Random Time Research Articles

Adaptive promising zone design for cancer immunotherapy with heterogeneous delayed treatment effect

ABSTRACT Indirect mechanisms of cancer immunotherapies result in delayed treatment effects that vary among patients. Consequently, the use of the log-rank test in trial design and analysis can lead to significant power loss and pose additional challenges for interim decisions in adaptive designs. In this paper, we describe patients’ survival using a piecewise proportional hazard model with random lag time and propose an adaptive promising zone design for cancer immunotherapy with heterogeneous delayed effects. We provide solutions for calculating conditional power and adjusting the critical value for the log-rank test with interim data. We divide the sample space into three zones – unfavourable, promising, and favourable –based on re-estimations of the survival parameters, the log-rank test statistic at the interim analysis, and the initial and maximum sample sizes. If the interim results fall into the promising zone, the sample size is increased; otherwise, it remains unchanged. We show through simulations that our proposed approach has greater overall power than the fixed sample design and similar power to the matched group sequential trial. Furthermore, we confirm that critical value adjustment effectively controls the type I error rate inflation. Finally, we provide recommendations on the implementation of our proposed method in cancer immunotherapy trials.

Understanding and Quantifying Network Robustness to Stochastic Inputs.

A variety of biomedical systems are modeled by networks of deterministic differential equations with stochastic inputs. In some cases, the network output is remarkably constant despite a randomly fluctuating input. In the context of biochemistry and cell biology, chemical reaction networks and multistage processes with this property are called robust. Similarly, the notion of a forgiving drug in pharmacology is a medication that maintains therapeutic effect despite lapses in patient adherence to the prescribed regimen. What makes a network robust to stochastic noise? This question is challenging due to the many network parameters (size, topology, rate constants) and many types of noisy inputs. In this paper, we propose a summary statistic to describe the robustness of a network of linear differential equations (i.e. a first-order mass-action system). This statistic is the variance of a certain random walk passage time on the network. This statistic can be quickly computed on a modern computer, even for complex networks with thousands of nodes. Furthermore, we use this statistic to prove theorems about how certain network motifs increase robustness. Importantly, our analysis provides intuition for why a network is or is not robust to noise. We illustrate our results on thousands of randomly generated networks with a variety of stochastic inputs.

Charge fluctuations on the dust grains in the presence of energetic electrons

Abstract Dust charging is an arbitrary process occurring at random times which results in fluctuations of dust charge around its equilibrium value. To have a better insight into the mechanism of charge fluctuations, a numerical simulation of the statistical nature of the dust charging process is investigated. Here, a multicomponent non-Maxwellian hydrogen plasma comprising two electron groups, positive ions, and dust grains is modelled. An increase in the overall negative dust charge number is reported in the presence of non-Maxwellian electrons. Additionally, the study emphasizes the role of electron distribution and hot electron temperature and density on the charge fluctuations of the dust grains.

Local resetting in non-conserving zero-range processes with extensive rates

Abstract A non-conserving zero-range process with extensive creation, annihilation and hopping rates is subjected to local resetting. The model is formulated on a large, fully-connected network of states. The states are equipped with a (bounded) fitness level: particles are added to each state at a rate proportional to the fitness level of the state. Moreover, particles are annihilated at a constant rate, and hop at a fixed rate to a uniformly-drawn state in the network. This model has been interpreted in terms of population dynamics: the fitness is the reproductive fitness in a haploid population, and the hopping process models mutation. It has also been interpreted as a model of network growth with a fixed set of nodes (in which particles occupying a state are interpreted as links pointing to this state). In the absence of resetting, the model is known to reach a steady state, which in a certain limit may 
 exhibit a condensate at maximum fitness. If the model is subjected to global resetting by annihilating all particles at Poisson-distributed times, there is no condensation in the steady state. If the system is subjected to local resetting, the occupation numbers of each state are reset to zero at independent random times. These times are distributed according to a Poisson process whose rate (the resetting rate) depends on the fitness. We derive the evolution equation satisfied by the probability law of the occupation numbers. We calculate the average occupation numbers in the steady state. The existence of a condensate is found to depend on the local behavior of the resetting rate at maximum fitness: if the resetting rate vanishes at least linearly at high fitness, a condensate appears at maximum fitness in the limit where the sum of the annihilation and hopping rates is equal to the maximum fitness.

Random-time component reallocation and system replacement policy with minimal repair

The maintenance policies based on random working time guarantee the continuity of system functionality. This paper studies a random-time component reallocation and system replacement (RCS) policy, in which the random-time system replacement is to replace the system at the planned preventive time or the random working time, whichever comes first, the random-time component reallocation is to balance the degradation of components by reallocating components at random working time before system replacement, and minimal repair is implemented for failed components. To improve the efficiency and flexibility of RCS policy, we first establish an optimization model to minimize the expected system maintenance cost per unit time based on a given interval for random-time component reallocation, and analytical results are derived by comparing the proposed policy with the preventive component reallocation and random-time system replacement policy, and the simple random-time system replacement policy. Next, we construct another optimization model to maximize the interval length for the random-time component reallocation constrained by the threshold of the efficiency level of RCS policy. Numerical experiments demonstrate the performance of the RCS policy and provide guidance for the interval for the random-time component reallocation.

Target-oriented robust satisficing models for the single machine scheduling problems with release time

In this paper, we investigate single machine scheduling problems with release times and random processing times, where the release times can be either deterministic or random. The objective is to determine a scheduling sequence that exhibits strong out-of-sample performance. To achieve this, we employ target-oriented robust satisficing models to obtain the scheduling sequence. For the scheduling problem with deterministic release times, we derive an equivalent mixed-integer linear program and an approximate reformulation to address cases involving a large number of jobs. For the scheduling problem with random release times, we first demonstrate the challenges associated with providing an equivalent reformulation. To overcome this intractability, we propose an approximate reformulation based on the linear decision rule. Numerical experiments are conducted to demonstrate the superiority of our solutions through comparisons with several benchmarks. Furthermore, the numerical results demonstrate that a relatively higher target should be chosen if the worst-case performance is valued, otherwise, the decision-maker should set a relatively lower target to obtain better average performance.

Optimal Resilience-based Restoration of a System Subject to Recurrent Dependent Hazards

Many engineered systems are faced with multiple natural and manmade hazards during operation. Since such hazards may cause system disruptions (e.g., catastrophic failure and performance deterioration), timely and effective restoration needs to be conducted to minimize the negative impacts of the hazards. In practice, these hazards are recurrent and often interact dynamically with each other. This causes a major challenge in assessing the performance of the systems and determining the optimal restoration policy from a holistic perspective. In this paper, we model the availability and resilience of a system subject to recurrent and dependent hazards and develop the optimal restoration policies for decision makers with different risk preferences. Particularly, we first model the random occurrence times and severities of a set of hazards whose occurrence frequencies and severities are dynamically dependent. Next, we derive the analytical expressions for the system's availability metrics. Third, we investigate the optimal restoration policies for both risk-neutral (RN) and risk-averse (RA) decision makers by solving two related optimization problems. A case study on the pipelines in the Mid-Atlantic area of the U.S. is provided to illustrate the practical values of the proposed availability assessment and resilience optimization methods.

Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series.

The availability of a sufficient number of annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Creating these samples is time-consuming and costly. Active Learning (AL) offers a solution by streamlining sample annotation, resulting in more efficient training with less effort. Unfortunately, most of the developed AL methods overlook spatial information inherent in remote sensing images. We propose a novel spatially explicit AL that uses the semi-variogram to identify and discard redundant, spatially adjacent samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected 97 samples achieving an overall accuracy of 80%, compared to traditional AL selecting 169 samples with 82% overall accuracy. In Belgium, spatially explicit AL selected 223 samples and obtained 60% overall accuracy, while traditional AL selected 327 samples and obtained an overall accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as 'fruits and nuts' posed, however, a challenge.

Mean-field ranking games with diffusion control

AbstractWe consider a stochastic differential game, where each player continuously controls the diffusion intensity of her own state process. The players must all choose from the same diffusion rate interval $$[\sigma _1, \sigma _2]$$ [ σ 1 , σ 2 ] , and have individual random time horizons that are independently drawn from the same distribution. The players whose states at their respective time horizons are among the best $$p \in (0,1)$$ p ∈ ( 0 , 1 ) of all terminal states receive a fixed prize. We show that in the mean field version of the game there exists an equilibrium, where the representative player chooses the maximal diffusion rate when the state is below a given threshold, and the minimal rate else. The symmetric n-fold tuple of this threshold strategy is an approximate Nash equilibrium of the n-player game. Finally, we show that the more time a player has at her disposal, the higher her chances of winning.

Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of Resistant E. coli Infections.

Drug-resistant Gram-negative bacterial infections, on average, increase the length of stay (LOS) in U.S. hospitals by 5 days, translating to approximately $15,000 per patient. We used statistical and machine-learning models to explore the relationship between antibiotic usage and antibiotic resistance over time and to predict the clinical and financial costs associated with resistant E. coli infections. We acquired data on antibiotic utilization and the resistance/sensitivity of 4776 microbial cultures at a Kaiser Permanente facility from April 2013 to December 2019. The ARIMA (autoregressive integrated moving average), neural networks, and random forest time series algorithms were employed to model antibiotic resistance trends. The models' performance was evaluated using mean absolute error (MAE) and root mean squared error (RMSE). The best performing model was then used to predict antibiotic resistance rates for the year 2020. The ARIMA model with cefazolin, followed by the one with cephalexin, provided the lowest RMSE and MAE values without signs of overfitting across training and test datasets. The study showed that reducing cefazolin usage could decrease the rate of resistant E. coli infections. Although piperacillin/tazobactam did not perform as well as cefazolin in our time series models, it performed reasonably well and, due to its broad spectrum, might be a practical target for interventions in antimicrobial stewardship programs (ASPs), at least for this particular facility. While a more generalized model could be developed with data from multiple facilities, this study acts as a framework for ASP clinicians to adopt statistical and machine-learning approaches, using region-specific data to make effective interventions.

Stochastic resetting with refractory periods: pathway formulation and exact results

We look into the problem of stochastic resetting with refractory periods. The model dynamics comprises diffusive and motionless phases. The diffusive phase ends at random time instants, at which the system is reset to a given position—where the system remains at rest for a random time interval, termed the refractory period. A pathway formulation is introduced to derive exact analytical results for the relevant observables in a broad framework, with the resetting time and the refractory period following arbitrary distributions. For the paradigmatic case of Poissonian distributions of the resetting and refractory times, in general with different characteristic rates, closed-form expressions are obtained that successfully describe the relaxation to the steady state. Finally, we focus on the single-target search problem, in which the survival probability and the mean first passage time to the target can be exactly computed. Therein, we also discuss optimal strategies, which show a non-trivial dependence on the refractory period.

Wong–Zakai approximation of a stochastic partial differential equation with multiplicative noise

In this article, we derive the convergence rate for the Wong–Zakai equation of some approximation of stochastic evolution equations with multiplicative noise. To be more precise, the diffusion coefficient in front of the noise is the multiplication operator, and, is therefore not bounded, a situation not treated in the literature. Since our motivation comes from problems in numerical ling, we consider a finite, high-dimensional problem approximating a stochastic evolution equation on a random time grid. By imposing suitable stability conditions on the drift term and the time grid, we achieve a convergence rate in the mean square of order min { 1 − δ , 2 − 2 γ } , for some 0 < δ < 1 and 0 < γ < 1 / 2 .

Exploring logistics transport route optimization: An algorithmic study based on RFID technology

With the rapid growth of e-commerce, logistics companies face challenges in efficient routing and scheduling to meet dynamic delivery demands. This paper proposes a novel logistics scheduling model to optimize vehicle routing using Radio Frequency Identification (RFID) technology. A vehicle scheduling model is developed. The random customer demand and service time are solved using an adaptive taboo search algorithm combined with a nearest neighbor algorithm. Comparative experiments demonstrate the performance of the improved method in completing tasks and reducing queueing time compared to other methods. A case study of route optimization for a logistics company shows the model can recommend optimized routes that reduce total transportation cost by over 25% compared to using RFID alone. The results highlight the potential of the proposed technique to enhance logistics efficiency. Limitations and future work are discussed.

ACTION: A randomized phase 3 study of ONC201 (dordaviprone) in patients with newly diagnosed H3 K27M-mutant diffuse glioma.

H3 K27M-mutant diffuse glioma primarily affects children and young adults, is associated with a poor prognosis, and no effective systemic therapy is currently available. ONC201 (dordaviprone) has previously demonstrated efficacy in patients with recurrent disease. This phase 3 trial evaluates ONC201 in patients with newly diagnosed H3 K27M-mutant glioma. ACTION (NCT05580562) is a randomized, double-blind, placebo-controlled, parallel-group, international Phase 3 study of ONC201 in newly diagnosed H3 K27M-mutant diffuse glioma. Patients who have completed standard frontline radiotherapy are randomized 1:1:1 to receive placebo, once-weekly dordaviprone, or twice-weekly dordaviprone on two consecutive days. Primary efficacy endpoints are overall survival (OS) and progression-free survival (PFS); PFS is assessed by response assessment in neuro-oncology-high grade glioma criteria (RANO-HGG) by blind independent central review. Secondary objectives include safety, additional efficacy endpoints, clinical benefit, and quality of life. Eligible patients have histologically confirmed H3 K27M-mutant diffuse glioma, a Karnofsky/Lansky performance status ≥70, and completed first-line radiotherapy. Eligibility is not restricted by age; however, patients must be ≥10kg at time of randomization. Patients with a primary spinal tumor, diffuse intrinsic pontine glioma, leptomeningeal disease, or cerebrospinal fluid dissemination are not eligible. ACTION is currently enrolling in multiple international sites.

Randomised trials conducted using cohorts: a scoping review

IntroductionCohort studies generate and collect longitudinal data for a variety of research purposes. Randomised controlled trials (RCTs) increasingly use cohort studies as data infrastructures to help identify and recruit trial...

Safety and efficacy of continuous subcutaneous levodopa–carbidopa infusion (ND0612) for Parkinson's disease with motor fluctuations (BouNDless): a phase 3, randomised, double-blind, double-dummy, multicentre trial

Conventional oral levodopa therapy for the treatment of Parkinson's disease can be associated with variations in plasma concentrations. Levodopa infusion strategies might provide more consistent drug delivery and fewer motor fluctuations. We aimed to assess the safety and efficacy of a continuous 24 h/day subcutaneous infusion of ND0612 (a levodopa-carbidopa solution) compared with oral immediate-release levodopa-carbidopa for the treatment of motor fluctuations in people with Parkinson's disease. We conducted a phase 3, randomised, double-blind, double-dummy, active-controlled, multicentre trial at 117 academic and community neurology sites in 16 countries, including in Europe, Israel, and the USA. Eligible participants were men and women aged 30 years or older with a diagnosis of Parkinson's disease (Hoehn and Yahr stage ≤3 in the on state) who experienced at least 2·5 h/day of off time. Participants underwent an open-label run-in phase (<12 weeks), during which time optimal regimens were established for both oral immediate-release levodopa-carbidopa and for 24 h/day subcutaneous ND0612 infusion (levodopa-carbidopa 60·0/7·5 mg/mL), with supplemental oral levodopa-carbidopa if needed. Participants were then randomly assigned (1:1) to 12 weeks of double-blind treatment with their optimised regimen of either subcutaneous ND0612 or oral levodopa-carbidopa, with matching oral or subcutaneous placebo given as required to maintain blinding. Randomisation was done via an interactive web response system, stratified by region, using a permuted block schedule. Participants, study partners, treating investigators, study site personnel, and the sponsor were masked to treatment group allocation. The primary efficacy endpoint was the change from baseline (ie, time of randomisation, when all patients were receiving an optimised open-label ND0612 regimen) to end of the double-blind phase in total daily on time without troublesome dyskinesia, analysed by intention to treat. This trial is registered with ClinicalTrials.gov, NCT04006210, and is complete. Between Sept 30, 2019, and April 8, 2022, 381 participants were enrolled, of whom 259 (68%) were randomly assigned, 128 (49%) to subcutaneous ND0612 and 131 (51%) to oral levodopa-carbidopa. 243 (94%) participants completed the study. Treatment with subcutaneous ND0612 provided an additional 1·72 h (95% CI 1·08 to 2·36) of on time without troublesome dyskinesia compared with oral levodopa-carbidopa (change from baseline of -0·48 h [-0·94 to -0·02] with subcutaneous ND0612 vs -2·20 h [-2·65 to -1·74] with oral levodopa-carbidopa; p<0·0001). Significant treatment differences favouring subcutaneous ND0612 were also found in the first four of nine prespecified hierarchical outcomes of daily off time (-1·40 h [95% CI -1·99 to -0·80]), Movement Disorders Society-Unified Parkinson's Disease Rating Scale part II scores (-3·05 [-4·28 to -1·81]), Patients Global Impression of Change (odds ratio [OR] 5·31 [2·67 to 10·58]), and Clinical Global Impression of Improvement (OR 7·23 [3·57 to 14·64]). Hierarchical testing ended after the fourth secondary endpoint. Adverse events were reported by 287 (89%) of 322 participants during open-label ND0612 optimisation, and by 103 (80%) of 128 in the ND0612 group and 97 (74%) of 131 in the oral levodopa-carbidopa group during the double-blind phase. The most common adverse events were infusion-site reactions (266 [83%] participants during open-label ND0612, and 73 [57%] in the ND0612 group vs 56 [43%] in the oral levodopa-carbidopa group during the double-blind phase), most of which were mild. Serious adverse events in four participants in the ND0612 group were related to study treatment (infusion-site cellulitis [n=2], infusion-site abscess and infusion-site ulcer [n=1]; and paraesthesia and peripheral sensorimotor neuropathy [n=1]). One participant in the ND0612 group died during the double-blind phase, but the death was not related to study treatment (fall leading to traumatic brain injury). Results of this phase 3 study showed that subcutaneous ND0612 used in combination with oral immediate-release levodopa-carbidopa increased on time without troublesome dyskinesia and reduced off time, with a favourable benefit-risk profile. ND0612 might offer a safe and efficacious subcutaneous levodopa infusion approach to managing motor fluctuations in people with Parkinson's disease. The ongoing open-label extension phase will provide further information on the long-term efficacy and safety of treatment. NeuroDerm.

Withdrawal of renin-angiotensin system inhibitors’ effect on estimated glomerular filtration rate in adults with advanced kidney disease: the STOP-ACEi RCT

Background Renin–angiotensin system inhibitors, both angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, slow progression of mild and moderate chronic kidney disease. However, some evidence suggests that discontinuation of renin–angiotensin system inhibitors in patients with advanced chronic kidney disease might increase estimated glomerular filtration rate or slow its decline. Objective To test the hypothesis that stopping angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, or a combination of both, compared with continuing these treatments, improves or stabilises kidney function in patients with progressive stages 4 or 5 chronic kidney disease based on assessment of kidney function using the modification of diet in renal disease four-variable estimated glomerular filtration rate at 3 years, follow-up. Setting Thirty-seven UK hospitals with kidney services. Design An investigator-led multicentre open-label, randomised controlled trial of 411 participants with advanced (stage 4 or 5) progressive chronic kidney disease. Participants Adult patients with advanced (estimated glomerular filtration rate &lt; 30 ml/minute/1.73 m2) and progressive chronic kidney disease who were receiving either angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, or a combination of both. Interventions Participants were randomised in a 1 : 1 ratio using a centralised internet-based system to either discontinue renin–angiotensin system inhibitors (n = 206) or continue renin–angiotensin system inhibitors (n = 205). Main outcome measures The primary outcome was the estimated glomerular filtration rate at 3 years; measurements of estimated glomerular filtration rate made after commencing kidney replacement therapy were excluded. Secondary outcomes included development of end-stage kidney disease or starting kidney replacement therapy, a composite of either a &gt; 50% decline in estimated glomerular filtration rate or commencement of kidney replacement therapy (including end-stage kidney disease), cystatin C, hospitalisations, blood pressure, exercise capacity and quality of life. Cardiovascular events, death and safety were recorded. Results At 3 years, the least-squares mean (± standard error) estimated glomerular filtration rate was 12.6 ± 0.7 ml/minute/1.73 m2 in the discontinuation group and 13.3 ± 0.6 ml/minute/1.73 m2 in the continuation group [difference −0.7, 95% confidence interval (−2.5 to 1.0; p = 0.42)] with a negative value favouring the continuation group. The treatment effect did not differ (heterogeneity) when data were analysed by the pre-specified subgroups. End-stage kidney disease or kidney replacement therapy occurred in 128 (62%) and 115 (56%) participants randomised to the discontinue and continue renin–angiotensin system inhibitor groups, respectively (hazard ratio 1.28, 95% confidence interval 0.99 to 1.65). The numbers of cardiovascular events and deaths observed were similar for those randomised to discontinue (108 events and 20 deaths) or continue (88 events and 22 deaths) renin–angiotensin system inhibitors. Limitations Non-white ethnic backgrounds were poorly represented, limiting the generalisability of our findings. The open-label nature of the trial may have affected clinical care and subjective end points, such as quality of life and exercise capacity. We only included patients who were receiving renin–angiotensin system inhibitors at the time of randomisation, thus excluding those who had already discontinued these agents. Conclusions Discontinuing renin–angiotensin system inhibitors in advanced and progressive chronic kidney disease does not cause a clinically relevant change in estimated glomerular filtration rate or difference in its long-term decline. Future work Future work should focus on updating clinical guidelines. Further analyses, in addition to the prespecified analyses, may be undertaken if new estimated glomerular filtration rate equations are introduced into clinical practice. Subgroup analysis by kidney disease aetiology and gender may be undertaken to look for potential differences in outcome in specific groups. Trial registration This trial is registered as STOP ACEi EudraCT Number, 2013-003798-82; ISRCTN62869767. Funding This award was funded by the Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 11/30/07), a Medical Research Council (MRC) and National Institute for Health and Care Research (NIHR) partnership. This is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 5. See the NIHR Funding and Awards website for further award information.

Work statistics at first-passage times

We investigate the work fluctuations in an overdamped non-equilibrium process that is stopped at a stochastic time. The latter is characterised by a first passage event that marks the completion of the non-equilibrium process. In particular, we consider a particle diffusing in one dimension in the presence of a time-dependent potential U(x,t)=k|x−vt|n/n , where k > 0 is the stiffness and n > 0 is the order of the potential. Moreover, the particle is confined between two absorbing walls, located at L±(t) , that move with a constant velocity v and are initially located at L±(0)=±L . As soon as the particle reaches any of the boundaries, the process is said to be completed and here, we compute the work done W by the particle in the modulated trap upto this random time. Employing the Feynman–Kac path integral approach, we find that the typical values of the work scale with L with a crucial dependence on the order n. While for n > 1, we show that ⟨W⟩∼L1−n expkLn/n−vL/D for large L, we get an algebraic scaling of the form ⟨W⟩∼Ln for the n < 1 case. The marginal case of n = 1 is exactly solvable and our analysis unravels three distinct scaling behaviours: (i) ⟨W⟩∼L for v > k, (ii) ⟨W⟩∼L2 for v = k and (iii) ⟨W⟩∼exp−(v−k)L for v < k. For all cases, we also obtain the probability distribution associated with the typical values of W. Finally, we observe an interesting set of relations between the relative fluctuations of the work done and the first-passage time for different n—which we argue physically. Our results are well supported by the numerical simulations.

Queuing Network Models of Multiservice RANs

In this paper, we present a new queuing network model for the analysis of a portion of a radio access network (RAN) comprising macro cell base stations (BSs) and small cell BSs offering ”streaming” and ”elastic” services. Streaming services require a certain data rate for a random time. The required data rates depend on the type of service, e.g., audio and video. Elastic services require the transfer of random data volumes, and their data rate adjusts dynamically based on the capacity not utilized by the streaming services. To derive performance measures for the proposed model we develop a computationally efficient framework that exploits a new product form result for streaming services, relying on a well-known blocking policy, and an approximate product form for elastic services. Insensitivity to the distribution of service requirements holds in the case of negligible end user mobility. We show the high accuracy of our model in predicting the performance of practical system configurations by conducting a thorough comparison between the model’s results and those obtained from a detailed discrete-event simulator. Through this analysis, we uncover significant counter-intuitive behaviors that arise from the competition between streaming services with diverse demands, and that are effectively captured and predicted by our modeling approach. Our computationally efficient queuing model is a useful new tool to support design and planning of multiservice RANs whose complex structures result from the coexistence of BSs of different generations in dense areas.

Asymptotics for the ruin probability in a proportional reinsurance risk model with dependent insurance and financial risks

. This article studies the joint ruin problem for two insurance companies that divide claims in positive proportions (modeling an insurance and re-insurance company). The arrival times of claims are delayed by a common random time. Suppose that the two insurance companies are allowed to make risk-free and risky investments, and the price processes of the corresponding investment portfolios are exponentials of jump-diffusion processes with common jumps. Furthermore, assuming that the claim sizes and their corresponding investment return jump possess a dependence structure, this article establishes an asymptotic formula for the ruin probability.