Rigorous Model Research Articles

Development of a mixed chamber experimental and CFD database of indoor environments

Recent studies reveal that individuals spend the majority of their time indoors, and indoor air quality (IAQ) plays a crucial role in occupant health and productivity. However, current research primarily focuses on specific environments with complex variables and challenging boundary conditions, limiting our understanding of IAQ and thermal comfort. Many studies use fixed experimental chambers, restricting control over boundary conditions. To address these limitations, we propose a novel data-driven smart indoor environmental control framework. This study advocates for a standardized database that integrates diverse IAQ and thermal comfort metrics to enhance indoor environmental conditions and presents the initial phase of this endeavor, detailing the development of a chamber experimental database. This phase involves examining six standardized experiments and over 96 distinct case studies, resulting in a comprehensive dataset of 528,000 entries, rigorously analyzed. Simultaneously, this research enhances its empirical foundation by developing and validating computational fluid dynamics (CFD) models. These models complement and refine the database, increasing its utility and robustness. Notably, experimental findings regarding CO2 and temperature variations indicate a significant 32 % and 65 % decrease in average relative CO2 concentration when the inlet air speed increased from 1.7 to 2.1 and 2.5 m/s, respectively. Spatial variations were also observed, with areas near the inlet surface having lower pollution concentrations and higher air speeds. In summary, this research contributes significantly to the establishment of a comprehensive database, combined with insights from rigorous experimentation and computational modeling, paves the way for advancements in smart indoor environmental control strategies.

Automated Upscaling Via Symbolic Computing for Thermal Runaway Analysis in Li-Ion Battery Modules

Despite the availability of advanced numerical capabilities and computational power, complex multi-physical, multiscale systems continue to challenge modeling efforts due to their elusive behaviors and nontrivial couplings. High-fidelity simulations have paved the way for simulating such systems, but the associated computational costs prohibit such tools from being used in iterative routines for engineering design and optimization. While numerical strategies for accelerating complex at-scale multi-physical simulations exist, they often concede predictive accuracy and lack rigorous justification. Alternatively, methods for developing rigorous multiscale models (e.g., upscaling techniques) offer a priori modeling error guarantees, but are primarily used in academic settings due to their requirements in time and specialized expertise for handling analytically intractable derivations. In our previous work, we developed an automated upscaling engine, Symbolica, that uses symbolic computation to avoid these overheads and rapidly develop multiscale models via rigorous homogenization theory. In this work, we extend Symbolica’s model development capabilities to accommodate multi-domain structures and demonstrate Symbolica’s generality in model development by applying it to a real-world problem; namely, to analyze thermal runaway in Li-ion battery modules. We obtain homogenized models that capture the sharp spatiotemporal gradients associated with thermal runaway to within the guaranteed modeling error. Upon numerically validating the models, we demonstrate how Symbolica can be used to reduce the level of effort in rigorous multiscale modeling and model implementation for real-world applications. Figure 1

Thermal Runaway in Battery Cells: Guiding Principles for Continuum Scale Modeling

Lithium-ion batteries are sensitive to temperature. Low-temperature or high-temperature operation may lead to lithium plating and dendrites, SEI decomposition, and separator collapse. Such phenomena may cause internal short circuits, rapid heat generation and, subsequently, may trigger thermal runaway, resulting in fire or explosion [1][2]. Hence, accurate modeling and prediction of the thermal response of battery cells inside the pack is critical to preventing safety hazards as well as optimizing battery performance. Battery thermal management system (BTMS) plays a significant role in maintaining an optimal operation temperature and ensuring thermal safety for electric vehicles [3]. However, the large number of battery cells inside one module makes it challenging for BTMS algorithms to realize real-time simulations based on fully resolved physics-based models. The conventional lumped capacitance (LC) thermal model achieves reduced computation costs under the hypothesis that temperature is spatially uniform. Nevertheless, such a model works better for small Biot number conditions, usually under low C-rate operation and moderate cooling scenarios, and it lacks justification under a broad range of battery operating conditions and materials [4]. To overcome such limitations, rigorous multiscale models, based on mathematical coarse-graining theories, have been developed to predict the thermal response of batteries at the module scale [5] while not compromising on computational cost. In this work, we investigate the accuracy of the classical LC thermal model for twenty potential battery modules constructed with commercial batteries and packing materials. We show that the classical LC thermal model may be inaccurate under a broad range of design conditions. In the context of multiscale modeling, we identify which effective models should be instead used, and analyze the impact of SOC and the relative strength of heat insulation and heat dissipation on model selection and accuracy. This work provides guidance, based on first principles and rigorous multiscale theory, on best practices for model selection in practical applications involving thermal behavior and thermal runaway in battery cells at the module scale.

Material Dynamics of Nickel (oxy)Hydroxide As an Alcohol Oxidation Electrocatalyst

Electrochemical alcohol oxidation is an attractive reaction for renewable-energy driven production of value-added chemicals, like precursors for acrylic (acrolein from glycerol) and nylon (adipic acid from cyclohexanol). Implementing selective, earth-abundant catalysts for alcohol oxidation can improve electrolyzer efficiency and economics. For example, replacing the energy intensive oxygen evolution reaction (OER) in CO2 electrolyzers with alcohol oxidation can decrease operating voltage by about 0.85 V and reduce electricity requirements up to 53% while also reducing carbon emissions from chemical production.1 Electrochemical alcohol oxidation catalyzed by as synthesized nickel oxyhydroxide (NiOOH) is hypothesized to proceed through two pathways, a chemical, redox pathway preferred by aldehydes (geminal diols in alkaline electrolyte), and a potential dependent pathway preferred by alcohols.2 Many studies have focused on understanding reaction mechanisms and device performance, however, transition metal alcohol oxidation catalysts, such as NiOOH, are known to undergo reduction/oxidation, phase changes, and even dissolution in the same potential window as alcohol oxidation.3 It is still unclear how these processes affect catalyst material stability and electrochemical performance.In this work, we probe the performance stability and material stability of Ni(OH)2 for alcohol oxidation in alkaline media through electrochemical cycling and chronoamperometry. We use a rigorously Fe-free environment to mitigate performance impacts from Fe-contamination common in non-purified alkaline electrochemical studies.4 We show that the rate of change of alcohol oxidation current density (mA cm-2) depends on reaction pathway (redox vs. potential dependent), alcohol concentration, and side chain structure. Using EC-MS and NMR, we also probe changes in performance in yield and selectivity by quantifying oxidation products, including oxygen gas. These changes in performance are correlated to material changes through monitoring dissolution with on-line ICP-MS. Furthermore, to disentangle the role of potential and reactant (e.g. alcohol or water) on stability, we elucidate oxidation state and local structural changes using in situ X-ray Absorption Spectroscopy (XAS). Insights from this rigorous model system are directly translatable to the broader goal of developing efficient, long-lasting catalysts for industrial deployment of electrochemical organic reactions powered by clean energy. References. Verma, Sumit, Shawn Lu, and Paul JA Kenis. Nature Energy 4.6 (2019): 466-474.2.Bender, Michael T., and Kyoung‐Shin Choi. ChemSusChem 15.13 (2022): e202200675.Klaus, Shannon, et al. The Journal of Physical Chemistry C 119.13 (2015): 7243-7254Wei, Lingze, et al. ACS Catalysis 13.7 (2023): 4272-4282.

Optimization and safety analysis for CO oxidative coupling reactor with co-current thermal management

The CO oxidative coupling with methyl nitrite (MN) to dimethyl oxalate (DMO) in syngas-based ethylene glycol (EG) process faces challenges in low conversion and capacity per unit. In this work, by using a rigorous reactor model and NSGA-II algorithm, Pareto solutions balancing production capacity and safety were obtained to compare thermal control schemes. Under the specified domain, the optimized co-current-flow scheme achieved a DMO yield of 1.0 gDMO/(gcat∙h), surpassing the conventional isothermal-coolant scheme (0.55 gDMO/(gcat∙h)), and even under conservative conditions, the co-current scheme achieves 43% enhancement in capacity. Subsequently, the dynamic behavior of co-current reactor was compared with the isothermal-coolant one in much depth on the conditions determined by optimization of both performance and sensitivity. The results show that the reactor with co-current scheme provides the operators with twice more time to rectify operational biases, and an alarm of the wrong-way behavior when GHSV and inlet temperature are changed.

Quantifying suicide contagion at population scale.

The spread of suicidal behavior among individuals is often described as a contagion; however, rigorous modeling of suicide as a dynamic, contagious process is minimal. Here, we develop and validate a model-inference system depicting suicide ideation and death and use it to quantify the contagion processes in the US associated with two prominent celebrity suicide events: Robin Williams during 2014 and Kate Spade and Anthony Bourdain, which occurred 3 days apart during 2018. We show that both events produced large transient increases of suicide contagion contact rates, i.e., the spread of suicidal thought and behavior, and a period of elevated suicidal ideation in the general population. Our modeling approach provides a framework for quantifying suicidal contagion and better understanding, preventing, and containing its spread.

Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants

The development of technology and changes in job market dynamics have created new challenges in aligning education with industry needs. In this research, the XGBoost model with hyperparameter tuning was applied for industry classification on job applicant data taken from the Kaggle dataset LinkedIn Job Postings in 2023. This dataset consists of 23 attributes with a total of 33,085 job vacancy data points. The experimental results show that both the model without hyperparameter tuning and with GridSearchCV produce the same classification accuracy, which is 0.89 or 89%, with stable precision, recall, and F1-Score values. The best parameters found in this study are colsample_bytree = 1.0, learning_rate = 0.3, max_depth = 6, min_child_weight = 1, n_estimators = 100, and subsample = 1.0. However, cross-validation using k-fold shows a significant increase in accuracy to 0.90, or 90%. This finding confirms that the use of cross-validation can improve the performance estimation of the model more accurately and robustly by utilizing all available data for training and testing. Moreover, the implementation of cross-validation demonstrates the importance of leveraging all data points to enhance model reliability and robustness. Future research can explore alternative hyperparameter tuning methods and apply the model to larger datasets to further validate the generalizability and reliability of the XGBoost model in different application contexts. Thus, this study underscores the significance of rigorous model evaluation techniques in achieving high-performing machine learning models

Mechanistic Pavement Modeling of Typical Brazilian Pavements: Cohesive Zone Fracture Modeling and Continuum Damage Modeling

Pavement performance evaluation with respect to fatigue cracking has seen a major shift toward more rigorous mechanistic models that can capture the complex damage response of the mixtures more accurately. This study utilized two mechanistic methods (cohesive zone [CZ]-based fracture modeling and continuum damage [CD] modeling) to study the cracking behavior of asphalt mixtures and subsequently predict the damage-associated performance of pavements. A Superpave mixture designed for Brazilian highway conditions was selected to evaluate the two modeling approaches for the performance of the mixture when utilized as a surface course within typical Brazilian highway pavements. The mixture was first evaluated for its linear viscoelastic properties, and then the damage characteristics of the mixture were obtained through two different experiments: the semi-circular bending (SCB) beam test and the cyclic fatigue (CF) test. The SCB tests performed at different loading rates were used to obtain the model parameters of the nonlinear viscoelastic CZ model with a Gaussian damage evolution criterion, while the CF tests were performed at different strain amplitudes, and the mixture-specific damage characteristic curve and the fatigue failure criterion were obtained using the simplified-viscoelastic continuum damage model. From a design perspective, three pavement structures that varied in geometry (pavement layer thickness) and underlying layer properties were selected while retaining the same asphalt mixture. The three pavement scenarios were evaluated for the fatigue cracking performance from each of the mechanistic modeling methods. The results indicate that both methods rank the performance of the pavement structures in a similar manner, while the damage initiation and progression were seen to be different because of the different mechanics in modeling cracks.

Measuring Traffic Speed Change After the Reallocation of Road Space for Cycling: A Data-Driven Analysis for Bogotá

In recent years, many local authorities have sought to accommodate cycling and active transportation in urban areas by creating dedicated spaces for them. However, densely populated cities face a land shortage, making it challenging to introduce measures such as bike lanes without impacting existing road users. Therefore, understanding the potential impacts of these initiatives is essential for informed decision-making, effective policy formulation, and successful urban planning strategies. In 2020, Bogotá, Colombia introduced extensive bike lanes along important corridors previously exclusively designated for motorized vehicles. The modifications were made possible in response to the unique context of the COVID-19 pandemic. The objective of this study is to provide a data-driven assessment of the impacts of some of the implemented bike lanes by utilizing traffic speed data from 2019 (prior to the lane modifications) and 2021 (after the modifications). Rigorous statistical analysis and modeling were employed to measure the changes on speed levels of road traffic, on both, the treatment group with modified roads as well as on the control group with non-modified roads within the study area. Due to the intricate factors influencing traffic speeds, spatial relationships between road segments and estimation errors were explicitly accounted for. Findings revealed a maximum speed reduction of 4.5 km/h (equivalent to a 19 % decrease), for users on modified roads. Apart from quantifying the worsened speed conditions because of the reduced road space on treated segments, results also highlight that certain parallel road segments experienced an increase in speed levels. This observation lends support to the intervention and emphasizes the importance of comprehensively assessing the entire road network when considering the implementation of bike lane projects in similar context cities.

Computational urban science needs to go beyond computational

In this paper, I advocate for a radical expansion of computational urban science to encompass a multidisciplinary, human-centered approach, addressing the inadequacies of traditional methodologies in capturing the complexities of urban life. Building on insights from Jane M. Jacobs and others, I argue that integrating computational tools with disciplines like sociology, anthropology, and urban planning can significantly enhance our understanding and management of urban environments. This integration facilitates a deeper analysis of cultural phenomena, improves urban policy design, and promotes more sustainable, inclusive urban development. By embracing qualitative research methods—such as ethnography and participatory observations—alongside computational analysis, I highlight the importance of capturing the nuanced social fabrics and subjective experiences that define urban areas. I also stress the necessity of including community stakeholders in the research process to ensure that urban science not only analyzes but also improves the lived experiences of urban populations. Furthermore, I underscore the need for ethical governance and the mitigation of biases inherent in computational tools, proposing rigorous model auditing and the inclusion of diverse perspectives in model development. Overall, this work champions a holistic approach to urban science, aiming to make cities smarter, more equitable, and responsive to their residents’ needs.

Cascaded-prism multi-mode beam scanning method for three-dimensional imaging lidar

Light detection and ranging (lidar) has emerged as an indispensable approach to three-dimensional (3D) perception, which probably suffers from performance limitations with traditional beam steering devices. In this paper, we investigate a cascaded-prism beam scanning method to enhance the versatility of 3D imaging lidar systems. The cascaded-prism-based 3D lidar architecture is theoretically developed with an emphasis on a rigorous beam scan model. By exploiting the additional flexibility of cascaded prisms, the lidar can achieve beam scanning through prism rotation in either step-motion or constant-speed mode, which exhibits superior agility and adaptability in multi-mode pattern analysis. Moreover, the cascaded-prism beam scanning lidar is demonstrated with a 3D imaging performance evaluation in terms of field of view, angular resolution and sampling density. It proves that the cascaded-prism beam scanner can offer lidar systems with flexible and configurable 3D imaging capability while balancing between a wide field of view and high angular resolution.

Thermodynamic modeling and process evaluation of advanced ionic liquid-based solvents for CO2/CH4 separation

Carbon capture technology is a prospective strategy to address the increasing concentration of CO2 in the atmosphere, with the core challenge of developing new cost-effective processes. In this work, the energy analysis and economic evaluation were conducted based on rigorous thermodynamic models and the process simulation results of a novel chemical absorption-dominated hybrid solvent which consists of functional ionic liquid of choline triazole ([Cho][Triz]) and sulfolane (TMS) to separate CO2 from shale gas. The solubility of CO2 and CH4 in different solvents were calculated using phase equilibrium model including the NRTL activity coefficient equation, the RK equation of state, chemical reaction equilibrium equations, and the mass balance equation. The physical properties of the ionic liquid-based solvent systems were calculated with empirical equations which was corrected using experimental data. The results obtained from the thermodynamic models exhibited good agreement with experimental data. Subsequently, the established models and the parameters obtained were embedded into Aspen Plus for further analysis. The total CO2 capture energy consumption of 1.65 GJ·t−1 CO2 and the cost of 48.07 $·t−1 CO2 were achieved using the new solvent when the mass fraction of IL was 60 wt%. Compared with the commercial 30 wt% MDEA carbon capture process, it reduced the energy consumption and economic cost of 64.16 % and 45.59 %, respectively.

Rigorous development and comparison of multi-dimensional reactor models encompassing the catalyst domains for steam methane reforming

Hydrogen is being considered as a possible large-scale carbon-free industrial combustion and transportation fuel. Steam methane reforming (SMR) reaction is the most dominant industrial hydrogen production method. This paper develops comprehensive models and delves into the influences of the multi-dimensional reactor and catalyst domains in SMR. Several single-scale or multi-scale SMR packed-bed reactor models are developed with the Aspen Custom Modeler and solved by the built-in finite difference method. These models are categorized into pseudo-homogeneous and heterogeneous models. This study thoroughly compares and discusses the effects of axial and/or radial mixing due to mass/heat dispersion as well as interfacial and intraparticle mass/heat transport phenomena across various types of reactor-catalyst configurations. Furthermore, insights into the prediction accuracy of each model under various operating conditions are provided. This research fills a gap in the existing literature by developing rigorous dynamic models for SMR reactors to investigate their profiles and performances, offering a clearer understanding of the relative importance of different modeling approaches in predicting the behavior of SMR reactors. The findings provide valuable guidelines for researchers and industry professionals in selecting the appropriate model for their design, analysis, optimization, and control tasks.

Comparison, operation and cooling design of three general reactor types for Power-to-Ammonia processes

This work investigates the heat transfer design and performance of three general types of ammonia reactors for Power-to-Ammonia (P2A). Specifically, the reactors are an adiabatic quench cooled reactor (AQCR), an adiabatic indirect cooled reactor (AICR), and an internal direct cooled reactor (IDCR).We present rigorous steady-state and dynamic models for the three reactor types. Steady-state optimisation at nominal load shows that the AICR and IDCR achieve the highest reactant conversion at 30.0% and 29.4% respectively, whereas the AQCR reaction conversion is significantly lower at 26.9%. Through open-loop simulations around the optimal operating point, we illustrate the unstable oscillatory dynamics of the AQCR. In contrast, the AICR and IDCR showcase stable and fast decaying oscillations. Nonetheless, our stability analysis reveals that the optimal operating points for all reactors are situated precariously close to the reactor extinction threshold. As a result, even minor disturbances pose a substantial risk of extinguishing the reactors at optimal operating conditions.We optimise the reactors across an operating window from 30% to 130% of its nominal load, reflecting varying load in a P2A plant. Over this operational range, the AICR and IDCR yield similar reactant conversions and demonstrate adaptability to varying loads by achieving significantly higher conversions at lower loads. In contrast, the AQCR shows a less pronounced increase in reactant conversion at reduced loads.Considering the location of the optimal operating points close to reactor extinction, we evaluate operating the reactors at a 15 K higher feed temperature than optimal value (stability margin). Our analysis demonstrates that a 15 K safety margin only slightly reduces ammonia conversion across the operating window, while ensuring stable operation even in the event of a 15% decrease in the catalyst activity.

Design and evaluation of integrated energy system combining solar energy and compressed-air energy storage

A new integrated energy system (IES) has been proposed by combining the cooling, heating, and power generation (CCHP) system coupled with PV/T and compressed air energy storage (CAES). Based on the developed control operation strategy, rigorous system modeling and dynamic simulation are carried out by TRNSYS to determine the integrated operation effect of each subsystem. Energy utilization efficiency and annual value of costs were adopted to assess the benefits of the system. A building in Gansu Longnan area is selected as a study case. The study results show that the proposed system can meet the building load demand well. The PV efficiency and thermal efficiency are 0.17 and 0.19, respectively. The gas ratio of CAES storage tank is 0.94, which is safe and stable. In addition, the energy utilization is maximum when the internal combustion engine proportioning capacity is 30 % of the maximum heat load, which is 89.13 %. The annual value of the cost is 2.49 × 107 RMB during 40 years of operation.

Research on control parameters of high-speed maglev train under stochastic track irregularities

During operational phases, maglev trains encounter track irregularities, presenting a formidable challenge to the integrity of their control systems owing to the inherently stochastic nature, a challenge accentuated in the context of high-speed maglev trains. This study aims to comprehensively examine control parameters within the context of stochastic excitation resulting from track irregularities. Through the development of a rigorous stochastic response analysis model utilizing the pseudo excitation method, the investigation delves into the dynamics of an EMS-type high-speed maglev train-rigid track system. Evaluation indices, derived from the power spectral density of the maglev train's response, are established to quantify both control stability and operational stability. Subsequently, based on these indices, a recommended range of PD control parameters is proposed, accounting for the requirements stipulated by the evaluation metrics. The study elucidates that variation in PD control parameters exert discernible effects on the system's stability, primarily altering the nature frequency and damping ratio. Specifically, control stability demonstrates a positive correlation with increasing PD control parameters, while operational stability exhibits a nuanced relationship—initially bolstered by escalating proportional coefficients but subsequently tempered, albeit gradually, with heightened derivative coefficients. The delineated range of values for PD control parameters is meticulously determined, considering pertinent physical parameters of the electromagnetic system, such as mass, static suspension current, and static suspension gap, alongside factors like the Sperling indicator and suspension gap variation.

Rigorous modeling of the Traveling Grate stage in the iron ore pellet induration process

This work presents a robust phenomenological model for the Traveling Grate system in an iron ore pellet induration plant on an industrial scale. The system consists of a bed of pellets in constant movement, heated by perpendicular air currents. The aim is to integrate into a single model the details of the chemical and physical phenomena that occur in the system, including gas/pellet energy transfer, a three-stage drying mechanism, compressive strength, and kinetic models for coke combustion, magnetite oxidation, and carbonate calcination. The model demonstrated adherence to process data, with a maximum relative deviation of 2.35%. Simulations enabled the assessment of the influence of key parameters on the bed temperature, observing an inversely proportional relationship of the profile for grate velocity, pellet diameter, and bed height. Pellet quality was analyzed under different composition conditions and void fractions, highlighting the significant influence of the coke on compressive strength, limited by the vitrification phenomenon at high temperatures. This model enables critical analysis of phenomena that are difficult to detect in conventional operations and helps in defining operational ranges to optimize product quality and process energy efficiency.

On the Role of Early Case Detection and Treatment Failure in Controlling Tuberculosis Transmission: A Mathematical Modeling Study

Tuberculosis (TB) remains a pressing global health concern, demanding urgent attention to mitigate its spread and impact. In this study, we present a rigorous mathematical model of TB transmission that incorporates early case detection and addresses the critical issue of treatment failure. Through the development of a system of nonlinear ordinary differential equations, we conduct comprehensive analyses to assess the dynamics of TB transmission and the efficacy of intervention strategies. Our findings underscore the urgent need for effective TB control measures. Mathematical analyses reveal that the model exhibits a TB-free equilibrium, which is globally asymptotically stable only if the control reproduction number falls below one. However, we identify a concerning phenomenon: the model demonstrates a forward bifurcation when the control reproduction number equals one, suggesting that the disease-free equilibrium loses its stability, while simultaneously, the stable unique endemic equilibrium begins to emerge. Moreover, sensitivity analysis highlights the complex interplay between case detection rates, treatment failure probabilities, and TB transmission dynamics. Contrary to expectations, increasing case detection rates and minimizing treatment failure probabilities may not consistently reduce the basic reproduction number or the size of the infected population. Instead, there exists a critical threshold for intervention effectiveness, beyond which TB transmission can be significantly curtailed. Biologically, this phenomenon may occur if there is no balance between case detection and treatment efforts. If treatment quality does not improve, then case detection will not have a significant impact, and in the worst case scenario, it can exacerbate the intervention’s negative effects. These findings underscore the urgency of implementing targeted intervention strategies to combat TB transmission effectively. Failure to meet the critical intervention threshold risks undermining TB elimination efforts and exacerbating the global TB burden. Through numerical simulations, we elucidate potential intervention scenarios necessary for achieving TB elimination goals in human populations. In conclusion, our study highlights the urgent imperative for coordinated action to control TB transmission effectively. By elucidating the dynamics of TB spread and intervention efficacy, we provide valuable insights to inform evidence-based policy decisions and accelerate progress towards TB elimination on a global scale.

Integrated analysis of reliability, power, and performance for IoT devices and servers

The Internet of Things (IoT) is currently widely used in various sectors and spaces. IoT devices are becoming small yet powerful servers and perform server-like functions. Reliability is a critical aspect in both IoT devices and servers, as they work together to create a robust and dependable IoT ecosystem. Power and performance are two other major considerations of an IoT system. Modeling, analysis, evaluation, and optimization of reliability, power, and performance for IoT devices and servers are major components in IoT systems development and deployment. In this paper, we conduct an integrated study of reliability, power, and performance for IoT devices and servers by mathematically rigorous modeling and analysis. The contributions of the paper can be summarized as follows. We establish a continuous-time Markov chain (CTMC) model that incorporates server failure rate, server repair rate, task arrival rate, and task processing rate. Using such an analytical model, we can calculate the server availability, the average task response time, and the average power consumption. We point out that there is an optimal server speed that minimizes the power-time product and a combined cost-performance metric of power, performance, and reliability. We show the impact of server reliability on response time, power consumption, server utilization, and the power-performance tradeoff. To the best of the author’s knowledge, this is the first paper that takes a combined approach to modeling and analysis of reliability, power, and performance for IoT devices and servers. It has been noticed that there has been little such theoretically solid investigation in the existing literature. Therefore, this paper has made tangible contributions and significant advances in the joint understanding of reliability, power, performance, and their interplay in IoT devices and servers quantitatively and mathematically.

Fully Bayesian forecasts with evidence networks

Sensitivity forecasts inform the design of experiments and the direction of theoretical efforts. To arrive at representative results, Bayesian forecasts should marginalize their conclusions over uncertain parameters and noise realizations rather than picking fiducial values. However, this is typically computationally infeasible with current methods for forecasts of an experiment’s ability to distinguish between competing models. We thus propose a novel simulation-based methodology capable of providing expedient and rigorous Bayesian model comparison forecasts without relying on restrictive assumptions. Published by the American Physical Society 2024