Cost Metrics Research Articles

Lightcone bounds for quantum circuit mapping via uncomplexity

Efficiently mapping quantum circuits onto hardware is integral for the quantum compilation process, wherein a circuit is modified in accordance with a quantum processor’s connectivity. Many techniques currently exist for solving this problem, wherein SWAP-gate overhead is usually prioritized as a cost metric. We reconstitute quantum circuit mapping using tools from quantum information theory, showing that a lower bound, which we dub the lightcone bound, emerges for a circuit executed on hardware. We also develop an initial placement algorithm based on graph similarity search, aiding us in optimally placing circuit qubits onto a device. 600 realistic benchmarks using the IBM Qiskit compiler and a brute-force method are then tested against the lightcone bound, with results unambiguously verifying the veracity of the bound, while permitting trustworthy estimations of minimal overhead in near-term realizations of quantum algorithms. This work constitutes the first use of quantum circuit uncomplexity to practically-relevant quantum computing.

Supporting Organizations in Improving Employee Bulk E-mail --- A Tool Design and Evaluation Study

Organizations often send bulk emails to employees to make them aware of policy changes, organization plans, and events. Many of these emails, however, are long digests with many separate messages that waste employees' time and reduce their awareness. This study introduces CommTool--a prototype tool to help organizational communicators better understand their emails' performance and cost. We first interviewed 5 communicators and identified the need to measure the performance of each message within bulk email. Then we iteratively designed and deployed an organizational bulk email evaluation platform (CommTool), which enables communicators to get diverse message-level metrics such as reading time, relevance rate, comments, etc. We evaluated these designs through a 2-month field deployment with 5 communicators and 149 organization employees. We found that 1) the message-level metrics, such as reading time and relevance rate, helped communicators understand their audience and design bulk emails; 2) the cost and reputation metrics did not influence the organization leaders' decisions. We summarize with suggestions on designing organizational bulk email evaluation platforms that provide message-level performance and cost information.

A Secured Keystroke-Based Model for Preventing Social Engineering Attacks using Recurrent Neural Network

Among several authentication problems, preventing social engineering attacks using behavioural biometric approach has not received the required attention especially with focus on keystroke dynamics. This study aims to leverage the power of deep learning for more accurate and robust continuous authentication based on typing patterns. The proposed framework for this study utilized deep learning algorithm for behavioural biometrics authentication using Keystroke dynamics. The deep learning model was developed using Recurrent Neural Network (RNN) algorithm and was optimized was to obtain a better performance with Bayesian optimization which, eventually enhanced the model's accuracy. The dataset was split into training and testing in the model design phase and some hyperparameters such as dense, activation, batch size, sigmoid, filament, input size and epoch were used and optimized for building the deep learning algorithm. The RNN model is used to generate the evaluation metrics such as log loss, accuracy, precision and recall. The result presented the accuracy, precision, recall, and loss function as 100%, 100%, 100%, and 36% respectively for optimized model. The cost metrics yielded 0.0032, 0.0032, and 0.0006 MAE, MSE, and RMSE respectively. The developed KBB shows high level of social engineering attacks mitigation in comparison with the existing solution from the performance measure results.

Optimal Capacity Allocation for Life Cycle Multiobjective Integrated Energy Systems Considering Capacity Tariffs and Eco-Indicator 99

Traditional energy systems pose a significant threat to human social development due to fossil fuel depletion and environmental pollution. Integrated energy systems (IESs) are widely studied and applied due to their clean and low-carbon characteristics to achieve sustainable development. However, as integrated energy systems expand, their impact on ecosystems becomes more pronounced. This paper introduces the concept of the ecological damage index (EDI) to promote the sustainable development of integrated energy systems. Moreover, the introduction of a capacity tariff mechanism will impact the energy structure, making it essential to consider its effects on capacity allocation within integrated energy systems. This paper proposes a multiobjective optimization framework for constructing a capacity planning model for integrated energy systems, focusing on achieving a multidimensional balance between the economy, environment, and ecosystem using the life cycle assessment (LCA) method. Finally, the nondominated sorting genetic algorithm-II (NSGA-II) is employed to optimize the three objectives and obtain the Pareto frontier solution set. The optimal solution is selected from the solution set by combining the technique for order preference by similarity to ideal solution (TOPSIS) and Shannon entropy method. In comparison to scenarios with incomplete considerations, the multiobjective capacity optimization model proposed in this study exhibits significant improvements across the three metrics of cost, carbon emissions, and the ecological damage index, with a 19.05% reduction in costs, a 26.24% decrease in carbon emissions, and an 8.85% decrease in the ecological damage index. The study demonstrates that the model abandons traditional single-objective research methods by incorporating a multidimensional balance of the economy, environment, and ecosystems. This approach forms a foundational basis for selecting the optimal energy mix and achieving sustainable development in integrated energy systems. The life cycle assessment methodology evaluates impacts across all stages of integrated energy systems, providing a comprehensive basis for assessing and planning the sustainable development of the systems. The study offers guidance for the rational allocation of the integrated energy system capacity and advances the sustainable development of such systems.

Universal early-time growth in quantum circuit complexity

We show that quantum circuit complexity for the unitary time evolution operator of any time-independent Hamiltonian is bounded by linear growth at early times, independent of any choices of the fundamental gates or cost metric. Deviations from linear early-time growth arise from the commutation algebra of the gates and are manifestly negative for any circuit, decreasing the linear growth rate and leading to a bound on the growth rate of complexity of a circuit at early times. We illustrate this general result by applying it to qubit and harmonic oscillator systems, including the coupled and anharmonic oscillator. By discretizing free and interacting scalar field theories on a lattice, we are also able to extract the early-time behavior and dependence on the lattice spacing of complexity of these field theories in the continuum limit, demonstrating how this approach applies to systems that have been previously difficult to study using existing techniques for quantum circuit complexity.

Intelligent location-routing for sustainable reverse supply chain of end-of-life vehicles considering awareness cost and carbon penalty

This paper presents a comprehensive investigation into optimizing the reverse supply chain for end-of-life vehicles (ELVs) with a focus on sustainability considerations, including awareness cost and carbon penalty. A novel three-objective mathematical model is developed, leveraging fuzzy logic to address the complex nature of the problem. Two multi-objective algorithms, the Grey Wolf Optimizer (GWO) based on the Pareto boundary and the NSGA-II algorithm, are deployed to solve the model. A case study in China is conducted to validate the proposed model, with results compared against the outcomes of the algorithms. Additionally, a sensitivity analysis approach is employed to assess the impact of awareness cost and carbon penalty parameters. Furthermore, the Taguchi experimental design method is utilized to identify the optimal combination of parameter values. The findings reveal that the GWO algorithm surpasses NSGA-II in terms of solution quality and diversity, although NSGA-II demonstrates superior performance in uniformity and computational time. The sensitivity analysis highlights the positive correlation between increased awareness cost and various performance metrics, such as the number of collected vehicles, economic profit, and social profit, while also indicating a reduction in environmental impacts. Conversely, escalating carbon penalties leads to a decrease in the acceptance and processing of vehicles within the supply chain, resulting in diminished chain and social profits, despite the decrease in carbon penalties.

The Integration of AI and Machine Learning in Supply Chain Optimization: Enhancing Efficiency and Reducing Costs

One of the biggest issues today is the increasing intricacy of supply chain networks and supply chain networks becoming more global. Following is the research paper on supply chain management accompanying the integration of AI & ML for effectiveness & efficiency & reduction of cost impacts. The purpose of the research is to assess the effectiveness of adoption of Artificial Intelligence and Machine Learning tools based on predictive analytics, automation, and real-time decision models with supply chain management tendencies in demand forecasting, inventory control, and logistics. In line with the research design that is mixed method, the data were obtained from high-impact case studies and industry reports with further support from the literature review. The usefulness analysis of AI and ML was conducted in line with the supply chain performance measures that include lead time, cost and system metrics for the actual implementation. The results suggest that the application of AI and ML leads to the key performance improvement in companies, such as an average of 20% decrease in operational costs and 15% shorter delivery times. The study will also provide a new understanding of the real world incorporation of AI and ML in supply chain and a path forward in the literature and practice. These technologies reveal the development prospect of how these supply chains can be rebuilt to be more robust as well as flexible.

The Future of Content Marketing: Developing KPIs for Metaverse Content Marketing

In recent times, the Metaverse technology has gathered attention due to its futuristic capabilities. Being a relatively new concept, it is important to keep on exploring the possibilities of this technology. Content Marketing is a marketing method, which has been tried and tested by marketers and researchers since the beginning of internet. The study envisions the use of Metaverse as a platform for content marketing activities of the brands. To bring this vision into perspective, the study identifies a set of Key Performance Indicators (KPIs) that can be used to assess the performance of content marketing strategies used by marketers in the Metaverse platforms. The paper follows an analytical research approach to conceptualize the Key Performance Indicators for measuring marketing performance within the Metaverse ecosystem. By analyzing existing literature, the study extends conventional media performance metrics to develop KPIs for evaluating marketing performance inside Metaverse. The study identifies three broad KPIs namely Traffic, Engagement and Brand Awareness. In addition to this, the study also identified eight metrics specific to measuring content marketing performance inside the Metaverse. These include, Spatial Analysis, User Journey Analysis, Virtual Events Metrics, User Contribution and Creation, Time Spent, Frequency of Interaction, Community Growth and Cost Metric.

Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

Load-aware switch migration for controller load balancing in edge–cloud architectures

As the fundamental infrastructure for edge–cloud architectures, the inter-datacenter elastic optical network is used for data analysis and processing. As the demand for applications increases, the large number of service requests increases the processing overhead in the control plane, resulting in unbalanced controller loads. Existing switch migration mechanisms have been proposed for controller load balancing. Unfortunately, most of the existing mechanisms only consider the switch with the highest flow request rate as the migration object in the process of switch selection, and ignore the migration cost generated in the switch migration activity, such as the update cost of flow request message and the deployment cost of migration rule, which may increase the controller load. Additionally, most of them choose the controller with light load as the target controller to associate with the switch to be migrated, without considering whether the target controller is overloaded after the switch migration, which leads to the low load balancing performance of the controller. In view of the above problems, this paper proposes a Load-Aware Switch Migration (LASM) mechanism in edge–cloud architectures. The LASM mechanism models and analyses the cost metrics affecting switch migration and selects lower-cost switches from overloaded controller-controlled domain networks for migration activities. Besides, the LASM mechanism models switch migration based on the 0-1 knapsack problem and avoids overloading the target controllers through a greedy policy to achieving optimal migration activities. The experimental results show that the proposed LASM mechanism improves controller load balancing performance by an average of 34.3%, eliminates migration costs by 30.2%, and reduces response times by an average of 39.3%, respectively, compared to existing solutions.

(Invited) CA Hydrogen Hub and Advanced Electrolysis R&D at LBNL

Widespread use of hydrogen energy is contingent on the development of reliable and economical sources of hydrogen. This is seen in DOE’s Hydrogen Energy Earthshot goal of production of hydrogen of $1 per 1 kg in 1 decade, as well as the Regional Hydrogen Hub Program. Endemic to reaching these cost metrics using renewable, clean electricity as a feedstock is the use of electrolysis. Prime among the electrolysis technologies are those utilizing ion-conducting polymers (ionomers) including polymer-electrolyte water electrolyzer (PEWE), high-temperature solid-oxide electrolyzer (SOEC), and those using liquid alkaline systems (LAWE). However, these technologies need to exhibit increased efficiency, (dynamic) performance, and durability to reduce costs and be commercially viable. In this talk, we will overview some of these technologies through the lens of recent advances at Lawrence Berkeley National Laboratory.In addition, key to enabling a hydrogen ecosystem is the deployment of various hydrogen technologies at scale in resilient and sustainable system. Recently, the Alliance of Renewable Clean Hydrogen Energy Systems (ARCHES), a collective representing the CA region, was selected for negotiation with DOE as a hydrogen hub. In this talk, an overview of ARCHES and its approach will be given.Finally, like most electrochemical devices, PEWEs involve multiple components (e.g., catalyst, ionomer, transport layers, membrane, plates) and multiple phases, with phenomena occurring across different time and length scales. Key in PEWE technologies is the interface between the ionomer and the catalyst. In this talk, time-permitting, recent results from the Center for Ionomer-based Water Electrolysis (CIWE) will be introduced including the description of interfacial properties and double layers at this interface.

(Invited) Techno-Economics of Photoelectrochemical Water Splitting and Progress in Cost Reduction of High-Efficiency III-V Photoabsorbers

Photoelectrochemical (PEC) water splitting is a potentially promising route to direct solar-driven hydrogen production. Challenging targets for solar-to-hydrogen (STH) efficiency, durability, and semiconductor absorber costs must be realized for this approach to be economically competitive with other hydrogen production pathways. The first part of this talk will focus on the cost and performance metrics that go into techno-economic analysis and their status.Research on PEC hydrogen production at the National Renewable Energy Laboratory (NREL) has focused on III-V semiconductor absorber materials because their tunable bandgaps, high photon conversion efficiencies, and ability to make multi-junction structures that have resulted in the highest STH efficiencies yet demonstrated. However, to achieve the U.S. DOE’s hydrogen production target of $2/kg, in addition to high STH efficiency, the semiconductor absorber synthesis cost must come down 1-2 orders of magnitude from the current metal organic vapor phase epitaxial route. Hydride vapor phase epitaxy (HVPE) is a technique that has the potential to lower tandem III-V synthesis costs. The second part of this talk will describe experimental photoelectrochemical characterizations of HVPE-grown GaInP/GaAs tandem photoelectrodes with 1.85/1.38 eV bandgaps. Their incident photon-to-current efficiency, photocurrent vs. circuit bias, and unbiased chronoamperometry will be presented and discussed. Upon illumination, the HVPE-grown structures were able to drive water electrolysis spontaneously (e.g., without an external bias) at STH efficiencies up to 10%. The durability of III-V photoelectrodes remains an unresolved challenge for cost effective hydrogen production via photoelectrolysis.

Assessing the net financial benefits of employing digital endpoints in clinical trials.

In the last few decades, developers of new drugs, biologics, and devices have increasingly leveraged digital health technologies (DHTs) to assess clinical trial digital endpoints. To our knowledge, a comprehensive assessment of the financial net benefits of digital endpoints in clinical trials has not been conducted. We obtained data from the Digital Medicine Society (DiMe) Library of Digital Endpoints and the US clinical trials registry, ClinicalTrials.gov. The benefit metrics are changes in trial phase duration and enrollment associated with the use of digital endpoints. The cost metric was obtained from an industry survey of the costs of including digital endpoints in clinical trials. We developed an expected net present value (eNPV) model of the cash flows for new drug development and commercialization to assess financial value. The value measure is the increment in eNPV that occurs when digital endpoints are employed. We also calculated a return on investment (ROI) as the ratio of the estimated increment in eNPV to the mean digital endpoint implementation cost. For phase II trials, the increase in eNPV varied from $2.2 million to $3.3 million, with ROIs between 32% and 48% per indication. The net benefits were substantially higher for phase III trials, with the increase in eNPV varying from $27 million to $40 million, with ROIs that were four to six times the investment. The use of digital endpoints in clinical trials can provide substantial extra value to sponsors developing new drugs, with high ROIs.

A survey on node localization technologies in UWSNs: Potential solutions, recent advancements, and future directions

SummaryLocation‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes.

Analysis of the Ghanaian Health System and Patients Safety within the IHI Triple Aim Framework

Objective: To analyze Ghana’s healthcare system challenges through the Institute for Healthcare Improvement’s Triple Aim framework encompassing population health, patient experience and per capita cost metrics. Method: Dimensional analysis of peer-reviewed papers, government data and case studies assessed population health indicators and equity, literature on patient perspectives regarding quality and responsiveness of care along with evaluation of cost trends and deficiencies driving expenditure growth. Results: Gaps persist in universal health coverage amid rural-urban disparities in access and financial protection. Care experiences vary significantly by socioeconomic status and geography. Rising costs attributed to inefficiencies in areas like procurement and prescriber practices. Scientific Novelty: Unique application of Triple Aim framework for structured health systems analysis in a lower-middle income sub-Saharan country enabling robust diagnosis of coverage, quality, sustainability gaps. Practical Significance: Demonstrates utility of balanced Triple Aim methodology for health policymakers in low-resource settings to systematically evaluate healthcare priorities across access, outcomes, experiences and costs.

Analysis of the Ghanaian Health System and Patients Safety within the IHI Triple Aim Framework

Abstract Objective: To analyze Ghana’s healthcare system challenges through the Institute for Healthcare Improvement’s Triple Aim framework encompassing population health, patient experience and per capita cost metrics. Method: Dimensional analysis of peer-reviewed papers, government data and case studies assessed population health indicators and equity, literature on patient perspectives regarding quality and responsiveness of care along with evaluation of cost trends and deficiencies driving expenditure growth. Results: Gaps persist in universal health coverage amid rural-urban disparities in access and financial protection. Care experiences vary significantly by socioeconomic status and geography. Rising costs attributed to inefficiencies in areas like procurement and prescriber practices. Scientific Novelty: Unique application of Triple Aim framework for structured health systems analysis in a lower-middle income sub-Saharan country enabling robust diagnosis of coverage, quality, sustainability gaps. Practical Significance: Demonstrates utility of balanced Triple Aim methodology for health policymakers in low-resource settings to systematically evaluate healthcare priorities across access, outcomes, experiences and costs.

Exploring the effects of faults on the performance of a biological wastewater treatment process

ABSTRACT To prioritise which faults should be detected in a biological wastewater treatment process, and with what level of urgency, it is necessary to understand the effect that they have on the process. Using the Benchmark Simulation Model No. 1 and 2. (BSM1 and BSM2), several process and sensor faults were considered and their impacts on various cost, quality, and controller performance evaluation metrics analysed. Both the cost of treating the wastewater and the quality of the effluent were impacted in varying degrees of severity by the faults tested. The most influential faults in both models were decreases to autotrophic and heterotrophic growth rates, decreases to the heterotrophic death rate, and the inhabitation fault. It was shown that only larger fault sizes were significant, and the required speed of detection is dependent on the fault profile. Prioritising detection of the most influential faults was shown to have significant effects on monitoring requirements for fault detection and the subsequent complexity required of a fault detection system. A valuable takeaway was the similarity of results from BSM1 and BSM2; the consistency of the influential process faults suggests that systems that can be described by these models are likely affected by the same faults.

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

Research investigations in the realm of micro-robotics often center around strategies addressing the multi-robot task allocation (MRTA) problem. Our contribution delves into the collaborative dynamics of micro-robots deployed in targeted hostile environments. Employing advanced algorithms, these robots play a crucial role in enhancing and streamlining operations within sensitive areas. We adopt a tailored GREEDY approach, strategically adjusting weight parameters in a multi-objective function that serves as a cost metric. The objective function, designed for optimization purposes, aggregates the cost functions of all agents involved. Our evaluation meticulously examines the MRTA efficiency for each micro-robot, considering dependencies on factors such as radio connectivity, available energy, and the absolute and relative availability of agents. The central focus is on validating the positive trend associated with an increasing number of agents constituting the cluster. Our methodology introduces a trio of micro-robots, unveiling a flexible strategy aimed at detecting individuals at risk in demanding environments. Each micro-robot within the cluster is equipped with logic that ensures compatibility and cooperation, enabling them to effectively execute assigned missions. The implementation of MRTA-based collaboration algorithms serves as an adaptive strategy, optimizing agents' mobility based on specific criteria related to the characteristics of the target site.<p class="JAMRISAbstract"> </p>

Multi-disciplinary seismic resilience modeling for developing mitigation policies and recovery planning

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

Optimization of 3D printing supply chain in the era of live streaming e-commerce.

This study examines the effects of the rising live streaming e-commerce on the 3DP supply chain, employing system dynamics to develop separate models for pure polymer and polymer-metal mixed printing. The analysis focuses on optimizing the 3DP supply chain configuration. Results indicate that, based solely on printing time, cost, and quality metrics, Corporate-live-3DP services are optimal for live commerce scenarios. However, despite this, Private-live-3DP maintains a substantial consumer base in practice, as evidenced by literature data and case studies. Both models pose significant challenges to conventional supply chains, necessitating adaptation. For Corporate-live-3DP, optimization strategies may include technology advancements, digital transformation, agile manufacturing, global network optimization, innovative management, collaborative R&D, fine-tuned inventory control, quality system upgrades, talent development, and organizational restructuring. Conversely, Private-live-3DP can be optimized through consolidation of private 3D printing resources, demand prediction and order optimization, supply chain collaboration platforms, quality management extensions, inventory strategy adjustments, increased transparency, regulatory compliance, and risk mitigation measures.