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The Airport Risk Assessment Model (ARAM) model automatically determines the optimal allocation of limited security resources to minimize total risk to the airport. At the same time, the model adds an element of randomness to the, reducing the predictability of asset schedules and further increasing countermeasure effectiveness.
Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.
The PRO-X Research Reactor Database is a functioning tool that can be used to quickly view attributes of past, current, and future research reactors. It contains all data found in the IAEA Research Reactor Database and entries generated from the M3 fuel conversion program. Additionally, it can be downloaded to any computer with the ability to run Microsoft ACCESS. Several research reactor attributes visible to the user have a limited amount of information, much of which is available and vetted for entry into the database. However, the necessary resources have not been allocated to add this information to the data tables. A change management system has also been developed to track changes to the database. This would need to be incorporated into the database program prior to data table modification in order to ensure proper management of the changes.
The motivation for this project stemmed from the challenges associated with rapidly refueling heavy-duty hydrogen fuel cell electric trucks (FCETs). Current manual refueling processes for fast refueling involve large, heavy equipment (e.g., hoses three times heavier than standard) and pose ergonomic risks and potential for equipment damage. The goal was to develop and test an autonomous fueling system to improve ergonomics, enhance safety, increase equipment durability through design improvements, and potentially speed up the fueling process. This project aimed to add to the understanding of autonomous systems in the context of heavy-duty hydrogen refueling, evaluating the technical effectiveness of potential concepts. A successfully developed system would benefit the public by facilitating the adoption of zero-emission heavy-duty transport, reducing reliance on manual labor for a physically demanding task, and potentially improving the safety and efficiency of hydrogen refueling infrastructure. The major accomplishment during the project's active period was the completion of the system-level architecture task. This involved establishing a detailed list of system requirements covering interfaces, environmental conditions, regulatory compliance, industry standards, safety, security, performance capabilities, and optional features. Five key use cases for the autonomous system were also identified. However, due to internal restructuring at Nikola, the necessary resources could not be allocated to continue the project. Consequently, Nikola opted to discontinue the project. The award was mutually terminated by Nikola and the DOE.
Monte Carlo-based project risk models are essential tools in project risk management, particularly for large, complex projects in which it is difficult to glean intuitively the importance of individual risks to the overall project. Conventional correlation-based methods for measuring the importance of contributing risks in such models are argued here to be both statistically flawed and unintuitive in the importance metrics they produce. Drawing on and adapting methods more traditionally associated with engineering risk models, Risk Reduction Worth (RRW) is introduced as an importance measure. RRW is shown to be statistically robust and provide an easily interpretable, quantitative measure of the importance of a risk to the overall performance of a project; specifically, measuring the extent to which removal of a risk would improve confidence in project outcomes. To ensure practicality of the method, an approach is described in which RRWs can be produced for all contributing risks based on a single Monte Carlo sample. This is demonstrated through application to a large, complex project risk model from which intuitive risk importance insights provide a robust, transparent basis for the allocation of risk management resources.
Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (kapp) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.
The interconnection among food–energy–water (FEW) systems in meeting societal demands is broadly acknowledged. Similarly, competitive or synergistic allocations of water and energy resources for agricultural production, manufacturing, and human consumption are understood, and their economic impacts can be predicted. Far less appreciated and understood are the outcomes of the FEW nexus in response to operation changes in agricultural practices and the associated technological innovations for future generations. Also, the inter-scale and feedback effects of emerging technology-driven resource reallocation and decision-making on FEW systems are largely unknown. For example, how do the agroeconomic feedbacks of intelligent technologies influence the FEW nexus of agricultural production under environmental and demographic changes? How does the necessary water allocation for powering non-powered dams and pumped-storage hydropower generation influence agricultural production and municipal water supply maintenance? How do solar and wind energy farms influence land use for agriculture and the rural economy? In turn, how can the generated solar and wind energy help reduce the cost of groundwater extraction or water desalination?
Curbspace is a limited resource in urban areas. Delivery, ridehailing and passenger vehicles must compete for spaces at the curb. Cities are increasingly adjusting curb rules and allocating curb spaces for uses other than short-term paid parking, yet they lack the tools or data needed to make informed decisions. In this research, we analyze and quantify the impacts of different curb use allocations on curb performance through simulation, covering various mixes of curbspace uses (bus stops, paid parking, passenger pick-up/drop-off zones, and commercial vehicle loading zones), parking rules, and driver rule compliance. Three metrics (including two new ones) are developed to evaluate the performance of the curb, covering productivity and accessibility of passengers and goods, and CO 2 emissions. The metrics are calculated for each scenario across a wide range of input parameters (traffic volume, parking demand rate, vehicle dwell time, and street design speed) and compared to each other and to a baseline scenario. This work can inform policy decisions by providing municipalities a tool to analyze various curb management strategies and choose the ones that produce results more in line with their policy goals.
While the computing landscape supporting LHC experiments is currently dominated by x86 processors at WLCG sites, this configuration will evolve in the coming years. LHC collaborations will be increasingly employing HPC and Cloud facilities to process the vast amounts of data expected during the LHC Run 3 and the future HL-LHC phase. These facilities often feature diverse compute resources, including alternative CPU architectures like ARM and IBM Power, as well as a variety of GPU specifications. Using these heterogeneous resources efficiently is thus essential for the LHC collaborations reaching their future scientific goals. The Submission Infrastructure (SI) is a central element in CMS Computing, enabling resource acquisition and exploitation by CMS data processing, simulation and analysis tasks. The SI must therefore be adapted to ensure access and optimal utilization of this heterogeneous compute capacity. Some steps in this evolution have been already taken, as CMS is currently using opportunistically a small pool of GPU slots provided mainly at the CMS WLCG sites. Additionally, Power9 processors have been validated for CMS production at the Marconi-100 cluster at CINECA. This note will describe the updated capabilities of the SI to continue ensuring the efficient allocation and use of computing resources by CMS, despite their increasing diversity. The next steps towards a full integration and support of heterogeneous resources according to CMS needs will also be reported.
Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.
Abstract Quantifying the impact of drought on microbial processes and its consequences for soil carbon cycling is hindered by the lack of underlying mechanistic understanding. Therefore, there is a need to scale up the physiological response to changing water status from individual soil microbes to collective communities across different ecosystems. Here we propose the use of a framework that incorporates trait‐based ecology to link drought‐impacted microbial processes to rates of soil carbon decomposition and stabilisation. We briefly synthesise existing knowledge on the effects of drought on microbial physiology at the individual to community scale, before integrating this understanding within a framework incorporating life‐history strategy, ecological strategy and biochemistry. This framework highlights a dynamic allocation to high yield (Y), resource acquisition (A) and stress tolerance (S) pathways as environmental conditions change. Y‐A‐S strategies represent sets of traits that tend to correlate due to physiological or evolutionary trade‐offs. This framework enables assessment of microbial processes along two key environmental gradients of water and resource availability, both of which are constrained by drought. The variable chemistry of biomass and necromass produced under different physiological strategies in response to drying–rewetting impacts organic matter decomposition and stabilisation in soils, and should also be considered when quantifying soil carbon balance. We highlight that diversion of resources away from microbial growth can alter soil organic matter chemistry and its persistence depending on the kind of microbial compounds produced. To advance such a framework, we highlight avenues of research that would enable the further identification and quantification of traits linked to Y‐A‐S strategies and the physiological outcomes at the community level under drought and rewetting, and conclude by hypothesising how ecosystem‐level changes might feedback on to the soil carbon cycle. A scalable understanding of microbial drought‐response mechanisms affecting soil carbon cycling will transform the way microbial physiology is represented in ecosystem studies. Read the free Plain Language Summary for this article on the Journal blog.
The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.
With the goal of evaluating biomass resource availability in certain regions in the United States, the Department of Energy, Department of Agriculture, Environmental Protection Agency, national laboratories, and Forest Service research laboratories, together with academic and industry collaborators, performed a resource analysis study, 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy, Volume 1: Economic Availability of Feedstocks, using POLYSYS model to estimate biomass potential. The study provided county- and state-level biomass potential projections, with biomass feedstock production estimates and suitability assessment of lands acreage likely to transition to energy crops. For this analysis, we estimate biomass supply by year 2028, more specifically, lands acreage transition to switchgrass in the Midwest of the United States, across 50 counties in 3 states (Nebraska, Kansas, and Colorado). We propose a multi-criteria site suitability framework, to determine the potential acreage and production for switchgrass, at the field level. Site selection criteria span agronomic and ecosystem services such as previous land cover, crop rotation, soil water holding capacity, crop productivity, field efficiency, topography, soil organic matter, soil leaching propensity, distance to surface water bodies, and soil erosivity. Efficient agricultural field selection for dedicated energy crops field allocation is critical to evaluate biomass resource availability, and it is our position that estimation at the field level would provide a more accurate and useful estimation to support an emerging bioeconomy. Using field suitability scores and county-level assessments, we model land allocation decision for energy crop in the future, estimating the spatial distribution of dynamics of energy crop adoption with priority to fields scoring the highest. Additionally, compared with POLYSYS results from the report mentioned above, our method shows good performance overall, with about 2% and 1.2% of difference in switchgrass acreage allocation and crop production, respectively.
Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.
Abstract Skewed sex ratios at birth are widely reported in wild populations, however, the extent to which parents are able to modulate the sex ratio of offspring to maximize their own fitness remains unclear. This is particularly true for highly polytocous species as maximizing fitness may include trade‐offs between sex ratio and the size and number of offspring in litters. In such cases, it may be adaptive for mothers to adjust both the number of offspring per litter and offspring sex to maximize individual fitness. Investigating maternal sex allocation in wild pigs ( Sus scrofa ) under stochastic environmental conditions, we predicted that under favorable conditions, high‐quality mothers (larger and older) would produce male‐biased litters and invest more in producing larger litters with more males. We also predicted sex ratio would vary relative to litter size, with a male‐bias among smaller litters. We found evidence that increasing wild boar ancestry, maternal age and condition, and resource availability may weakly contribute to male‐biased sex ratio, however, unknown factors not measured in this study are assumed to be more influential. High‐quality mothers allocated more resources to litter production, but this relationship was driven by adjustment of litter size, not sex ratio. There was no relationship between sex ratio and litter size. Collectively, our results emphasized that adjustment of litter size appeared to be the primary reproductive characteristic manipulated in wild pigs to increase fitness rather than adjustment of offspring sex ratio.
Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained simultaneously, sharing clients’ computing resources, which we call Multiple-Model Federated Learning (MMFL). Current MMFL algorithms use naïve average-based client-task allocation schemes that often lead to unfair performance when FL tasks have heterogeneous difficulty levels, as the more difficult tasks may need more client participation to train effectively. Furthermore, in the MMFL setting, we face a further challenge that some clients may prefer training specific tasks to others, and may not even be willing to train other tasks, e.g., due to high computational costs, which may exacerbate unfairness in training outcomes across tasks. We address both challenges by firstly designing FedFairMMFL, a difficulty-aware algorithm that dynamically allocates clients to tasks in each training round, based on the tasks’ current performance levels. We provide guarantees on the resulting task fairness and FedFairMMFL’s convergence rate. We then propose novel auction designs that incentivizes clients to train multiple tasks, so as to fairly distribute clients’ training efforts across the tasks, and extend our convergence guarantees to this setting. Here, we finally evaluate our algorithm with multiple sets of learning tasks on real world datasets, showing that our algorithm improves fairness by improving the final model accuracy and convergence speed of the worst performing tasks, while maintaining the average accuracy across tasks.
Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.
Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.