Probabilistic Techniques Research Articles

A novel blockchain-based anonymous roaming authentication scheme for VANET

With the widespread application of vehicular ad-hoc networks, ensuring secure and seamless cross-regional roaming for mobile users and obtaining corresponding services has become a focal point. However, designing an efficient and secure roaming authentication protocol is challenging due to the confidentiality and privacy issues that data transmission during the roaming authentication process may cause and the limited computational capabilities of mobile devices. Researchers have proposed many security-oriented schemes to address this thorny challenge. However, many state-of-the-art schemes need help meeting various security requirements and facing privacy leakage and single points of failure. Recently, Xue et al. proposed a distributed authentication scheme for roaming services in mobile vehicular networks based on smart contracts. Regrettably, it is noted that their scheme is vulnerable to ephemeral key leakage attacks. Further, we present a blockchain-based anonymous roaming authentication scheme called BARA, which changes how session keys are generated and significantly reduces on-chain storage costs using probabilistic data structure techniques. We utilize Scyther and Burrows–Abadi–Needham (BAN) logic to prove the security of BARA and compare it with similar protocols in terms of computation, communication, and revocation check. The analysis results demonstrate that BARA achieves a good balance between security performance and execution efficiency.

Gathering on a circle with limited visibility by anonymous oblivious robots

A swarm of anonymous oblivious mobile robots, operating in deterministic Look-Compute-Move cycles, is confined within a circular track. All robots agree on the clockwise direction (chirality), they are activated by an adversarial semi-synchronous scheduler (SSYNCH), and an active robot always reaches the destination point it computes (rigidity). Robots have limited visibility: each robot can see only the points on the circle that have an angular distance strictly smaller than a constant ϑ from the robot's current location, where 0<ϑ≤π (angles are expressed in radians).We study the Gathering problem for such a swarm of robots: that is, all robots are initially in distinct locations on the circle, and their task is to reach the same point on the circle in a finite number of turns, regardless of the way they are activated by the scheduler. Note that, due to the anonymity of the robots, this task is impossible if the initial configuration is rotationally symmetric; hence, we have to make the assumption that the initial configuration be rotationally asymmetric.We prove that, if ϑ=π (i.e., each robot can see the entire circle except its antipodal point), there is a distributed algorithm that solves the Gathering problem for swarms of any size. By contrast, we also prove that, if ϑ≤π/2, no distributed algorithm solves the Gathering problem, regardless of the size of the swarm, even under the assumption that the initial configuration is rotationally asymmetric and the visibility graph of the robots is connected.The latter impossibility result relies on a probabilistic technique based on random perturbations, which is novel in the context of anonymous mobile robots. Such a technique is of independent interest, and immediately applies to other Pattern-Formation problems.

Optimizing IoT Networks in Smart Agriculture Using Probabilistic Models and Machine Learning Algorithms

This study aims to enhance the performance of Internet of Things (IoT) networks in smart agriculture by integrating probabilistic models and machine learning algorithms. The research addresses the need for efficient, reliable, and scalable IoT infrastructures to support precision farming and sustainable agricultural practices in increasingly complex and data-intensive farming environments. A multi-faceted approach was employed, combining probabilistic modeling and machine learning techniques. IoT sensor networks were simulated using real-world agricultural data, supplemented by a small-scale field deployment. Bayesian networks and Hidden Markov Models were applied to capture system dynamics, while Random Forest, Support Vector Machines, and Long Short-Term Memory networks were used for pattern recognition and predictive analytics. Network performance was evaluated using metrics including latency, throughput, packet loss rate, energy efficiency, scalability, reliability, and data accuracy. The integrated approach significantly improved IoT network performance in agricultural settings. Key improvements include: 25% reduction in latency, 35% increase in throughput, 60% decrease in packet loss rate, 30% improvement in energy efficiency, and 6.5% increase in data accuracy. Adaptive routing algorithms, informed by machine learning predictions, were particularly effective in managing network load during peak agricultural seasons, reducing latency by 18% during these periods. The study relied primarily on simulations and limited field trials. Comprehensive real-world implementation across diverse agricultural environments and larger scale deployments is necessary to fully validate the findings. The research did not address potential cybersecurity challenges in optimized IoT networks, which remains an important area for future investigation.

Comprehensive Analysis Using Probabilistic Linguistic Group Decision-Making and MEREC Technique With Sustainable Development Evaluation in Higher Education

University administrators should organize and coordinate various affairs, scientifically utilize resources such as budget, teaching equipment, and faculty, continuously improve teaching quality, and ensure the sustainable development of educational management. The sustainable development evaluation in higher education management is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (ExpTODIM) and PROMETHEE technique was employed to put forward the MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a technique for characterizing uncertain information during the sustainable development evaluation in higher education management. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed to put forward the MAGDM under PLTSs. The MEREC technique is employed to obtain the weight values under PLTSs. Finally, a numerical example for sustainable development evaluation in higher education management is put forward to validate the ExpTODIM-MABAC technique.

A minute-resolution downscaling algorithm for high-resolution global irradiance time series

Numerous studies have demonstrated the significant impact of the resolution of solar irradiation data on the outcomes of hourly production models. Accurate integration of photovoltaic (PV) systems sometimes demands a high-resolution global horizontal irradiance (GHI) time series to capture the rapid fluctuations in PV power output induced by swift irradiance changes. Most of the available databases provide data at hourly resolution, leading to a lack of accuracy in PV simulations. Those existing hourly averages of global horizontal irradiance in open sources fail to represent this volatility adequately, especially when PV systems are coupled with fast ramp rate technologies. In the present work, an easy-to-use algorithm is implemented to synthesize high-resolution GHI time series from hourly averaged and clear sky irradiance datasets. By employing Linear interpolation, a technique that helps to achieve the desired time resolution and afterward computing critical factors, the algorithm identifies periods characterized by short-term weather phenomena, thus creating a high-resolution time series that accurately represents these dynamics. Avoiding the probabilistic components and machine learning techniques conserves computational power and reduces calculation time, but this comes at the cost of reduced fidelity in reproducing the results. Improving accuracy in PV simulations is not always directly related to reproducing real phenomena, but enhancing the amount of information contained in the data is sufficient. This study’s approach enhances user-friendliness and facilitates seamless integration into existing energy modeling frameworks, aiming for representation with sub-hourly time steps. The algorithm’s effectiveness is demonstrated by applying the model to hourly averaged data to revert them to a one-minute time step, and finally comparing the synthetically produced one-minute GHI data to the original measured data. The comparative analysis between synthesized and measured data demonstrated a strong agreement, with normalized mean bias error (MBE) values ranging between 1.8% and 9.6% and normalized root mean square error (NRMSE) values between 2.7% and 16.1%, depending on weather conditions. Additionally, the coefficient of determination (R2) consistently remained above 0.64. Successful algorithm validation makes our algorithm suitable for use in meteorological datasets and locations, with similar climatic characteristics.

Addressing large scale patterns of no-flow events in rivers: An in-depth analysis with Achelous software

Establishment of hydrological criteria that could serve as guidelines for addressing intermittency is not an easy task. However, efforts in the last years are yielding promising advancements in this direction. Scientists have been working to unravel the complexities of intermittency dynamics. In this study, we aimed to investigate the characteristics of naturally intermittent water systems by exploring the occurrence of no-flow events. A hydrological model was employed to generate streamflow data. Our analysis encompassed a thorough examination of nineteen flow regime metrics, estimated across 2064 subbasins, with 190 of these meeting the criterion for intermittency. A custom Python library was deployed to automate the quantification of no-flow events, aligning with the concept of critical thresholds. Using the probabilistic t-distributed Stochastic Neighbor Embedding technique to capture complex patterns, three clusters were emerged. The first one was characterized by a low probability of no-flow events and a small number of no-flow events per year, the second cluster was abundant in no-flow events and demonstrated a tendency towards longer annual recession time scales. The third cluster stands out due to the significant variance in the duration of no-flow events. Concerning the time variability of the no-flow events, we concluded that they predominantly occurred during August. Both long- and short-term quantification of no-flow events should be under consideration so as to harmonize the naturally intermittent waterways with the water use requirements and the potential consequences of not meeting them. Future research should prioritize the investigation of hydroecology and ecohydrology in relation to streamflow dynamics and ecosystem interactions. By doing so, we can elevate our comprehension of how intermittent water systems function and their significance within the broader ecological context.

Sharp analytic version of Fefferman's inequality

Let T be a unit circle. Assume further that f is an element of the Hardy space H1(T) and g belongs to the analytic BMO space on T. The paper contains the identification of the optimal universal constant C in the estimate|12π∫Tf(ζ)‾g(ζ)dζ|≤C‖f‖H1(T)‖g‖BMO(T). Actually, the inequality is studied in the stronger form, involving the Littlewood-Paley function on the left and the sharp maximal function of g on the right. The proof rests on the construction of an appropriate plurisuperharmonic function on a parabolic domain and the application of probabilistic techniques.

Adaptive Fault‐Tolerant Multiplicative Attitude Filtering for Small Satellites

ABSTRACTThis study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs).

A comparative study of different deep learning methods for time-series probabilistic residential load power forecasting

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy.

Mangrove Ecotourism in East Java, Indonesia: A Memorable Tourist Experience that Awakens Revisit Intention

As outdoor tourism grows amidst competition in the tourism industry, ecotourism must offer tourists Memorable tourist experiences (MTEs). Memorable experiences shared on social media can be re-shared and have the potential to go viral. This study drew from previous empirical research and explored the mediating role of electronic word of mouth (eWOM) and MTEs on revisit intention in mangrove ecotourism in East Java, Indonesia. Samples were selected using a probabilistic technique among mangrove ecotourism tourists in Trenggalek District and Surabaya City, East Java, Indonesia. 250 questionnaires were distributed to respondents, out of which 160 were returned and deemed complete. The structural equation model (SEM) tested the hypothesis with the Partial Least Squares (PLS) approach. This research concludes that positive experiences left by tourists can directly influence revisit intention, but this influence is more significant if the experience is staged through eWOM. This research has implications for theory in understanding the role of MTEs and eWOM on revisit intention, while managerially it has implications for developing destination marketing strategies to create maximum MTEs and eWOM to generate revisit intention. Keywords: ecotourism, eWOM, memorable tourist experiences, revisit intention

Energy management system for a workplace PV-EV charging station with active and reactive power dispatch of EVs considering transformer aging and uncertainties

This study presents a new energy management system (EMS) for a grid-tied photovoltaic (PV) – electric vehicle (EV) integrated workplace charging station. The proposed EMS is developed as a convex stochastic mixed-integer quadratically constrained problem (MIQCP) to minimize the expected apparent power demand while limiting the distribution transformer's accelerated aging and satisfying the EV driving needs. This is accomplished by scheduling real and reactive powers of EVs in real-time through Vehicle-to-grid (V2G) mode. The inherent diurnal uncertainties and seasonal variations associated with the workplace non-EV load, PV generation are incorporated using probabilistic and hierarchical clustering techniques, respectively. The implementation of the developed EMS under receding horizon model enables real-time operation by adapting to dynamic arrivals of EVs. The effectiveness of the proposed EMS is validated through numerous simulations from the view point of the distribution system operator (DSO), charging station owner (CSO), and EV prosumer. The results indicate a substantial reduction in the peak demand, minimized transformer's loss-of-life (LoL), and operating cost saving while satisfying the EV driving needs in comparison to uncoordinated charging method.

Different diseases, different needs: Patient preferences for gene therapy in lysosomal storage disorders, a probabilistic threshold technique survey

BackgroundGene therapy is currently in development for several monogenetic diseases including lysosomal storage disorders. Limited evidence is available on patient preferences for gene therapy in this population. In this study, we compare gene therapy-related risk tolerance between people affected by three lysosomal storage diseases currently faced with different therapeutic options and prognoses.MethodsA survey including the probabilistic threshold technique was developed in which respondents were asked to choose between gene therapy and the current standard of care. The attributes included to establish participants’ risk tolerance were previously identified in focus groups of affected people or their representatives, namely: risk of mild side effects, severe side effects, the need for additional medication, and the likelihood of long-term effectiveness. The survey was distributed among people receiving outpatient care for type 1 Gaucher disease (good prognosis with current treatment options), Fabry disease (varying prognosis with current treatment options, XY-genotype on average more severely affected than XX), and parents representing people with severe forms of mucopolysaccharidosis type III A/B (poor prognosis, no disease-specific therapy available).ResultsA total of 85 surveys were completed (15 Gaucher disease respondents, 62 Fabry disease respondents (17 self-identifying male), eight parents of ten people with mucopolysaccharidosis type III). Disease groups with higher disease severity trended towards higher risk tolerance: Gaucher disease respondents were most cautious and predominantly preferred the current standard of care as opposed to MPS III representatives who were more risk tolerant. Respondents with Fabry disease were most heterogeneous in their risk tolerance, with male participants being more risk tolerant than female participants. Long-term effectiveness was the attribute in which respondents tolerated the least risk.ConclusionsPeople affected by a lysosomal storage disease associated with a poorer prognosis and less effective current treatment options trended towards more risk tolerance when choosing between gene therapy and the current standard of care. This study shows the importance of involvement of patient preferences before and during the development process of new treatment modalities such as gene therapy for rare diseases, to ensure that innovative therapies align with the wishes and needs of people affected by these diseases.

Stochastic stability of random stacking of blocks

The paper explores the stability of a tower obtained by stacking identical rectangular blocks on top of each other with an inherent randomness due to slight positional offsets between successive blocks. With probabilistic modeling techniques, the diffusive behavior of the stacking process is studied and the collapse is seen as a first passage time problem. In the considered model, alignment errors are idealized as independent Gaussian random variables with zero mean and given standard deviation. We derive expressions for the joint probability density functions of block positions, and analyze their correlation. The study extends to the stochastic stability of a stack of given height, by exploring the statistical characteristics of the center of gravity of the part of tower above each block. Eventually, the probabilistic analysis of collapse is developed to quantify the statistics of the number of blocks that can be heaped up before the tower topples. Although this problem may initially appear playful, it offers an illustrated introduction to first passage problems on a non homogenous process. From a practical standpoint, this analysis offers a simple understanding of the influence of alignment errors on the overall stability of a stack, which may find several fields of application.

AI-powered decision making for road safety optimization under probabilistic linguistic Sugeno-Weber aggregation information

Traffic accidents, a global concern, pose threats to lives and carry substantial economic and societal burdens. This paper explores the innovative integration of artificial intelligence (AI) into the ambiance of Multi Criteria Decision Making (MCDM) to address the challenge. We investigate AI's potential to enhance road safety globally, utilizing data analysis, predictive modeling, and intelligent traffic management systems. Our goal is to revolutionize accident prevention by optimizing decision-making processes and promoting safer and more efficient road environments. In the realm of information aggregation and fusion, growing interest among researchers has centered on probabilistic linguistic expression sets, which adeptly aggregate uncertain data. This article's primary aim is to explore methodologies for aggregating information within a probabilistic linguistic environment. To achieve this goal, we have introduced procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs), rooted in both the product and sum of SW operations. Consequently, we have crafted a range of probabilistic linguistic aggregation techniques, encompassing the probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG), followed by weighted as well as ordered aggregation operators like PLSWWA and PLSWWG, PLSWOWA and PLSWOWG. By harnessing SW τ-norm and τ-conorm, we have developed versatile aggregation tools that facilitate information reinforcement. Through the utilization of proposed operators, we have presented strategies for effectively integrating probabilistic linguistic term sets (PLTs) in the context of MCDM. We have compared our suggested procedures with the TOPSIS approach and elucidated the diverse features inherent to these operators.

Seismic quantitative interpretation of diagenetic processes: A study in Brazilian pre-salt carbonate reservoirs

The Brazilian pre-salt carbonate province is a scientific frontier that has been subject of extensive research and academic discussion. Santos basin carbonates show that reservoir quality is strongly conditioned by the characteristics of the depositional environment and its associated diagenetic history and alterations. Dolomitization and silicification are two of the most common diagenetic processes in Brazilian carbonates, and the combination of sedimentary and diagenetic complexity leads to a much more heterogeneous reservoir, especially complicated for geoscientists trying to mimic these geological features in terms of static and dynamic modelling. This study proposes a methodology for seismic derived diagenetic properties calculation through seismic quantitative interpretation and classification of inverted P- and S-impedance volumes applied to Brazilian Pre-Salt carbonate reservoirs. Our proposition is to establish relations between petroelastic properties and diagenetic features, first in the well-log domain and, later, in the seismic domain through different quantitative probabilistic techniques. Using the available seismic data and probabilistic approaches through stochastic inversion and Bayesian facies classification, we can interpret the occurrence of diagenetic silica and dolomite in 3D volumes. The volumetric analysis generated through the presented methodology can provide much more insights into the sedimentary history of the field, especially for static and dynamic porosity and permeability modelling.

Comparative static analysis of functionally graded porous curved beams: Deterministic versus stochastic approaches

This study presents a comprehensive exploration of the comparative static analysis of functionally graded porous curved beams, examining deterministic versus stochastic approaches. The material randomness is incorporated using a stochastic finite element model. The formulation employed in this study utilizes three-noded elements and combines a higher-order shear deformation theory (HSDT) with the probabilistic first-order perturbation technique. The investigation focuses on an FGM curved beams incorporating various porosity models, including even, uneven, and sinusoidal distributions. The FGM porous curved beam is subjected to a uniform distributed load, and the study aims to determine the stochastic bending responses under these conditions. Additionally, various parameters influencing bending response, such as boundary conditions, geometric configurations, volume fraction indices, and porosity distributions, are examined. The uncertain bending responses are assessed in terms of material stochasticity using a stochastic finite element model, with validation through Monte Carlo simulation. Subsequent research endeavors within this field may investigate the impact of random fluctuations on both geometry and boundary conditions.

Machine learning-based approach for assessing the seismic vulnerability of reinforced concrete frame buildings

Recent artificial intelligence (AI) advancements have significantly impacted the field of structural engineering by offering new solutions to enhance safety, efficiency, and cost-effectiveness in the design, construction, and maintenance of infrastructure and urban areas. This study has investigated a machine learning-based approach to estimate the seismic vulnerability of reinforced concrete (RC) building frames. The study utilized a probabilistic technique to assess the simulation of a 4-story RC building frame in the face of epistemic uncertainty. Data was collected using the Monte-Carlo approach, which created a machine learning (ML) model for predicting structural damage. The problem was formulated as both a regression-based and a classification-based model. An artificial neural network (ANN) feed-forward network architecture model was used to solve the classification problems. The optimal number of neurons in the hidden layer was determined to produce the best estimation model. Three different models were combined for the regression model, including LASSO regression, random forest, and gradient boosting, by implementing the stacking generalization. The investigation results indicate that the stacked predicted model exhibits less variance than other ML models. The classification and regression-based algorithms can forecast damage states with a high degree of accuracy, ranging from 87 to 95 percent. In conclusion, the study has demonstrated the effectiveness of machine learning techniques for predicting the seismic vulnerability of RC building frames. With the continued advancement of AI and big data, these methods will likely play a crucial role in the structural engineering field.

A novel probabilistic load shifting approach for demand side management of residential users

Demand side management is a beneficial set of techniques which leads improving consumption profile of residential users. Practice of dynamic pricing and appropriate shifting of electrical appliances at home are important tasks at that point. In this study, a novel probabilistic load shifting strategy is proposed and its beneficial effects on economic and technical parameters are demonstrated. For that purpose, a survey on energy consumption is applied for residential users and its results are used for creating a probabilistic model. 300 virtual residents are created in this way and a probabilistic simulation technique is developed. Next, the developed simulation technique is applied for creation of the monthly consumption data. Finally, it is demonstrated that electricity bills are decreased significantly when the proposed approach is applied. Besides that, peak-to-average ratio which is calculated by considering all residential users is also decreased as a result. In conclusion, when the results are compared with the ones of conventional constant tariff, time-of-use tariff with three time zones and a deterministic load shifting strategy, it is observed that the best results of economic and technical parameters in question are achieved by using the proposed probabilistic load shifting strategy together with dynamic pricing mechanism involved.

Continual knowledge graph embedding enhancement for joint interaction-based next click recommendation

Knowledge Graph Embedding (KGE) based deep neural networks contribute to recommender systems in diverse application scenarios. However, Catastrophic Forgetting (CForg) significantly degrades their performance. Although exemplar replay is commonly adopted as a possible remedy to alleviate the intensity of CForg, a trade-off between performance and complexity occurs in this process. Therefore, in this work, we introduce Continual Knowledge graph embedding enhancement for joint Interaction-based Next click recommendation (CKIN) to defy the CForg and assuage the complexity. Typically, we introduce the Semantic Relevance Estimation (SRE) technique to ensure information relevance by filtering out irrelevant-data and reducing the space complexity. We introduce the SRE-enhanced deep probabilistic technique to probably replay the most relevant exemplars to defy the CForg and reduce the time complexity. Moreover, we introduce the integration of locality-preserving loss into the KGE framework to optimize the loss. In substantial experiments on real-world datasets, CKIN outperforms the baseline methods by effectively meeting the highlighted challenges.

Effectiveness of probabilistic contact tracing in epidemic containment: The role of superspreaders and transmission path reconstruction.

The recent COVID-19 pandemic underscores the significance of early stage nonpharmacological intervention strategies. The widespread use of masks and the systematic implementation of contact tracing strategies provide a potentially equally effective and socially less impactful alternative to more conventional approaches, such as large-scale mobility restrictions. However, manual contact tracing faces strong limitations in accessing the network of contacts, and the scalability of currently implemented protocols for smartphone-based digital contact tracing becomes impractical during the rapid expansion phases of the outbreaks, due to the surge in exposure notifications and associated tests. A substantial improvement in digital contact tracing can be obtained through the integration of probabilistic techniques for risk assessment that can more effectively guide the allocation of diagnostic tests. In this study, we first quantitatively analyze the diagnostic and social costs associated with these containment measures based on contact tracing, employing three state-of-the-art models of SARS-CoV-2 spreading. Our results suggest that probabilistic techniques allow for more effective mitigation at a lower cost. Secondly, our findings reveal a remarkable efficacy of probabilistic contact-tracing techniques in performing backward and multistep tracing and capturing superspreading events.