Behavior Of System Research Articles

Experimental and theoretical analysis of photovoltaic performance and thermal behavior for bifacial PV-Trombe wall system with reversible louvers in summer

The traditional monofacial PV-Trombe wall can harness both solar photovoltaic (PV) and thermal energy in buildings, but its performance is hindered by the need for transparent PV glass panels, which reduces PV power generation performance. To address this issue, this paper introduces a novel bifacial PV-Trombe Wall that leverages the rear-side power generation capability of bifacial PV cells. Theoretical analysis of the PV power generation performance of the system highlighted key factors and relationships, indicating that at a coverage rate of 0.5, the increase in its power generation compared to traditional systems reaches a maximum of 17.46 %. Furthermore, an experimental setup was devised and tested to explore the system's PV performance and thermal behavior during the summer season. The experimental results show that the reflective function of reversible louvers had successfully reduced the maximum temperature and rate of temperature rise on the attached wall, effectively alleviating the issue of solar heat absorption in buildings during summer. Besides, the system with reversible louvers improved the overall PV efficiency from 15.40 % to 16.17 %, with a total power generation increase of 5.04 %. Therefore, the system can enhance PV power generation and efficiently decrease the building's cooling energy consumption during the summer.

Leveraging Artificial Intelligence and Machine Learning for Detecting Fake Accounts on Social Media

Detecting fake accounts is a significant challenge for social media platforms, e-commerce sites, and online services. Malicious users create fake profiles to spread misinformation, commit fraud, or manipulate system behavior. Traditional rule based methods often fail to catch sophisticated fake accounts that resemble genuine user activities. This paper examines the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the detection of these deceptive accounts. Using KNN model and Gradient Boosting algorithm offer a more robust solution to identifying and combating fake accounts in Social Media Platform.

Coupling Vibration Characteristics and Wind-Induced Responses of Large-Span Transmission Lines Under Multi-Dimensional Wind

Transmission lines, crucial for power and urban infrastructure, are vulnerable to wind damage; this paper addresses research gaps in tower-line systems under multi-dimensional wind loads and aerodynamic damping. By incorporating multi-dimensional aerodynamic damping and conducting comprehensive multi-dimensional wind response analysis, it examines parameters like ground roughness and wind attack angles that significantly influence the tower responses, offering a holistic understanding of system behavior under real wind conditions. This study analyzes wind-induced responses of a large-span Chinese transmission line using a finite element model (FEM) with three spans and two towers. This paper conducts modal analyses of a single tower and the tower-line system, comparing their vibration characteristics under one- and multi-dimensional wind loads generated via harmonic superposition methods. Incorporating the multi-dimensional aerodynamic damping, the impact of wind velocity, ground roughness, and wind attack angle on the tower-line system is analyzed through time-history results and gust response factor. The findings reveal that under the premise of multi-dimensional aerodynamic damping, multi-dimensional wind loads significantly amplify responses compared to one-dimensional loads. As wind speed, ground roughness, and wind attack angle increase, responses are elevated, causing complex changes in gust response factors, underscoring the importance of considering multi-dimensional wind loads.

Assessment of the humoral immunity against diphtheria, tetanus, and hepatitis B among children with acute lymphocytic leukemia

Background: Approximately all systemic therapies for childhood affect the immune system. The behavior of the immune system in leukemia patients following chemotherapy is not yet clearly defined. The probability of vaccination failure and the need for revaccination remain challenging for these patients. Aim: To evaluate the humoral immunity against diphtheria, tetanus, and hepatitis B in children with acute lymphocytic leukemia (ALL) immediately and 6 months after chemotherapy. Materials and Methods: In the present prospective cohort study, 21 patients with ALL referred to Mofid Children’s Hospital were studied immediately and 6 months after chemotherapy. Serum samples were collected from patients, and the levels of immunoglobulins (IgG, IgM, IgE, and IgA) antibodies against diphtheria, tetanus, and hepatitis B were determined using specific enzymelinked immunosorbent assay kits. The obtained data were analyzed using Statistical Package for Social Sciences 21 software. Results: A total of 13 males and 8 females with an average age of 8.6 ± 2.5 years were included in the present study. Six months after chemotherapy, the mean level of IgG, IgM, IgE, and IgA displayed an increase of 563.1 units in IgG, 11 units in IgM, 11.3 units in IgE, and 5 units in IgA levels. Moreover, data revealed that 6 months after chemotherapy, the mean level of IgG antibodies displayed an increase of 7.09, 3.43, and 1.03 units against hepatitis B, diphtheria, and tetanus, respectively. A significant relationship was found between the antibody level against diphtheria and the age group of the patients (p = 0.003). Conclusion: Humoral immune status was boosted after 6 months of chemotherapy, though all patients had some extent of lasting immune dysfunction. We indicate that survivors of childhood cancer have ongoing humoral immunological defects and may remain at risk for infectious complications after completion of therapy. Relevance for Patients: The present study indicated that systemic therapies for pediatrics with leukemia affect the immune system. Pediatrics with leukemia may remain at risk for infectious complications after completion of therapy.

Psychosocial Factors Influencing Voting Behavior

This study delves into the voting behaviour in the context of Indian electorate, shedding light on understanding it in the relatively new dimension, where the focus is primarily on psychological factors and social factors (demography) influencing the voting behaviour, which further sheds light on understanding the broader issues, like low voter turnout in India compared to other democracies worldwide, understanding of opinion polls and exit surveys and other evolving unethical Influences which are centred around mental process of voters cognition, which could create a new dimension of understanding the need for neuro rights of the voters, where the motive of free and fair elections can be achieved. This pioneering attempt to comprehend the psycho - social factors influencing voting behaviour in the Indian electoral system is groundbreaking as it has never been attempted before. In an election scenario, voters are exposed to a myriad of stimuli related to political candidates, parties, promises, and discussions, prompting selective exposure, perception, and integration processes to form the basis for their voting decisions. The study also draws on the voter turnout theory, highlighting the role of social reference groups and their impact on voter behaviour. Furthermore, it emphasizes the under explored domain of psychological and social factors in explaining political attitudes and voting behaviour among the mass public from relative unexplored dimensions, asserting that psychological traits directly influence the voting behaviour. With the Indian electoral system becoming increasingly dynamic due to diverse societal participation and steadily growing influential factors the need for study arises strongly, This research attempt is being carried forward by examining personality through the DISC personality assessment tool and quantifying the voting behaviour through the theory of planned behaviour questionnaire, the study seeks to discern the connections between demographic variables, personality traits and voting behaviour (intention). The components of voting behaviour were formed on the pivotals of theory of planned behaviour (TBH) such as behaviour beliefs, control beliefs and normative beliefs which more precisely create assessment area of voting behaviour intention. The study employs questionnaire with a five - point Likert scale to measure respondents' agreement with statements related to voting behaviour. The research's objectives include exploring the influence of a voter's personality and demographic variables on their voting behaviour and the influence of specific personality types on various facets of voting behaviour. The study's hypotheses posit that voting behaviour is influenced by psycho - social factors, particularly personality and demographic variables, and that different personality types may alternatively influence the voting behaviour intention. In conclusion, this study aims to elevate our comprehension of this context to a higher standard.

Co-substrate model development and validation on pure sugars and corncob hemicellulosic hydrolysate for xylitol production

A co-substrate model of Candida tropicalis TISTR 5306 cultivated in 10 - 100 g/L xylose and 1 - 10 g/L glucose at the ratio of 10:1 was developed based in part on modified Monod equation. The kinetic parameters include substrate limitation as well as substrate and product inhibitions with inclusion of threshold values. A general good fitting with average RSStotal, R2, and MStotal values of 162, 0.979, and 10.8, respectively, was achieved between ten simulated profiles and experimental kinetics data. The implementation of developed model on xylitol production from non-detoxified corncob hemicellulosic hydrolysate resulted in relatively good agreement with RSStotal, R2, and MStotal values of 368, 0.988, and 24.5, respectively. The developed model can be applied to predict microbial behavior in batch xylitol production system using hemicellulosic hydrolysate over a xylose range of 10 - 100 g/L and provide useful information for subsequent design of fed-batch and continuous systems to achieve the efficient sustainable resource management of this agricultural and agro-industrial waste.

Unsteady Free Convection of Nanofluid Past an Infinite Vertical Sheet Under the Effects of Inclined Magnetic Field and Cogley Radiation

Background: An infinite vertical sheet is the medium of inquiry for studying the copper- water nanofluid behavior with an inclined magnetic field and Cogley radiation. The research contains significant aspects. The use of this nanofluid is of great practical importance, especially in the areas of energy efficiency, thermal management, radiative cooling for space technology, environmental impact reduction, and improving heat transfer efficiency in electronic device cooling operations. Moreover, the research enhances the understanding and manipulation of sophisticated materials. The uttermost significance of this resides in its capacity to uncover issues inherent in the stages of planning, developing, analyzing, and improving manufacturing processes, possibly securing new solutions via patent protection. Objective: This research aims to provide a comprehensive analysis of the unsteady natural convection of a nanofluid along an infinite vertical sheet. The study focuses on understanding how an inclined magnetic field, Cogley radiation, heat source, and varying nanoparticle volume concentrations impact the velocity, thermal, and concentration profiles of the nanofluid. Additionally, the investigation seeks to evaluate the Nusselt number, Sherwood number, and skin friction coefficient across different concentration profiles to derive meaningful insights. Methods: The Laplace transform (L.T.) method, in combination with the MATLAB symbolic computing program, is used to develop numerical solutions for the main boundary value issues. The Laplace transform technique is used to clarify dimensionless partial differential equations (PDEs) and their limiting situations. An in-depth study is conducted on the profiles of momentum, energy, and concentration for various kinds of nanofluids with changing volume concentrations of nanoparticles. This research provides a comprehensive examination and also identifies prospective prospects in this field that might be protected by patents. Result: The work provides an in-depth understanding of how inclined magnetic fields, Cogley radiation, heat generation, and nanoparticle volume concentration affect the velocity, energy, and mass profiles of the nanofluid. Graphical representations depict these effects, offering a visual comprehension of the system's behavior. The Nusselt number and skin friction coefficient are determined by analytical and numerical methods. These findings demonstrate that the Nusselt number and skin friction coefficient have an inverse relationship with changes in concentration profiles. Conclusion: The work improves our comprehension of the fluctuating natural convection of nanofluids across an unbounded vertical surface, taking into account variables such as an inclined magnetic field, heat source, Cogley radiation, and nanoparticle volume concentration. The practical implications of the discoveries, together with the analytical and numerical results, enhance our comprehension of the dynamics of nanofluid systems. Ensuring the protection and widespread distribution of patented technology is essential for promoting sustainable advancements.

Model‐based systems engineering and safety assessment: A workflow for mechatronic systems design

AbstractMechatronic systems become ever more complex because of their increasing number of interconnected safety critical components and sophistication. MBSE (Model‐based Systems Engineering) and MBSA (Model‐Based Safety Assessment) are the most commonly adopted approaches to deal with the design and safety analysis of mechatronic systems. Unfortunately, both approaches are normally adopted separately, especially in the earlier phases of system design, thus leading to a lack of communication between system engineers and the safety team. This work aims to fill that gap at a high level, that is, through process interaction. This paper proposes an enhanced V‐model for the design of safety‐critical mechatronic systems. It relates a system development process with specific safety assessment methods. Specifically, the proposed workflow details exchange flows between the RFLP (Requirements, Functional, Logical, Physical) method, the FHA (Functional Hazard Analysis), the FMEA (Failure Mode and Effects Analysis), the MBSA and simulation, and the FTA (Fault Tree Analysis). These analyses are complemented with multiphysics modeling and simulation to observe system behavior in functional and failure scenarios, with the aim of requirements verification. The design workflow has been applied to a winged Unmanned Aerial Vehicle to apply the parallel process and the necessary interaction of MBSE and MBSA approaches. The information flows between the individual activities proved effective for designing a safe system before the verification phase. The main benefit of the proposed workflow is providing both the design and safety team with some interaction points, thus avoiding a lack of safety‐critical analysis in the early phases of system design.

From Monitoring to Observability: A Paradigm Shift in IT Operations

This article explores the paradigm shift from traditional monitoring to observability in IT operations, driven by the increasing complexity of modern distributed systems, data overload, evolving cybersecurity threats, and the need for proactive problem-solving. Observability platforms, built on the four pillars of events, metrics, traces, and logs, provide comprehensive insights into system behavior and performance. Adopting observability practices offers numerous benefits, including automated discovery of system components, dependency mapping, topology visualization, correlative intelligence, faster root cause diagnosis, and more accurate anomaly detection through auto-baselining. Organizations that embrace observability report significant improvements in incident response times, system reliability, and overall operational efficiency. The article guides successfully transitioning to an observability-focused approach by embracing automation, fostering a culture of observability, implementing the right tools, and leveraging AI and machine learning for advanced analytics and insights.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Dynamic Analysis of a Vehicle–Bridge System Under Excitation of Random Road Irregularities

This paper presents a comprehensive study of the dynamic response analysis of vehicle–bridge coupled systems, with detailed simulation methods for the vehicles, bridges, and wheel–road coupling relationships. The simulation of the entire vehicle–bridge coupling system is carried out using the open-source finite element analysis platform OpenSees. A novel three-dimensional wheel–road coupling element is introduced to model the interactions between the wheel and road nodes. This element facilitates precise computation of the dynamic responses within the vehicle–bridge coupled system, including both vehicle and bridge behaviors, along with the interaction forces between the wheels and the bridge surface. The coupling element consists of a wheel node and all potential road nodes on the bridge surface that the wheel may traverse. This configuration preserves the finite element model of the entire vehicle–bridge coupled system throughout the vehicle’s movement, thereby improving the efficiency of numerical simulations of vehicle–road interactions. The study accounts for the impact of random road irregularities on the dynamic responses of both the vehicle and the bridge. These irregularities are treated as input parameters for the wheel–road coupling element rather than being accounted for through the wheel–road interaction constraint equations, thereby improving the convenience of simulating random road irregularities.

Investigation of nonlinear dynamic behaviors of vertical rotor system supported by aerostatic bearings

Abstract A common issue associated with gas bearing-rotor systems is the tendency to generate self-excited vibrations, leading to instability. To address this problem, the fluid-structure coupling model of an aerostatic bearing-rotor system is established in this paper. Then a hybrid method combining the finite difference method and direct integration method, is employed to solve the bearing lubrication equation and rotor motion equation simultaneously. Furthermore, based on the orbit of rotor center,frequency spectrum diagram, Poincaré map, waterfall diagram and bifurcation diagram, the effects of rotational speed, rotor mass, orifice diameter and nominal clearance on the nonlinear dynamic behaviors of the bearing-rotor system are investigated. The results indicate that the system exhibits rich nonlinear behaviors with increasing rotational speed and rotor mass, including the occurrence of typical half-speed whirl. However, the nonlinear vibrations of the system can be restricted by selecting appropriate bearing structural parameters, providing theoretical guidance for the design of aerostatic bearing-rotor systems.

Spatiotemporal implicit neural representation as a generalized traffic data learner

Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employed to directly map coordinates to traffic variables. To unravel the entangled spatial–temporal interactions, the variability is decomposed into separate processes. We further enable modeling in irregular spaces such as sensor graphs using spectral embedding. Through continuous representations, our approach enables the modeling of a variety of STTD with a unified input, thereby serving as a generalized learner of the underlying traffic dynamics. It is also shown that it can learn implicit low-rank priors and smoothness regularization from the data, making it versatile for learning different dominating data patterns. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales. Empirical results not only indicate that our model has significant superiority over conventional low-rank models, but also highlight that the versatility of the approach extends to different data domains, output resolutions, and network topologies. Comprehensive model analyses provide further insight into the inductive bias of STTD. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks. PyTorch implementations of this project is publicly available at:https://github.com/tongnie/traffic_dynamics.

Superposition of standing waves induced ultra high-resolution atom localization

Abstract In this study, we have theoretically demonstrated ultra-high resolution two-dimensional atom localization within a three-level $\lambda$-type atomic medium via superposition of asymmetric and symmetric standing wave fields. Our analysis provides understanding into the precise spatial localization of atomic positions at the atomic level, utilizing advanced theoretical approaches and principles of quantum mechanics. The dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics. A theoretical approach is constructed to describe the interaction between the system and external fields, specifically a control field and a probe field. The absorption spectrum of the probe field is thoroughly examined to clarify the spatial
localization of the atom within the proposed configuration. We have theoretically investigated that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial domain. Specifically, the emergence of one and two sharp localized peaks has been observed within a region of one wavelength domain. We observed notable influences of the intensity of the control field, probe field detuning, and decay rates on atom localization. Ultimately, we have achieved an unprecedented level of ultra-high resolution and precision in localizing an atom within an area smaller than λ/35×λ/35. These findings hold promise for potential applications in domains such as Bose-Einstein condensation, nanolithography, laser cooling, trapping of neutral atoms, and the measurement of center-of-mass wave-functions.

Effect of Vacancy Defects on the Electronic Structure and Optical Properties of Bi4O5Br2: First-Principles Calculations

First-principles calculations based on density functional theory are employed to investigate the impact of vacancy defects on the optoelectronic properties of Bi4O5Br2. The results indicate that vacancy defects induce minimal lattice distortion in Bi4O5Br2 without compromising its structural stability. Oxygen or bromine vacancies are more likely to occur than bismuth vacancies. The introduction of a bismuth vacancy leads to n-type semiconductor behavior in the Bi4O5Br2 system, while the creation of an oxygen vacancy reduces the bandgap and enhances the light absorption capacity. Bi4O5Br2 with three coordinated oxygen vacancies exhibits a higher effective electron–hole pair mass ratio, which is advantageous for the efficient separation of electron–hole pairs. Bi4O5Br2 with three coordinated oxygen vacancies exhibits enhanced absorption and reflection coefficients in the visible-light region compared to other systems, indicating that oxygen vacancy defects significantly promote visible-light absorption and electron–hole separation. This research provides new theoretical insights for understanding and optimizing the performance of photocatalysts based on Bi4O5Br2.

The mechanism of arsenic behaviour in the soil-plant system and its interaction with biogenic macroelements of plants under conditions of toxic stress

ABSTRACT The mobility of arsenic compounds in the soil-plant system and its interaction with biogenic macroelements K, P, Ca, Mg, Si, S, Na in plants under conditions of toxic stress have been studied. This is illustrated by the wheatgrass Elytrigia repens. It has been established that the root system is responsible for protecting the aerial part of wheatgrass from excess arsenic, and it is of great importance for phytostabilisation of arsenic pollution therefore indicating the increased tolerance of this plant. The intensity of As accumulation in plants depends on its speciation in the soil. Antagonism of the main essential elements (K, P) to the accumulation of As in wheatgrass shoots is observed. It can lead to disruption of metabolic processes in plant cells. It has been shown that changes in the K+/Na + values may lead to violation of osmotic processes in the aerial plant organs and contribute to their degradation not only under salt stress but also under the influence of higher concentrations of As in the plants. In areas intensively contaminated with arsenic, a decrease in the K/Na value in the above-ground organs of wheatgrass can be used as a criterion for the degree of phytotoxicity.

Monte Carlo Simulation Via Visual Basic Application

Objective: The objective of this study is to implement the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. Theoretical Framework: Implementing the results of the changes we desire on a real-life system through trial and error can lead to costly and risky outcomes. Simulation allows us to develop a computer model of an existing system, enabling us to understand the system's behavior. Method: Monte Carlo Simulation, a widely used simulation technique, utilizes random sampling to estimate possible outcomes of a process or experiment. Monte Carlo Simulation utilizes random sampling to estimate the outcomes of an experiment. Results and Discussion: The purpose of this study is to implement the Monte Carlo Simulation method using MS Excel Visual Basic Application. When the user interacts with the Excel interface by clicking the "Calculate" button, the system generates random numbers based on predefined probability distributions. Subsequently, statistical measures such as the mean, variance, standard deviation, median, mode, range, and extreme values (maximum and minimum) are calculated using VBA. Research Implications: Thanks to the developed application, Monte Carlo Simulation applications can be calculated easily. Originality/Value: This study contributes to the literature by implementing the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. These statistics provide insights into the underlying randomness and variability of the system, which aids in decision-making processes.

Addressing the non‐ideality of an application relevant surfactant mixture within the hydrophilic–lipophilic‐deviation concept

AbstractIn cleaning products, selecting the right surfactants for the specific soil or contamination is crucial. The hydrophilic–lipophilic deviation (HLD) concept serves as a useful formulation tool by describing the interactions between surfactants, oil and water. However, for mixtures of ionic and non‐ionic surfactants, the HLD concept assumes ideal mixing behavior, which does not hold true. Therefore, interactions between different surfactants must be considered. This can be achieved by introducing the interaction term GEX/RT. Evaluating this interaction term using a set of equilibrated surfactant/oil/water (SOW) systems under various conditions is both time‐consuming and resource intensive. This work aims to demonstrate the rapid determination of this interaction term using the dynamic phase salinity inversion (DSPI) method. We determined the phase behavior of a ternary surfactant system, consisting of sodium dioctyl sulfosuccinate, alkyl polyglucoside, and fatty alcohol ethoxylate, through both equilibrated systems and the DSPI method. Differences between the theoretical HLD calculations and actual experiments allowed us to access interaction terms. To validate GEX/RT obtained by DSPI, we employed net‐average‐curvature concept to model the interfacial tension under different conditions. Spinning drop measurements showed excellent agreement between theoretical and measured values, confirming the applicability of DSPI for determining interactions in this surfactant system.

Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model

Data assimilation-based real-time forecasting is widely used in meteorological and hydrological applications, where continuous data streams are employed to update forecasts and maintain accuracy. However, the reliability of the data source can be compromised due to sensor and communication failures or physical or cyber-attacks, and the impact of data stream failures on the accuracy of the forecasting system is not well understood. This study aims to systematically investigate the process of data stream failure and recovery for the first time. To achieve this, data gaps with varying lengths and timings are introduced to EnKF-based data assimilation system on the Lorenz model operating in both chaotic and periodic modes. Results show that the forecasting error grows exponentially in the chaotic mode but was limited in the periodic mode from the start of the data gap. For chaotic mode, the recovery of the system depends on the length of the data gap if the model error is not saturated; after saturation, the timing of the data stream recovery is important. Moreover, even long after restarting the data assimilation in the chaotic mode, the forecasting system cannot fully restore the original accuracy, while the periodic mode is generally resilient to disruption. This research introduces new metrics for quantifying system resilience and provides crucial insights into the long-term implications of data gaps, advancing our understanding of forecasting system behavior and reliability.

Predicting the Impact of Distributed Denial of Service (DDoS) Attacks in Long-Term Evolution for Machine (LTE-M) Networks Using a Continuous-Time Markov Chain (CTMC) Model

The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gather data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such attacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we developed a predictive model called Markov Detection and Prediction (MDP) using a Continuous-Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing, and predicting DDoS attacks in Long-Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious, and Malicious traffic. Additionally, we are able to predict the system behavior when facing different DDoS attacks.