ω 2 , Ω 3 ) . (i) We call the full problem the set of equations governing the temporal behaviour of the flow and temperature for an arbitrary Ω , and by the reduced problem the set of equations for the angular velocity ( Ω 1 , 0 , Ω 3 ) . Here x,y are horizontal Cartesian coordinates in the layer and z is the vertical one. We prove that a y-independent solution to one of the two problems is also a solution to the second one. (ii) We calculate the critical Rayleigh number for the monotonic onset of convection. The instability mode in the form of rolls (a flow independent of a horizontal direction) is assumed. Let β be the angle between the horizontal projection of Ω and the rolls axes. We show that β = 0 for the least stable mode. When the axis of rotation is horizontal, this is proven analytically, and for Ω 3 ≠ 0 , the result is obtained numerically. Taking i into account, we conclude that the critical Rayleigh number for the onset of convection is independent of Ω 1 and Ω 2 and the emerging flow are rolls with axis aligned with the horizontal component of the rotation vector. (iii) We study the behaviour of convective flows by integrating numerically the three-dimensional equations of convection for Ω = ( 0 , Ω 2 , Ω 3 ) and a range of the Rayleigh numbers, other parameters of the problem being fixed. We assume square horizontal periodicity cells, whose sides are equal to the period of the most unstable mode. The computations indicate that, in general, in the nonlinear regime convective rolls become more stable as Ω 2 increases. Namely, on increasing Ω 2 , the interval of the Rayleigh numbers for which convective rolls are stable increases.

From certainty to chance: probabilistic insights into quantum control

This study introduces an innovative control methodology designed specifically for quantum physical systems. It bridges a critical gap by accounting for the full spectrum of uncertainties and noise that could affect the time evolution of these systems. In contrast to conventional methods, our pioneering approach utilizes probability density functions (pdfs) to characterize quantum dynamics, providing a more detailed and accurate description of their temporal behavior. We propose a strategy that seeks to minimize the discrepancy between the actual pdf, which encapsulates the combined dynamics of the quantum system and an external electric field, and a desired pdf that aligns with the system’s intended outcomes. This strategy marks a significant shift from traditional quantum control techniques. Initially, we present a solution for controlling quantum systems defined by general pdfs. This solution is then demonstrated on quantum systems described by Gaussian pdfs, with an in-depth account of the resulting optimized controller’s structure. The study culminates with practical demonstrations, showcasing the approach’s efficacy and practicality, thus endorsing its potential as a formidable instrument in quantum control.Graphical abstract

A Systematic Analysis of the X-Ray Emission in Optically Selected Tidal Disruption Events: Observational Evidence for the Unification of the Optically and X-Ray-selected Populations

We present a systematic analysis of the X-ray emission of a sample of 17 optically selected, X-ray-detected tidal disruption events (TDEs) discovered between 2014 and 2021. The X-ray light curves show a diverse range of temporal behaviors, with most sources not following the expected power-law decline. The X-ray spectra are mostly extremely soft and consistent with thermal emission from the innermost region of an accretion disk, which cools as the accretion rate decreases. Three sources show formation of a hard X-ray corona at late times. The spectral energy distribution shape, probed by the ratio (L BB/L X) between the UV/optical and X-ray, shows a wide range of L BB/L X ∈ (0.5, 3000) at early times and converges to disklike values of L BB/L X ∈ (0.5, 10) at late times. We estimate the fraction of optically discovered TDEs with L X ≥ 1042 erg s−1 to be at least 40% and show that X-ray loudness is independent of black hole mass. We argue that distinct disk formation timescales are unlikely to be able to explain the diverse range of X-ray evolution. We combine our sample with X-ray-discovered ones to construct an X-ray luminosity function, best fit by a broken power law, with a break at L X ≈ 1044 erg s−1. We show that there is no dichotomy between optically and X-ray-selected TDEs; instead, there is a continuum of early-time L BB/L X, at least as wide as L BB/L X ∈ (0.1, 3000), with optical/X-ray surveys selecting preferentially, but not exclusively, from the higher/lower end of the distribution. Our findings are consistent with unification models for the overall TDE population.

Polymerase-Based Signal Delay for Temporally Regulating DNA Involved Reactions, Programming Dynamic Molecular Systems, and Biomimetic Sensing.

Complex temporal molecular signals play a pivotal role in the intricate biological pathways of living organisms, and cells exhibit the ability to transmit and receive information by intricately managing the temporal dynamics of their signaling molecules. Although biomimetic molecular networks are successfully engineered outside of cells, the capacity to precisely manipulate temporal behaviors remains limited. In this study, the catalysis activity of isothermal DNA polymerase (DNAP) through combined use of molecular dynamics simulation analysis and fluorescence assays is first characterized. DNAP-driven delay in signal strand release ranged from 100 to 102min, which is achieved through new strategies including the introduction of primer overhangs, utilization of inhibitory reagents, and alteration of DNA template lengths. The results provide a deeper insight into the underlying mechanisms of temporal control DNAP-mediated primer extension and DNA strand displacement reactions. Then, the regulated DNAP catalysis reactions are applied in temporal modulation of downstream DNA-involved reactions, the establishment of dynamic molecular signals, and the generation of barcodes for multiplexed detection of target genes. The utility of DNAP-based signal delay as a dynamic DNA nanotechnology extends beyond theoretical concepts and achieves practical applications in the fields of cell-free synthetic biology and bionic sensing.

AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output.

Analyzing the impact of time-fractional models on chemotherapy's effect on cancer cells

In this study, we employ the Laplace Variational Iterational Method (LVIM) as a sophisticated mathematical tool to investigate into the complex dynamics of cancer cells under the influence of chemotherapy. The LVIM, a method combining Laplace transformations and variational iteration techniques, is specifically adapted to address a system of time fractional differential equations (FDEs) that characterizes the temporal behavior of cancer cells. To enhance the efficacy of our approach, we introduce a semi-analytic version of LVIM, which proves to be a powerful and versatile tool for solving mathematical problems involving fractional derivatives. The focus of our analysis centers on elucidating the impact of chemotherapy, with a particular emphasis on drug diffusion within cancer cells and the fractality of DNA walks. Through numerical exploration encompassing varying fractional order derivatives, our study exposes nonlinear behaviors that remain secret in systems featuring only integer order derivatives. Notably, the methodology we propose is not only applicable to the specific cases examined in this research but also exhibits broad versatility, making it suitable for exploring the effects of different drugs and types of cancers. This research contributes valuable insights into the dynamics of cancer cells, aiding in the understanding of the implications for therapeutic strategies in the context of cancer treatment.

Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls

Advanced Persistent Threats (APTs) are highly sophisticated cyberattacks that are aimed at achieving strategic goals and are usually backed by a well-funded entity. In this paper, we tackle the challenges of detecting and attributing APTs by proposing Bon-APT, a temporal learning method that analyzes and segment the occurrences of API calls invoked during the dynamic analysis of the examined PE. Those segments can be used to profile the temporal behavior of an APT, provide insights into its modus operandi, and induce an accurate machine-learning based model for the detection and attribution of APTs. Moreover, Bon-APT provides a human comprehensible explainability regarding the relations among segments as well as the behavior of the APT in each of them. This not only improves transparency and reliability from a human expert perspective, but it can also enrich the security experts with new knowledge regarding APTs' behavior. To evaluate Bon-APT, we built a unique collection of 12,655 APTs, belonging to 188 different cyber-groups and 17 different nations, which, to the best of our knowledge, is the largest collection of its kind. We conducted four experiments to evaluate the proposed method and compared its performance to the performance of state-of-the-art methods on the tasks of APT detection and authorship attribution (for both group and nation). Bon-APT achieved promising results in each of the tasks while outperforming the state-of-the-art methods. Bon-APT also provides a simple and concise explanation regarding its decisions and the APT behavior, as well as an easy, straightforward visual and quantitative behavioral comparison.

Effect of gas components on the post-discharge temporal behavior of OH and O of a non-equilibrium atmospheric pressure plasma driven by nanosecond voltage pulses

OH radicals and O atoms are two of the most important reactive species of non-equilibrium atmospheric pressure plasma (NAPP), which plays an important role in applications such as plasma medicine. However, experimental studies on how the gas content affects the post-discharge temporal evolutions of OH and O in the noble gas ns-NAPP are very limited. In this work, the effect of the percentages of O2, N2, and H2O on the amounts of OH and O productions and their post-discharge temporal behaviors in ns-NAPP is investigated by laser-induced fluorescence (LIF) method. The results show that the productions of OH and O increase and then decrease with the increase of O2 percentage. Both OH and O densities reach their maximum when about 0.8% O2 is added. Further increase of the O2 concentration results in a decrease of the initial densities of both OH and O, and leads to their faster decay. The increase of N2 percentage also results in the increase and then decrease of the OH and O densities, but the change is smaller. Furthermore, when the H2O concentration is increased from 100 to 3000 ppm, the initial OH density increases slightly, but the OH density decays much faster, while the initial density of O decreases with the increase of the H2O concentration. After analysis, it is found that OH and O are mainly produced through electron collisional dissociation. O(1D) is critical for OH generation. O3 accelerates the consumption processes of OH and O at high O2 percentage. The addition of H2O in the NAPP considerably enhances the electronegativity, while it decreases the overall plasma reactivity, accelerates the decay of OH, and reduces the O atom density.

A Situation and Temporal Behaviors of Air Pollution by ARIMA Model: A Case Study of Chiang Mai

The persistent issue of haze has become a paramount concern in various regions, impacting air quality, human health, and overall environmental well-being. This study focuses on the case of Chiang Mai, a city prone to recurrent haze times. This research reviews haze situation and its temporal behaviors to propose successive opportunities for effectively addressing the haze problem in Chiang Mai, encompassing both short-term mitigation measures and long-term sustainable strategies by applying haze data from 1996 to 2022 derived from Yupparaj school ground-based station recorded by Pollution Control Department (PCD) for analyzing the ARIMA statistics. According to the study's findings, the daily averages of PM10 and PM2.5 move greatly from January to April. There is a minor change in the daily average carbon monoxide (CO) between January and April. Although short-term projections in light of the area's pollution levels, it may be indicated that PM10, PM2.5, SO2, NO2, and O3 will likely increase, regarding the local pollution's changing behavior. When compared to other working days and weekends, it was discovered that the average CO values were significantly different at the 0.01 level, with Friday having the highest average CO emissions. Consideringly the timing, it was discovered that the CO2 levels were at their highest during rush hour, peaking in the evening and then the morning and noon periods, respectively. These characteristics indicate that measures taken to address this problem should concentrate on operating at a time that is relevant to the time the air pollution arises as well as the influences reducing and mitigating the level of air pollution greatest efficiency.

Optimizing code allocation for hybrid on-chip memory in IoT systems

With the increasing application of IoT devices, the memory subsystem, as the performance and energy bottleneck of IoT systems, has received a lot of attention. One of the keys is on-chip memory which can bridge the performance gap between the CPU and main memory. While many off-the-shelf embedded processors utilize the hybrid on-chip memory architecture containing scratchpad memories (SPMs) and caches, most existing literature ignores the collaboration between caches and SPMs. This paper proposes static SPM allocation strategies for the architecture mentioned above in IoT systems, which try to minimize the overall instruction memory subsystem latency and/or energy consumption. We capture the intra- and inter-task cache conflict misses via a fine-grained temporal cache behavior model. Based on this cache conflict information, we propose an integer linear programming (ILP) algorithm to generate an optimal static function level SPM allocation for system performance. Furthermore, to improve the scalability of the proposed allocation scheme for an enormous task set, we offer the interference factor to calculate the interference impact quantitatively. Then, based on the interference factor, we present two approximate knapsack based heuristic algorithms to provide near optimal static allocation schemes at both function- and basic block-level granularities, which favors fast design space exploration. The experiment results demonstrate that the proposed solution achieves a 30.85% improvement in memory performance, and up to 31.39% reduction in energy consumption, compared to the existing SPM allocation scheme at the function level. In addition, the proposed basic block level allocation algorithm shows better performance than our function level allocation algorithm and other basic block level allocation algorithm.

Safety of cyclists interacting with autonomous vehicles: A combined microscopic simulation and SSAM analysis

The aim of this paper is to investigate the safety of cyclists while interacting with autonomous vehicles (AVs) in an uncontrolled T-intersection using PTV Vissim microsimulation combined with the Surrogate Safety Assessment Model (SSAM). For the microsimulation, three cycling behaviours were constructed (cautious, normal, and aggressive), and two AV behaviours (cautious and normal) were adopted. 87 h of microsimulation resulted in 230 conflicts. The type and nature of these conflicts, as well as two temporal indicators and four speed-related indicators were analysed. Nearly 96% of the conflicts were rear-end collisions, and almost 70% were conflicts where the cyclist was the first to arrive at the conflict point. Interestingly, the least involved cycling behaviour in conflicts was aggressive and the most involved was cautious. A significant relationship was found between severity clusters of temporal indicators and cycling behaviour. It is also concluded that an important future research direction would be the construction of a two-dimensional conflict diagram with severity thresholds specifically for bicycle-vehicle interactions.

Understanding the Efficiency Levels among Cryptocurrencies: Islamic, Green and Traditional

Background: Islamic cryptocurrencies are different from conventional ones in that they are backed by physical assets and are based on religious principles. After the COVID-19 pandemic, cryptocurrencies showed different behavior. However, there are not many studies on the efficiency, in its weak form, of these three typical families of cryptocurrencies (Islamic, green, and traditional). Purpose: This study compares the efficiency levels of Islamic cryptocurrencies (HelloGold), green cryptocurrencies (Cardano, NANO, Stellar, IOTA), and traditional cryptocurrencies (BTC and ETH) in the preceding period and during the geopolitical conflict between Russia and Ukraine in 2022. Methods: This research will use Lo and Mackinlay's (1988) variance ratio methodology, and the Detrended Fluctuation Analysis (DFA) model will be used. Results: The results indicate that the Islamic currency HGT and the green currency XNO display significant information asymmetries, rejecting the random walk hypothesis for various time intervals. Similarly, other green currencies such as XLM, ADA, and MIOTA, as well as ETH and BTC, reject the hypothesis to varying degrees and time intervals. Furthermore, the Islamic cryptocurrency (HelloGold) was anti-persistent before and during the conflict. The digital currencies ADA and BTC are persistent in both periods. ETH is in equilibrium in the pre-conflict period and becomes persistent during the conflict (0.50 - 0.56), while MIOTA and XLM are persistent during the pre-conflict period and shift to equilibrium during the Russian invasion of Ukraine in 2022. Finally, the XNO eco-currency shows the same anti-persistence characteristics during the two sub-periods. Conclusion: These results highlight the complexity and dynamics of cryptocurrency markets, indicating that different digital currencies can exhibit different temporal behaviors regarding information efficiency and persistence or anti-persistence patterns.

Enhancing academic performance prediction with temporal graph networks for massive open online courses

Educational big data significantly impacts education, and Massive Open Online Courses (MOOCs), a crucial learning approach, have evolved to be more intelligent with these technologies. Deep neural networks have significantly advanced the crucial task within MOOCs, predicting student academic performance. However, most deep learning-based methods usually ignore the temporal information and interaction behaviors during the learning activities, which can effectively enhance the model’s predictive accuracy. To tackle this, we formulate the learning processes of e-learning students as dynamic temporal graphs to encode the temporal information and interaction behaviors during their studying. We propose a novel academic performance prediction model (APP-TGN) based on temporal graph neural networks. Specifically, in APP-TGN, a dynamic graph is constructed from online learning activity logs. A temporal graph network with low-high filters learns potential academic performance variations encoded in dynamic graphs. Furthermore, a global sampling module is developed to mitigate the problem of false correlations in deep learning-based models. Finally, multi-head attention is utilized for predicting academic outcomes. Extensive experiments are conducted on a well-known public dataset. The experimental results indicate that APP-TGN significantly surpasses existing methods and demonstrates excellent potential in automated feedback and personalized learning.

Temporal dynamics of surface phonon polaritons in polar dielectric nanoparticles with nonlocality

Surface phonon polaritons (SPhPs) supported by polar dielectrics have been a promising platform for nanophotonics in mid-infrared spectral range. In this work, the temporal dynamic behavior of polar dielectric nanoparticles without (or with) spatial dispersion/nonlocality driven by the ultrashort Gaussian pulses is carried out. We demonstrate that three possible scenarios for the temporal evolutions of the dipole moment including ultrafast oscillations with the decay, exponential decay, and keeping a Gaussian shape exist, when the pulse duration of the incident field is much shorter than, similar to, and much longer than the localized SPhP lifetime. Once the nonlocal effect is considered, the oscillation period becomes large slightly, and the exponential decay turns fast. Furthermore, nonlocality-induced novel temporal behavior is found such as the decay with long-period oscillations when the center frequency of the incident pulse lies at the frequency of adjacent longitudinal resonant modes. The positive and negative time-shifts of the dielectric response reveal that the excitation of the dipole moment will be delayed or advanced. These temporal evolutions can pave the way towards potential applications in the modulation of ultrafast signals for the mid-infrared optoelectronic nanodevices.

Effect of K+ Force Fields on Ionic Conductivity and Charge Dynamics of KOH in Ethylene Glycol.

Predicting ionic conductivity is crucial for developing efficient electrolytes for energy storage and conversion and other electrochemical applications. An accurate estimate of ionic conductivity requires understanding complex ion-ion and ion-solvent interactions governing the charge transport at the molecular level. Molecular simulations can provide key insights into the spatial and temporal behavior of electrolyte constituents. However, such insights depend on the ability of force fields to describe the underlying phenomena. In this work, molecular dynamics simulations were leveraged to delineate the impact of force field parameters on ionic conductivity predictions of potassium hydroxide (KOH) in ethylene glycol (EG). Four different force fields were used to represent the K+ ion. Diffusion-based Nernst-Einstein and correlation-based Einstein approaches were implemented to estimate the ionic conductivity, and the predicted values were compared with experimental measurements. The physical aspects, including ion-aggregation, charge distribution, cluster correlation, and cluster dynamics, were also examined. A force field was identified that provides reasonably accurate Einstein conductivity values and a physically coherent representation of the electrolyte at the molecular level.

Study of a cannibalistic prey–predator model with Allee effect in prey under the presence of diffusion

In this study, we have investigated the temporal and spatio-temporal behavior of a prey–predator model with weak Allee effect in prey and the quality of being cannibalistic in a specialist predator. The parameters responsible for the Allee effect and cannibalism impact both the existence and stability of coexistence steady states of the temporal system. The temporal system exhibits various kinds of local bifurcations such as saddle–node, Hopf, Generalized Hopf (Bautin), Bogdanov–Takens, and global bifurcation like homoclinic, saddle–node bifurcation of limit cycles. For the model with self-diffusion, we establish the non-negativity and prior bounds of the solution. Subsequently, we derive the theoretical conditions in which self-diffusion leads to the destabilization of the interior equilibrium. Additionally, we explore the conditions under which cross-diffusion induces the Turing-instability where self-diffusion fails to do so. Further, we present different kinds of stationary and dynamic patterns on varying the values of diffusion coefficients to depict the spatio-temporal model’s rich dynamics. It has been found that the addition of self and cross-diffusion in a prey–predator model with the Allee effect in prey and cannibalistic predator play essential roles in comprehending the pattern formation of a distributed population model. It is expected that the comprehensive mathematical analysis and extensive numerical simulations used in investigating the global dynamics of the proposed model can facilitate researchers in studying the temporal and spatial aspects of prey–predator models in more significant detail.