Probabilistic Procedure Research Articles

Analysis of the seismic fragility of slender piers on prestressed concrete viaducts

This research aims to study the seismic fragility evaluations of slender piers with single-column sections on prestressed concrete viaducts to investigate how pier height, prestressing force, and ground motion intensity are related, not only in terms of mean values but also in their probability density functions (PDFs), which inherently imply parameters such as dispersion indices. A detailed nonlinear numerical analysis was performed using a finite element model that incorporated the realistic response of concrete, steel, and infrm-FB element types for the columns. To this end, fragility curves were developed based on a probabilistic procedure that accounted for the variability of material properties, pier geometry, and seismic loading. The results show that the fragility function of high piers is hardly dependent on the shear span efficiency. Conversely, slender piers exhibit an increased level of fragility when the effect is confined to a constant prestressing force. This study highlights the need to account for the nonlinearity of pier members and variation in seismic loading when estimating the seismic fragility of slender piers. The conclusions of this study are beneficial for designing and retrofitting strategies for prestressed concrete viaducts in seismic regions.

A computational probabilistic procedure to quantify the time of breast cancer recurrence after chemotherapy administration

We propose a random linear differential equation with jumps to model the dynamics of breast tumor growth using real patients’ data. The model considers the effect of chemotherapy administration and tumor resection. Also, the inherent randomness of the data and the model are taken into account.The model is probabilistically solved by combining two main methods belonging to Uncertainty Quantification: the principle of maximum entropy (PME) and the random variable transformation technique (RVT). The PME is applied to assign proper probability distributions to model parameters. The RVT technique is used to determine the probability density function (PDF) of the solution of the random differential equation, which is a stochastic process.We apply computational optimization techniques to determine the PDF of model parameters so that the PDF of the solution matches the ones assigned to real patients’ data.Once random tumor dynamics has been modeled, we simulate, after surgery, different adjuvant chemotherapy strategies with the aim of delaying tumor recurrence as long as possible. Results are in concordance with medical literature, and tumor relapse is delayed as more cycles of chemotherapy are administered.Although the proposed model has a common general structure, it is shown how it can be customized to patients in order to construct projections about the tumor’s growth. To better illustrate the applicability of the proposed approach, we carefully show how our model can be tailored to two real patients treated at the Hospital Clínico de Valencia (Spain). The obtained results have been overseen by doctors from this hospital.

Efficient quantum algorithm for all quantum wavelet transforms

Wavelet transforms are widely used in various fields of science and engineering as a mathematical tool with features that reveal information ignored by the Fourier transform. Unlike the Fourier transform, which is unique, a wavelet transform is specified by a sequence of numbers associated with the type of wavelet used and an order parameter specifying the length of the sequence. While the quantum Fourier transform, a quantum analog of the classical Fourier transform, has been pivotal in quantum computing, prior works on quantum wavelet transforms (QWTs) were limited to the second and fourth order of a particular wavelet, the Daubechies wavelet. Here we develop a simple yet efficient quantum algorithm for executing any wavelet transform on a quantum computer. Our approach is to decompose the kernel matrix of a wavelet transform as a linear combination of unitaries (LCU) that are compilable by easy-to-implement modular quantum arithmetic operations and use the LCU technique to construct a probabilistic procedure to implement a QWT with a known success probability. We then use properties of wavelets to make this approach deterministic by a few executions of the amplitude amplification strategy. We extend our approach to a multilevel wavelet transform and a generalized version, the packet wavelet transform, establishing computational complexities in terms of three parameters: the wavelet order M, the dimension N of the transformation matrix, and the transformation level d. We show the cost is logarithmic in N, linear in d and superlinear in M. Moreover, we show the cost is independent of M for practical applications. Our proposed QWTs could be used in quantum computing algorithms in a similar manner to their well-established counterpart, the quantum Fourier transform.

Photovoltaic-penetrated power distribution networks’ resiliency-oriented day-ahead scheduling equipped with power-to-hydrogen systems: A risk-driven decision framework

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities.

Weak Value Amplification of Photons in Optical Nonlinear Medium, Opto-Mechanical, and Spin-Mechanical Systems

A measurement of an observable A performed on a quantum system that is initially prepared in a state ρi, followed by a probabilistic procedure that leaves the system in a final state ρf, a process often referred as state postselection (or filtering process), can yield, on average, anomalous measurement results, i.e., values that may exceed the eigenvalue range of the observable being measured or be complex numbers. There is, therefore, an amplification effect of the average measurement result, i.e., the effect of the system on the measurement device is increased. When the coupling between the system and the measurement device satisfies some weakness conditions, the amplification effect occurs due to the weak value of the operator A. In this article, the amplification effect due to the postselection process is reviewed, and theoretical proposals and experiments published in the recent literature on the field are commented on. The emphasis is made on interactions occurring in optical nonlinear media and opto-mechanical and spin-mechanical systems, in which the amplification of number operators takes place.

Performance-based probabilistic liquefaction-induced ground settlement procedure

Performance-based procedures represent an improvement over current state-of-practice procedures that treat the assessment of seismic demand and engineering response parameters independently. Procedures used in current practice generally provide estimates of liquefaction-induced ground settlement that are inconsistent with the desired ground settlement hazard level. A recently developed probabilistic procedure to estimate liquefaction-induced ground settlement is employed to develop a new performance-based procedure that estimates ground settlement which accounts for key sources of uncertainty. The ground-motion intensity and ground settlement estimations are integrated in the proposed procedure to produce hazard curves for liquefaction-induced ground settlement. The hazard curve for ground settlement links different hazard levels with their corresponding values of ground settlement by evaluating a wide range of ground-motion intensities and site characterization parameters with their associated uncertainties. The proposed performance-based procedure also permits the evaluation of different sources of uncertainty and their effects on the ground settlement estimate.

MODEL-BASED OPTIMIZATION OF ICVs’ CONTROL FOR PRODUCERS, INJECTORS, AND WAG-CO2 CYCLES SIZE IN A CARBONATE RESERVOIR UNDER UNCERTAINTY

Life cycle management of oil fields involves complex decisions, with many variables to be considered. In some fields of the Brazilian pre-salt, this task is even more challenging given the ultra-deep water location, with an expressive amount of gas (including CO2) production. This feature becomes a restricting factor for oil production in these types of fields, and a proper management is essential to overcome this issue, improving both production and economic returns. Therefore, Water-Alternate-Gas (WAG) is often used as an Enhanced Oil Recovery- EOR mechanism, providing a better mobility control, improving reservoir sweep and oil recovery. Moreover, WAG can be an alternative for recycling the produced gas, providing an adequate destination (mainly for CO2) and avoiding environmental problems. In this case, we name the method as WAG-CO2. Moreover, Interval Control Valves (ICVs) are also a good alternative to deal with these fields, since they work as flow control devices and allow for a better balance of the fluid flow in the reservoir. In this work, we evaluate the impact of the use of ICVs in producers, injectors, along with WAG cycles size to improve the performance of a developed field. The work consists of two parts. In Part I, using a single simulation model, we performed (1) six hierarchical optimization approaches to verify if the order in which the optimization occurs influences the final results, and (2) a joint optimization of all parameters for comparison purposes. The final strategies are simulated in a reference case (representing the “true response” of the field) to evaluate how the achieved control rules would behave in a real field. In Part II, we considered a probabilistic procedure, applying one of the hierarchical approaches in several representative models (RMs) expressing uncertain scenarios. In this case, we compared nominal optimization of individual RMs against a robust optimization (in which all RMs are optimized simultaneously). The study is applied to the benchmark case SEC1_2022, a synthetic model analogous to a pre-salt field. The results show an expressive increase in the economic return of the field with a proper operation of the ICVs and size of the WAG-CO2 cycles, despite the platform being restricted by gas production capacity. This result occurred because of a better balance in fluid flow through the reservoir and among wells. Moreover, in the first part of the work, all hierarchical approaches presented very similar results in terms of NPV, so the order of the optimization steps did not significantly affect the results. The non-hierarchical approach presented a slightly better result, but still close to those obtained in the hierarchical approaches. Furthermore, the most impacting variable was the ICV for producers, followed by ICV for injectors and, then, WAG-CO2 cycles. Similar tendencies were observed in the second part of the work, with higher expected economic return for the robust optimization.

Probabilistic Procedures for SIR and SIS Epidemic Dynamics on Erdös-Rényi Contact Networks

This paper introduces the mathematical formalization of two probabilistic procedures for susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) infectious diseases epidemic models, over Erdös-Rényi contact networks. In our approach, we consider the epidemic threshold, for both models, defined by the inverse of the spectral radius of the associated adjacency matrices, which expresses the network topology. The epidemic threshold dynamics are analyzed, depending on the global dynamics of the network structure. The main contribution of this work is the relationship established between the epidemic threshold and the topological entropy of the Erdös-Rényi contact networks. In addition, a relationship between the basic reproduction number and the topological entropy is also stated. The trigger of the infectious state is studied, where the probability value of the stability of the infected state after the first instant, depending on the degree of the node in the seed set, is proven. Some numerical studies are included and illustrate the implementation of the probabilistic procedures introduced, complementing the discussion on the choice of the seed set.

AC transmission expansion planning based on self‐organized criticality to mitigate the risk of cascading blackouts

AbstractThe secular increase of load causes the evolving power system to operate near the critical margins. In such attitudes, the probability of cascading line outages, as the major reason for recent blackouts, is much more than expected. As a result, an innovative approach for optimal AC transmission expansion planning (TEP) to minimize the risk of cascading blackouts is presented in this paper. The proposed multi‐objective AC‐TEP problem minimizes the expected energy not supplied due to the total load shedding imposed on the system to mitigate the risk of cascading line outages along with the total investment and operational costs of the new transmission lines planned to be optimally added to the system. This probabilistic procedure is based on self‐organized criticality and considers the actual aspects of the power system such as reactive power and voltage concerns in the planning stage. The obtained Pareto front set provides the opportunity for the planners to choose the most preferred solution. The proposed method is implemented and verified in the IEEE 118‐bus test system and compared with two types of TEP approaches. The first one is a previously reported version of this work which was a simplified DC model of the problem and the second one is the conventional AC‐TEP model. The obtained results show the benefits of the proposed AC model on the mitigation of cascading line outages in a more realistic framework, considering the impact of reactive power and voltage stability concerns.

Fragility estimate of railway bridges due to concrete fatigue

Railway concrete bridges are prone to fatigue collapse, being subjected to multiple cyclic loads during their lifetime. This paper proposes a probabilistic procedure for assessing the fatigue life of concrete railway bridges. The procedure includes the uncertainties related to the concrete fatigue model and the concrete strength by highlighting the relevant uncertainty of existing fatigue models. Therefore, it proposes an enhancement of the fatigue model proposed by the Fib Code 2010 to reduce the modelling error possibly. The model has been calibrated on an extensive data set of normal-strength concrete samples following a Bayesian approach. Parallelly, the parameters have been reduced using a Bayesian step-wise deletion process. The paper uses the proposed probabilistic model to estimate the fragility curves in general cases by considering appropriate ranges for the train velocity, stress ratios and the number of cycles per year. In the second step, the paper applies the procedure to the fragility estimate of a typical prestressed-concrete railway bridge. The bridge response has been estimated using a finite-difference (FD) model. The FD model, simulating train–track–bridge interaction, has been calibrated on the measured displacement response. The analyses have been referred to different scenarios when suitable ranges of train velocities and cycles per year are considered.

Need for nursing care support in cancer patients: Registry-linkage study in Germany

Aim: In Germany, very little is known about the need for assistance and nursing care support among cancer patients after hospitalization. The aim of this study was to describe nursing care support for cancer patients and to analyse whether these patients need more care assistance than other persons in need for care. Methods: This was a registry linkage study conducted in 2011. Cases were identified from the population-based cancer registry for the Muenster District in north-western Germany and in factually anonymised form linked by a semi-automatic probabilistic procedure (the standard procedure of the cancer registry) with medical examination records of patients applying for assistance and nursing care support from the regional statutory health insurance. The application records of 4,029 patients with colon, breast and prostate cancer were compared to a reference group of 13,104 non-cancer patients. Results: In only 41.7% of colon, 45.8% of breast and 37.4% of prostate cancer patients was the malignancy the main underlying diagnostic cause for the application of assistance and nursing care. These patients were on average younger (mean age 71.1 vs. 76.8 years) than the non-cancer reference group, required higher levels of support (79.5 vs. 58.1% “considerable” or higher level care need) and their applications were less likely to be rejected (odds ratios [ORs] 0.26, 0.28, and 0.31, respectively). By contrast, the proportion of successful applications and the level of support granted did not differ between multimorbid cancer patients with other main diagnoses as compared to non-cancer applicants. Conclusion: Patients with colon, breast or prostate cancer do not need per se more nursing care than non-cancer patients. Only if cancer is the main underlying diagnosis for nursing care support, higher levels of support are needed.

A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability

Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting.

Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model

The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017–2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively.

Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.

K-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic Optimization

Deterioration of the searchability of Pareto dominance-based, many-objective evolutionary optimization algorithms is a well-known problem. Alternative solutions, such as scalarization-based and indicator-based approaches, have been proposed in the literature. However, Pareto dominance-based algorithms are still widely used. In this paper, we propose to redefine the calculation of Pareto-dominance. Instead of assigning solutions to non-dominated fronts, they are ranked according to the measure of dominating solutions referred to as k-Pareto optimality. In the case of probability measures, such re-definition results in an elegant and fast approximate procedure. Through experimental results on the many-objective 0/1 knapsack problem, we demonstrate the advantages of the proposed approach: (1) the approximate calculation procedure is much faster than the standard sorting by Pareto dominance; (2) it allows for achieving higher hypervolume values for both multi-objective (two objectives) and many-objective (25 objectives) optimization; (3) in the case of many-objective optimization, the increased ability to differentiate between solutions results in a better compared to NSGA-II and NSGA-III. Apart from the numerical improvements, the probabilistic procedure can be considered as a linear extension of multidimentional topological sorting. It produces almost no ties and, as opposed to other popular linear extensions, has an intuitive interpretation.

A hybrid approach for the seismic vulnerability assessment of the modern residential masonry buildings

In this paper a hybrid approach for the seismic vulnerability assessment of the existing modern masonry buildings is developed. The procedure takes advantage of the standardized designs and technological features of the XX century masonry structures to define a hybrid framework constituted by two different phases: an urban scale one and one at the building level. The methodology is applied at the external outskirt of the city of Florence, where the residential stock is characterized by a significant number of masonry constructions. At the urban level a cognitive research based on archive research and data collection allowed the identification of the architectural and structural features of modern masonry buildings. Then, a selected case study representative of a wider class is chosen for a probabilistic procedure based on nonlinear static analysis and targeted at the derivation of fragility curves. The analytical results have been assumed to calibrate a simplified procedure available in literature and targeted at the definition of bilinear capacity curves. The calibration of the different coefficients was conducted in terms of generic outcomes, defining correlations within architectural elements and their seismic behavior (facades, side walls). The rapid methodology has been finally implemented at the urban scale, and the results of the work exhibit a relevant vulnerability of existing buildings. The simplified methodology overcomes the limits of the empirical methodologies at the urban scale constituting a valid tool for the rapid seismic vulnerability assessment at urban scales of the modern masonry structures of different contexts.

Evaluating the temporal and spatio-temporal niche partitioning between carnivores by different analytical method in northeastern Japan

Temporal and spatio-temporal niche partitioning is an important strategy for carnivore coexistence. Camera-trap data has been analyzed through several methods to assess the temporal and spatio-temporal niche partitioning. However, different analytical approaches used to may evaluate niche partitioning detect different results. In this study, we evaluated the temporal or spatio-temporal partitioning among sympatric medium-sized carnivores, red foxes, raccoon dogs, and Japanese martens, based on three analytical methods—the temporal overlap, temporal co-occurrence, and time-to-encounter analysis—to evaluate. From May to October 2019 and 2020, we obtained the activity of the target species using camera-traps in northeastern Japan. We analyzed the data with the coefficient of temporal overlap, probabilistic co-occurrence analysis, checkerboard score, and multi-response permutation procedures. The results of the assessment of the niche partitioning differed depending on the analytical methods based on temporal and spatio-temporal partitioning. Therefore, we conclude that the choice of analytical approach is important for evaluating the temporal and spatio-temporal niche partitioning.

When all we have is not enough: a search for the optimal method of quantifying inflation expectations

Although inflation expectations are pivotal variables for central banks, they are not directly observable. Therefore, central banks use qualitative survey results to proxy consumer expectations, and their quantification in this manner is often criticized. In this study, we investigate and identify an optimal quantification procedure for survey results based on a set of regression and probabilistic models. Specifically, we seek to identify the method that returns time series that are most highly correlated with an unbiased representative of survey-based expectation: balance statistics. We place additional constraints on this criterion to identify the procedure that returns expectations that are most closely related to consumer intentions (directional co-movements and forecast accuracy). Our sample covers the European Union member states over the period of January 2002 to June 2019. We also test a post-crisis subsample. Our results suggest that different procedures may be optimal for different economies, in line with previous findings on cross-country divergences of expectations formation. However, we find that the most applied assumption of normal distribution does not prove to be the best one. Our recommendation is to apply probabilistic procedures rather than regression methods.

Optimisation modelling tools and solving techniques for integrated precinct-scale energy–water system planning

This invited review paper presents state-of-the-art applications to energy–water system optimisation with multiple technologies, implying possible (non)monetary benefits/impacts for facility custodians in urban precincts. To solve outstanding problems, optimisation modelling frameworks were examined in the quest for undertaking an integrated precinct-scale energy–water system planning. Underlying optimisation methods were identified for addressing nested long/short-term capital/managerial decisions that ensure cost-effectiveness and system reliability, especially selecting diversified investment portfolios to allocate controllable assets across energy and water resources. Despite the ongoing energy–water nexus investigations, the integrated energy–water system planning seems to lack a general optimisation problem formulation that reveal synergies between energy and water. Considering the dynamic and nonlinear nature of energy–water interactions, mathematical properties must be carefully treated for tackling complex integrated energy–water system planning optimisation problems. To optimise integrated precinct-scale energy–water system plans, classical and heuristic techniques can be jointly implemented for solving (re)formulated problems with hard constraints and desirable cost/resource-saving objectives. Critical energy–water system sizing/scheduling tasks were discussed while covering deterministic and probabilistic procedures to support decision-making under uncertainty. Although different combined stochastic programs have been proposed incorporating actual outcomes and empirical measurements as well as prior information and expert knowledge, inherent energy–water demand/supply uncertainties should be realistically characterised by parameterising representable random perturbation sets. To find robust optimal feasible solutions to sensitive parameters during extreme load/cost volatility situations, historical energy–water usage data can be randomly perturbed for uncertainty realisation in prospective scenarios, capturing spatiotemporal correlations that avoid overconservative worst-case expected values. From the scientific paradigm of physics-informed data-driven optimisation modelling, robust approaches can be adopted for severe societal/natural events to advance state-of-the-art applications in integrated precinct-scale energy–water system planning and engineering design.

Low Power Stochastic Neurons From SiO2-Based Bilayer Conductive Bridge Memristors for Probabilistic Spiking Neural Network Applications—Part I: Experimental Characterization

The development of low-power neurons with an intrinsic degree of stochasticity is considered quite important for the emulation of the respective probabilistic procedures that take place within the biological neural networks. While the majority of the reported artificial neuronal configurations relies on the demonstration of well-defined spiking patterns or the realization of complicated neuromorphic properties, there is an urgent need to devise biological-like neurons with arbitrary firing capabilities. Along these lines, we present a novel threshold switching memristor from SiO₂-based bilayer conductive-bridge resistive switching memory (CBRAM) and Pt nanoparticles (NPs) used as a bottom electrode (BE) in order to implement robust stochastic neuron activity accompanied with steep transition slope [&#x007E;0.75 mV/dec(A)], large OFF-state (&#x007E;<inline-formula> <tex-math notation="LaTeX">$10^{11} \Omega $ </tex-math></inline-formula>), low switching voltage (&#x007E;200 mV), and ultrafast turn-on speed (&#x007E;50 ns). Moreover, probabilistic leaky-integrate-and-fire (LIF) neuron properties were attained by employing a simple <i>RC</i> circuit as well as tunable integrate-and-fire (IF) properties from stand-alone threshold switching elements, which are of great significance for the development of artificial spiking neuromorphic networks.