Loss Probability Research Articles

Quantum wires with local particle loss: Transport manifestations of fluctuation-induced effects

We investigate the transport properties of a quantum wire of weakly interacting fermions in the presence of local particle loss. We calculate current and conductance in this system due to applied external chemical potential bias that can be measured in experimental realizations of ultracold fermions in quasi one-dimensional traps. Using a Keldysh field theory approach based on the Lindblad equation, we establish a perturbative scheme to study the effect of imbalanced reservoirs. Logarithmically divergent terms are resummed using a renormalization group method, and a novel powerlaw behavior for the conductance as a function of the potential bias across the wire is found. In contrast to the equilibrium case of a potential barrier in a Luttinger liquid, the conductance exhibits a scaling behavior, which depends on the interaction strength and on the loss probability. Repulsive interactions reduce the conductance of the wire while attractive interactions enhance it. However, perfect reflectivity and transparency are only reached in the absence of particle loss. Published by the American Physical Society 2024

Advanced Bayesian Method for Timely Small-Scale Forest Loss Detection in the Brazilian Amazon and Cerrado with Sentinel-1 Time-Series

Abstract. The world’s forests are undergoing significant changes due to loss and degradation, emphasizing the need for Near Real-Time (NRT) monitoring to prevent further damage. Traditional monitoring methods using optical imagery are hindered by cloud coverage, while newer Synthetic Aperture Radar (SAR) systems, although operational in all weather conditions, face challenges such as sensitivity to soil moisture and the need for spatial filtering to reduce speckle effects. These limitations affect the detection of small-scale forest loss, especially in seasonally variable regions like dry forests and savannas. This paper presents a SAR-based forest disturbance detection method using Bayesian inference. Unlike traditional methods, this approach maintains the native resolution of the data by avoiding spatial filtering. Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. This sequential adaptation ensures robustness against variations and trends, making it effective in monitoring disturbances across diverse forest types, including areas affected by seasonality. The proposed method was tested against other NRT monitoring systems for the year 2020, using small validation polygons (under 1 hectare) in the Brazilian Amazon and Cerrado savanna. Results demonstrate significant improvements in detecting small-scale disturbances and drastically reduced false alarm rates in both biomes. Notably, in the seasonality-sensitive Cerrado, our solution completely outperforms the leading and only existing optical technology.

ANALYSIS OF RISK IN THE LIVESTOCK OF THE STATE OF RIO DE JANEIRO AND BRAZIL BETWEEN JANUARY 2007 AND MARCH 2020

The article aims to analyze the exposure to risks that livestock farmers in the state of Rio de Janeiro face to Brazil using the Value at Risk (VaR) metric as a measure of risk and price return for cattle from January 2007 to March 2020, providing an expanded information that allows for presenting the level of risk of the activity between the analyzed states and comparing their levels. Through this metric, it is possible to have the total volume of losses that can occur for different types of assets and portfolios, including commodities. The results showed that ranchers in the state of Rio de Janeiro are exposed to a greater probability of financial losses when compared to other Brazilian ranchers. While in the state of Rio de Janeiro there is a 95% probability of finding maximum losses in investment of 4.81%, in Brazil the maximum losses were 3.09% for the 95% statistical confidence level. Finally, the non-parametric VaR metric of historical simulation proved to be a valuable tool in the risk management of livestock.

Estimation of Earthquake-Induced Direct Economic Losses of Portfolio Buildings Based on Seismic Fragility Surface

Regional seismic loss assessment is crucial for developing earthquake emergency plans, which can significantly reduce casualties and socioeconomic losses in urban communities. Due to the complex types of building structures in a region, it is not possible to accurately measure the damage state of building structures using a single scalar ground motion intensity measure (IM). To more accurately reflect the responses of structures under earthquake excitations, the seismic fragility surface models for 4 typical RC frame structures are developed based on the vector ground motion IMs. The results indicate that the fragility surface model offers a superior level of precision in capturing the inherent uncertainty of ground motion and subsequently determines a more precise probability of structural failure, outperforming traditional single scalar ground motion IM fragility curve models. This enhanced accuracy holds significant implications for regional loss assessment. To fully consider the spatial cross-correlation of vector ground motion IMs, based on the established ground motion database of 8778 records, principal component analysis (PCA) and geostatistical analysis methods are used to construct a spatial cross-correlation influence field that simultaneously considers multiple ground motion IMs to simulate more realistic earthquake scenarios. Considering the impact of uncertainty in loss ratio on earthquake-induced direct economic loss estimation results, the maximum entropy technique is used in conjunction with an improved Latin Hypercube Sampling (LHS) to achieve spatially uniform sampling of loss ratio (LR). This study proposes an assessment framework for regional seismic direct economic loss for portfolio buildings based on the seismic fragility surface model considering spatial cross-correlation and uncertainty of loss ratio. The proposed framework is verified by numerical examples, and it is proven that the regional portfolio buildings considering multiple ground motion IMs is more reliable and robust than those merely considering a single ground motion IM, and when spatial cross-correlation is not considered, the probability of serious economic losses will be underestimated.

The Impact of Drought on Vegetation at Basin Scale: A Case Study of the Wei River Basin, China

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management.

Risk attitude toward on-demand insurance: an experimental study

AbstractOn-demand insurance products cover risks for short periods of time via a smartphone or other electronic device. Such insurance contracts give policyholders the freedom to choose when to be insured in a flexible way. On-demand contracts may change the way risk is perceived. Therefore, we conduct an experiment and show that individuals can become exceptionally risk-averse when offered short-term insurance. We show two main reasons for this result: first, the underlying risk is often overestimated by the subjects due to a miscalculation of the loss probabilities. Second, the shorter valuation horizon of on-demand insurance contracts leads to myopic loss aversion.

Access to Waitlisting and Posttransplant Outcomes in Patients With Failed Kidney Allografts Secondary to Recurrent Glomerulonephritis.

Recurrent glomerulonephritis (GN) is an important cause of allograft loss after transplantation when GN is the primary cause of kidney failure. Retransplantation after allograft loss from recurrent disease requires careful consideration. We aimed to determine the probability of relisting and the risk of allograft loss after retransplantation in recipients with prior allograft loss from recurrent GN. Using data from the Australia and New Zealand Dialysis and Transplant Registry and multivariable Cox modeling, we compared the probability of waitlisting and allograft loss after second transplantation between those with and without prior allograft loss from recurrent disease. Of 3276 patients who received a second kidney transplant, 179 (5%) lost their first allograft from recurrent GN. Between 2006 and 2021, 1524 patients with failed first allografts (6% with recurrent GN, 45% with primary GN but no disease recurrence) were relisted for transplantation. Compared with patients without primary GN, the adjusted hazard ratios (95% confidence intervals) for relisting in patients with primary GN, with and without disease recurrence, were 1.09 (0.88-1.34) and 1.16 (1.05-1.29), respectively. The respective adjusted hazard ratios for allograft loss after repeat transplantation were 0.77 (0.59-1) and 1.02 (0.9-1.16). Of the 81 patients who received a second allograft after losing their first allograft to GN recurrence, 18 patients (22%) also lost their second allograft because of recurrent GN. Patients with prior allograft loss from GN recurrence were not disadvantaged, with comparable waitlist potential and allograft outcome after repeat transplantation. However, >20% of those with prior allograft loss from disease recurrence also lost their second allografts from recurrent disease.

Risk assessment for the surface water quality evaluation of a hydrological basin

This paper proposes a risk assessment methodology for evaluating the surface water quality of hydrological basins based on physico-chemical parameter concentrations. Considering the Douro River basin in Portugal and monthly recorded dissolved oxygen and conductivity parameter measurements in 18 water sampling stations from January 2002 to December 2013, the work intends to answer the research question of identifying the riskiest periods for water pollution in the year and classifying the water sampling stations in terms of risk for water pollution. The methodology consists first in determining the pollution risk implied by the physico-chemical parameters, based on the monthly water station measurements, using six different risk measures, namely mean, variance, loss probability, entropy, mean excess loss and value at risk. The risk values are ordered according to each risk measure and a final ranking is established through a ranking aggregation method. The final ranking permitted identifying the high risk period as ranging from May to October and the low risk period from November to April. Furthermore, July was classified as riskiest month concerning the dissolved oxygen concentration, and August as riskiest month regarding the conductivity levels. On the other hand, the ranking allowed classifying the water sampling stations, previously grouped in clusters, in terms of similar risk for water pollution: six sampling stations in the west of the basin formed the riskiest cluster in the dry period considering the dissolved oxygen concentrations, and four of those stations formed also the riskiest cluster concerning the conductivity levels.

Muscle Mass and Glucagon-Like Peptide-1 Receptor Agonists: Adaptive or Maladaptive Response to Weight Loss?

Recent studies have shown that pharmacologic weight loss with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and combination therapies is approaching magnitudes achieved with surgery. However, as more weight loss is achieved, there is concern for potential adverse effects on muscle quantity, composition, and function. This primer aims to address whether muscle-related changes associated with weight loss treatments such as GLP-1 RAs may be maladaptive (ie, adversely affecting muscle health or function), adaptive (ie, a physiologic response to weight loss maintaining or minimally affecting muscle health or function), or perhaps an enhanced response to weight loss (ie, improved muscle health or function after treatment). Based on contemporary evidence with the addition of studies using magnetic resonance imaging, skeletal muscle changes with GLP-1 RA treatments appear to be adaptive: changes in muscle volume z-score indicate a change in muscle volume that is commensurate with what is expected given aging, disease status, and weight loss achieved, and the improvement in insulin sensitivity and muscle fat infiltration likely contributes to an adaptive process with improved muscle quality, lowering the probability for loss in strength and function. Nevertheless, factors such as older age and prefrailty may influence the selection of appropriate candidates for these therapies because of risk for sarcopenia. Several pharmacologic treatments to maintain or improve muscle mass designed in combination with GLP-1-based therapies are under development. For future development of GLP-1-based therapies (and other therapies) designed for weight loss, as well as for patient-centered treatment optimization, the introduction of more objective and comprehensive ways of assessing muscle health (including accurate and meaningful assessments of muscle quantity, composition, function, mobility, and strength) is important for the substantial numbers of patients who will likely be taking these medications well into the future.

Successful occluder removal and reocclusion of an atrial septal defect after occluder immigration to aortic arch: a case report

BackgroundAtrial septal defect (ASD) is a common congenital heart disease, and currently, transcatheter intervention is the most common clinical treatment method. However, certain complications still occur during the percutaneous process, among which occluder loss and displacement are rare but serious complication. Although the probability of occluder loss and displacement is low, severe cases can endanger life.Case presentationHere, we report the case of a patient who underwent ASD closure in which the occluder fell off into the aortic arch, the detached occluder was recovered through catheter intervention, and the patient underwent ASD closure again.ConclusionsIn this case report, we highlight that although percutaneous closure of an ASD is regarded as a routine procedure, clinicians should remember the possibility of complications, especially occluder loss and displacement. Therefore, interventionist should carefully evaluate the situation before intervention closure, establish standardized interventional treatment procedures, and provide timely treatment follow-up.

The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range. Accordingly, the quality of traffic service is affected both by the number of subscribers simultaneously initiating requests from one sector of coverage, and by the appearance of obstacles opaque to radio radiation in the space between the subscriber device and the base station. Effective smoothing of 5G traffic fluctuations, taking into account these disturbing factors, is an urgent task. The goal of this research is to evaluate the service quality parameters in a target area characterized by a specific user density. It takes into account that if the declared QoS requirements for connection speed for users in a network segment deployed within the licensed frequency range for 5G are not met, they can utilize a network segment deployed in the unlicensed high-frequency range for 5G under conditions of free competition. The metric being studied is the probability of session loss in the licensed network segment and the achievable transmission speed in the unlicensed network segment. Based on this, a method for assessing the density of base station deployment in the unlicensed network segment necessary to support the specified user density in the licensed network segment with defined QoS guarantees in terms of bandwidth is formalized. The experiment results showed that the probability of losing sessions with regulated requirements for the quality of service in both network segments, in addition to the base station placement density and subscriber devices, is significantly affected by the minimum data transfer rate, the intensity of obstacles, and the value of the Contention Window.

Wavelength-division multiplexing (WDM) FSO communications with soliton microcombs

This study highlights the benefits of soliton microcombs (SMCs) in FSO communication systems. By employing a microring resonator coupled with an add/drop filter, we achieved soliton microcombs with a precise Free Spectral Range (FSR) of 1870 pm and a Full Width at Half Maximum (FWHM) of 60 pm, tailored for Wavelength Division Multiplexing (WDM) channels. The system utilized 100 lines for the WDM channel with an inter-channel spacing of 0.5 nm and PolSK modulation to encode the data. This combination achieved a bit error rate (BER) of 10 − 9 , significantly lower than the 10 − 7 observed with traditional systems. For performance simulation, the channel was characterized using the gamma-gamma model, which assessed the impact of atmospheric turbulence on data transmission. The results show a high channel capacity ( 10 3 Gbit/s) and a low loss probability ( 10 − 8 ), ensuring reliable data transmission even in the presence of atmospheric disturbances. This methodology provides substantial improvements in stability and efficiency for FSO networks.

Multi-objective optimisation of hybrid renewable energy systems for Colombian non-interconnected zones

Colombia’s Atlantic coast wind and solar resources could enhance energy mix and self-generation. Renewable clean energy has been studied in response to fossil fuel pollution. Wind and solar photovoltaic systems have low initial, operational, and levelized energy costs. Variable wind and sun radiation may limit availability. In this regard, hybrid renewable energy systems (HRES) and energy storage have become crucial. These systems can effectively meet load demand by utilising complementary renewable resources. This study deals with sizing a wind and photovoltaic HRES with storage, using a Particle Swarm Optimisation algorithm for yearly variable resources in a non-interconnected zone in La Guajira, Colombia. The study evaluates the LCOE, probability of load loss, and the system’s CO2 emission. It develops a sensitivity analysis to determine the importance of each objective factor. From a life cycle analysis, an HRES configuration with low LCOE and environmental emission rates is associated with the size of the wind resource; the HRES configuration with minimum LCOE values is close to obtaining higher equivalent CO2e emissions. The study highlights the configuration obtained for the Colombian context, giving more importance to the environmental factor and reaching an LCOE of 0.754 USD/kWh and emission of 18.97 tCO2e/year; this configuration also increases the wind energy generation, reaching 41 % more share, compared to the configuration obtained when the economic factor is a priority.

Surface loss probability of atomic oxygen on silica under atmospheric-pressure CO2

Abstract The surface loss probability γ O of atomic oxygen was measured for SiO2 in atmospheric-pressure CO2 and Ar/CO2(3.05%) mixture at 300 K. O atoms were produced via the photolysis of CO2 using a 172 nm Xe2 excimer lamp. After irradiation by Xe2 lamp, the working gas flowed through a quartz tube, causing O atoms to be lost on its inner surface. By measuring the density of O atoms at the end of the tube for different tube lengths, we obtained the value of γ O on the inner surface of the tube. The measured value was 5.5 × 10 − 4 ± 50 % , which falls within the range of referred values of γ O ( 1 × 10 − 5 to 1 × 10 − 3 ) at 4 to 1300 Pa measured using low-pressure O2 plasmas. Despite the 2 to 5 orders of magnitude pressure difference, no significant difference in γ O was obtained. The potential influence of the difference in background gas (CO2 and O2) on the value of γ O was also considered.

Dynamic assessment of the impact of compound dry-hot conditions on global terrestrial water storage

Precipitation and temperature are critical factors influencing terrestrial water storage (TWS) can lead to unexpected TWS losses when compounded by dryness and high temperatures. Yet, a dynamic assessment of the individual and combined effects of these conditions on TWS is lacking. This study proposes a framework to assess TWS loss driven by compound dry-hot conditions (CDHC) and dynamically evaluates risk probabilities and thresholds for 2003–2012 and 2013–2022. Results showed that CDHC exert a greater impact on TWS than dry or hot conditions alone. The risk probabilities of global TWS loss are higher in the late period than in the early period, with risk probabilities for light and extreme levels increasing by approximately 9–11 % and 2–7 %, respectively. Although the resilience of water resource systems to CDHC has increased in some regions, it still shows a decreasing trend on a global scale. The decrease in the resilience to TWS in major hyperarid areas is primarily influenced by temperature, whereas that in arid areas is primarily affected by precipitation. These distinct patterns may be the primary factors contributing to the exacerbation of global TWS loss. This study provides a novel approach for the dynamic assessment of global TWS under CDHC. The research findings offer valuable insights for decision-makers developing adaptive strategies to mitigate future CDHC challenges.

High accuracy model for HBsAg loss based on longitudinal trajectories of serum qHBsAg throughout long-term antiviral therapy

ObjectiveHepatitis B surface antigen (HBsAg) loss is the optimal outcome for patients with chronic hepatitis B (CHB) but this rarely occurs with currently approved therapies. We aimed to develop and...

Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm

This paper offers a fresh perspective to memory allocation in edge-PLCs within industrial IoT environments, leveraging a hybrid a structure that incorporates Deep Q-Networks (DQN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), complemented by Quantum Genetic Algorithm Optimization. In addressing the dynamic challenges of memory management, our method combines the reinforcement learning capabilities of DQN with the temporal contextualization provided by BiLSTM networks. The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. Moreover, we introduce Quantum Genetic Algorithm Optimization, a cutting-edge approach that infuses quantum-inspired principles into the traditional genetic algorithm framework, to further refine the memory allocation process. By leveraging principles of quantum computing, this optimization algorithm explores the solution space more efficiently, allowing for faster convergence and improved performance. Through simulation experiments, we exhibit the potency of our hybrid approach in reducing data loss probability and enhancing system performance in edge-PLCs. Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.

Incidence and timing of pregnancy loss following timed artificial insemination or timed embryo transfer with a fresh or frozen in vitro-produced embryo

The aim of this study was to characterize the incidence and timing of pregnancy loss from service event (timed artificial insemination or timed embryo transfer) to parturition. Lactating Holstein-Friesian cows were randomly assigned to receive either artificial insemination (AI, n = 243) or embryo transfer (ET, n = 863) with a fresh or frozen in vitro-produced blastocyst derived from oocytes collected from the ovaries of elite dairy donors (n = 14 Holstein Friesian and n = 8 Jersey) and elite beef donors (n = 21 Angus) using transvaginal ovum pick-up. In addition, oocytes were collected from the ovaries of beef heifers of known pedigree following slaughter at a commercial abattoir (n = 119). Blood samples were collected on d 7 from all cows to determine progesterone concentration and from subsets of cows on d 18 (n = 524) and d 25 (n = 378) to determine mRNA abundance of interferon-stimulated gene-15 and pregnancy-specific protein B concentration, respectively, to provide an early pregnancy diagnosis. Transrectal ultrasonography was conducted to determine pregnancy status on d 32, d 62 and d 125 after synchronized ovulation. Parturition date was recorded for all cows that reached a term delivery. The predicted probability of pregnancy (%) varied at each time point (d7, 18, 25, 32, 62, 125, parturition) depending on treatment (AI: 77.0, 60.2, 52.3, 48.8, 47.0, 44.6, 44.0; fresh ET: 100.0, 69.5, 60.3, 56.1, 48.4, 46.8, 45.5; frozen ET: 100.0, 61.7, 52.2, 41.6, 32.9, 31.8, 30.2). Irrespective of treatment, the largest proportion of pregnancy loss occurred in the period from service event (AI on d 0 or ET on d 7) to d 18, with minimal loss occurring between d 62 and parturition (AI: 1.8%, fresh ET: 1.9%, frozen ET: 3.5%). Treatment differences in the predicted probability of pregnancy per service event were detected between fresh ET vs frozen ET on d 32 and both AI and fresh ET vs frozen ET on d 62, 125 and at parturition. There was greater probability of pregnancy loss between d 32 and 62 following ET (Fresh: 11.3%, Frozen: 18.0%) than AI (4.0%). The percentage of cows that calved following transfer of a fresh embryo (45.5%) was similar to AI (44.0%), but less when a frozen embryo was transferred (30.2%). In conclusion, AI and fresh ET led to a greater probability of a cow becoming pregnant and maintaining the pregnancy to term than frozen ET. Cows that were still pregnant on d 62 had a very strong likelihood of maintaining the pregnancy to full-term parturition, irrespective of treatment. Further work is required to improve the likelihood of pregnancy establishment and reduce embryonic and fetal mortality following transfer of a cryopreserved in vitro-produced embryo.

Enhancing Sustainability through Weather Derivative Option Contracts: A Risk Management Tool in Greek Agriculture

This paper investigates the efficacy of weather derivatives as a risk management tool in the agricultural sector of Naousa, Greece, focusing on tree crops sensitive to temperature variations. The specific purpose is to assess how effectively weather derivative options can mitigate financial risks for farmers by providing strategic solutions. The study assesses the strategic application of Heating Degree Days (HDD) index options and their potential to alleviate economic vulnerabilities faced by farmers due to temperatures fluctuations. Employing different strike prices in Long Call and Straddle options strategies on the HDD index, the research offers tailored risk management solutions that cater to varying risk aversions among farmers. Moreover, the study applies the Value at Risk (VaR) methodology to quantify the financial security that weather derivatives can furnish, revealing a significantly reduced probability of severe financial losses in hedged scenarios compared to no-hedge conditions. Results show that all implemented strategies effectively enhance financial outcomes compared to scenarios without hedging, highlighting the exceptional utility of weather derivatives as risk management tools in the agricultural sector. Strategy 4, which exhibits the lowest VaR, emerges as the most effective, providing substantial protection against adverse weather conditions. This research supports the notion that weather derivatives can substantially contribute to the economic sustainability of rural economies, influencing policy decisions toward enhancing financial instruments for risk management in agriculture.

Modeling and Performance Study of Vehicle-to-Infrastructure Visible Light Communication System for Mountain Roads.

Visible light communication (VLC) is considered to be a promising technology for realizing intelligent transportation systems (ITSs) and solving traffic safety problems. Due to the complex and changing environment and the influence of weather and other aspects, there are many problems in channel modeling and performance analysis of vehicular VLC. Unlike existing studies, this study proposes a practical vehicle-to-infrastructure (V2I) VLC propagation model for a typical mountain road. The model consists of both line-of-sight (LOS) and non-line-of-sight (NLOS) links. In the proposed model, the effects of vehicle mobility and weather conditions are considered. To analyze the impact of the considered propagation characteristics on the system, closed-form expressions for several performance metrics were derived, including average path loss, received power, channel capacity, and outage probability. Furthermore, to verify the accuracy of the derived theoretical expressions, simulation results were presented and analyzed in detail. The results indicate that, considering the LOS link and when the vehicle is 50 m away from the infrastructure, the difference in channel gain between moderate fog and dense fog versus clear weather conditions is 1.8 dB and 3 dB, respectively. In addition, the maximum difference in total received optical power between dense fog conditions and clear weather conditions can reach 76.2%. Moreover, under clear weather conditions, the channel capacity when vehicles are 40 m away from infrastructure is about 98.9% lower than when they are 10 m away. Additionally, the outage probability shows a high correlation with the threshold data transmission rate. Therefore, the considered propagation characteristics have a significant impact on the performance of V2I-VLC.