Performance Trade-offs Research Articles

Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models

AbstractProcess-induced porosity, defects, and residual stresses lead to mechanical performance degradation in fiber-reinforced composite and other heterogeneous structures. Physical and chemical processes create complex process–material–performance relationships. Predicting porosity and residual stresses in this context requires computationally burdensome forward simulations and obtaining optimal process settings and calibrating properties of new materials requires solving inverse problems with predictions from the forward simulations. In this paper, we parameterized the process–material–performance space and created a dataset based on physics models that are valid for sintering ceramic powders. The dataset was used to train several machine learning models that captured the process–material–performance relationships. The trained ML models were applied in process optimization, calibration of properties for new material systems, and estimating performance for a given process and material. Support vector regression (SVR), convolutional neural networks (CNNs), and a Gaussian mixture model (GMM) called REPAIRS were selected, and their prediction accuracy was determined. While the SVR and CNN models require training several models, we show that the GMM model captures the process–material–performance relationships with a single machine-learned model and partial system completion methods. The paper describes root-mean-square error and mean absolute percentage errors of the inferences from the models on a validation dataset.

A new S-box pattern generation based on chaotic enhanced logistic map: case of 5-bit S-box

AbstractCryptography plays consistently an essential role in securing any sort of communications or data broadcast over the network. Since the security of any designed block cipher algorithm is related to its Substitution box (S-box), several efforts have been made by researchers to design a vigorous S-box that maintains flawlessly the cost, performance, and security trade-off. From the literature, we can find a variety of input–output sizes of S-boxes, each of which has its benefits and drawbacks. Therefore, this work introduces a new S-box pattern generation based on the chaotic enhanced logistic map where its chaotic behavior offers good randomness ability, a fact that enhances its unpredictability. In our realization, the intention was the generation of a 5-bit Sbox due to its suitability and cost-effectiveness to be integrated into lightweight cryptosystems. Moreover, the S-box security strength is proved by testing it through numerous cryptanalysis measurements. We can mention non-linearity, bijectivity, linearity, differential cryptanalysis, boomerang attacks resilience, Avalanche effect, and algebraic attacks resilience. The results show that our proposition provides good resistance to the aforementioned attacks and even shows superiority over its 5-bit competitors in terms of non-linearity, differential, Boomerang, and algebraic attack resistance.

Joint Sensing and Communications in Unmanned-Aerial-Vehicle-Assisted Systems

The application of joint sensing and communications (JSACs) technology in air–ground networks, which include unmanned aerial vehicles (UAVs), offers unique opportunities for improving both sensing and communication performances. However, this type of network is also sensitive to the peculiar characteristics of the aerial communications environment, which include shadowing and scattering caused by man-made structures. This paper investigates an aerial JSAC network and proposes a UAV-selection strategy that is shown to improve the communication performance. We first derive analytical expressions for the received signal-to-interference ratio for both communication and sensing functions. These expressions are then used to analyze the outage and coverage probability of the communication part, as well as the ergodic radar estimation information rate and the detection probability of the sensing part. Moreover, a performance trade-off is investigated under the assumption of a total bandwidth constraint. Various numerical evaluated results have been presented complemented by equivalent simulated ones. These results reveal the applicability of the proposed analysis, as well as the impact of shadowing and multipath fading severity, and interference on the system’s performance.

A simple but tough-to-beat baseline for fMRI time-series classification

Current neuroimaging studies frequently use complex machine learning models to classify human fMRI data, distinguishing healthy and disordered brains, often to validate new methods or enhance prediction accuracy. Yet, where prediction accuracy is a concern, our results suggest that precision in prediction does not always require such sophistication. When a classifier as simple as logistic regression is applied to feature-engineered fMRI data, it can match or even outperform more sophisticated recent models. Classification of the raw time series fMRI data generally benefits from complex parameter-rich models. However, this complexity often pushes them into the class of black-box models. Yet, we found that a relatively simple model can consistently outperform much more complex classifiers in both accuracy and speed. This model applies the same multi-layer perceptron repeatedly across time and averages the results. Thus, the complexity and black-box nature of the parameter rich models, often perceived as a necessary trade-off for higher performance, do not invariably yield superior results on fMRI.Given the success of straightforward approaches, we challenge the merit of research that concentrates solely on complex model development driven by classification. Instead, we advocate for increased focus on designing models that prioritize the explainability of fMRI data or pursue applicable objectives beyond mere classification accuracy, unless they significantly outperform logistic regression or our proposed model. To validate our claim, we explore possible reasons for the superior performance of our straightforward model by examining the innate characteristics of fMRI time series data. Our findings suggest that the sequential information hidden in the temporal order may be far less important for the accurate fMRI classification than the stand-alone pieces of information scattered across the frames of the time series.

Thermophilic and mesophilic anaerobic digestion of soybean molasses: A performance vs. stability trade-off

One of the factors that has a direct impact on anaerobic digestion is the applied organic loading rate (OLRA). Increasing OLRA can boost methane production but can also cause process failure. As a result, establishing the appropriate OLRA for the procedure is critical. This study evaluated the effect of increasing the OLRA using soybean molasses in a thermophilic anaerobic reactor (R-Thermo), as well as the effect of feeding strategy and co-processing with okara. Furthermore, the performance versus stability trade-off between R-Thermo and mesophilic anaerobic digestion (R-Meso) was investigated. The increase of OLRA from 10 to 15 and 20 kg-COD/m³/d led to a decrease in COD removal efficiency (90, 86, and 75%), methane yield (12.0, 11.6, and 9.9 mol-CH4/kg-COD) and an increase in total volatile acids concentration (251, 456, and 1393 mg-HAc/L, respectively). At 15 kg-COD/m³/d, R-Meso performed similarly to R-Thermo, and at 20 kg-COD/m3/d, R-Meso outperformed (81% COD removal efficiency, 9.3 mol-CH4/kg-CODrem and 154.5 mol-CH4/m3/d). Temperature greatly influenced the distribution of metabolic pathways, as shown by thermodynamic and kinetic analyses, thus impacting bacterial diversity. At 55 °C, amongst the bacterial genera, Tepidiphilus stood out (>28.2%), followed by Acetomicrobium, Coprothermobacter and Candidatus_Caldatribacterium. The OLRA clearly impacted the archaeal community; Methanothermobacter (77.4%) was favored over Methanosarcina (14.8%). Under thermophilic temperature, it seems that syntrophic acetate oxidation (SAO) bacteria might have competed for substrate with acetoclastic methanogens, while in R-Meso microorganisms responsible for the initial steps of organic matter breakdown, such as members of the Firmicutes and Proteobacteria phyla (at least 67%), were dominant. In summary, R-Meso, characterized by a more uniform distribution of metabolic pathways, as well as a diverse and well-adapted microbial consortium, have exhibited enhanced stability and outperformed R-Thermo at high-loads.

Highly efficient porous MXene desalination membranes controlled by the thickness of the transition metal carbides

MXene membrane has the vast potential to alleviate the global freshwater crisis. Herein, a new strategy for improving the desalination performance is proposed through tunable the thickness transition metal carbides of the nano-porous MXene membrane. The thickness of porous MXene is investigated by molecular dynamics (MD) simulation, including the Ti2CF2, Ti3C2F2, and Ti4C3F2. The results show that the porous MXene membrane can reach highly efficient desalination. The water density distribution reveals that more water gathers in the thicker transition metal carbides layer, contributing to the poor water flux; the average charge distribution indicates that the transition metal layer plays an essential role in effectively rejecting Na+/trapping Cl- ions. Multilayered porous membranes are explored to improve the trade-off desalination performance. The number of pores enhances the water permeability, and the multilayers improve the ion rejection. The two-layered Ti3C2F2 membrane with three nanopores achieves nearly doubled water flux and keeps 100% ion rejection. Our work reported that the nano-porous MXene membrane offers a prospective strategy to eliminate the current design defects, which provides an innovative method for the desalination semipermeable membrane industry.

Breaking mechanical performance trade-off in 3D-printed complex lattice-inspired multi-cell tubes under axial compression

It is a long-standing challenge to balance the structural load capacity and toughness in the design of lightweight multi-cell tubes. To tackle this challenge, we provide two kinds of complex lattice-inspired composite multi-cell tubes. The composite multi-cell tubes consist of inner polylactic acid (PLA) complex lattice-inspired multi-cell tubes and outside aluminum tubes. The energy absorption capacity of these multi-cell tubes was evaluated under quasi-static axial compression. The effect of cross-sectional topology and thermal exposure were considered in the experiment. The results show that the integration of PLA tubes within aluminum tubes significantly enhances their energy absorption performance, effectively addressing the limitations posed by the low fracture strain of PLA. The synergistic effect between the aluminum and PLA tubes mitigates the fracture instability and distributes the load more evenly, resulting in improved specific energy absorption (SEA) and mean crushing force (MCF) up to 103.32% and 184.38%, respectively. In these composite tubes, a global self-similar layout can markedly enhance its energy absorption. However, their mechanical properties decrease significantly at 323K compared to room temperature. Overall, this research provides a novel approach to enhancing the mechanical performance of PLA tubes, paving the way for their application in engineering fields requiring lightweight and high-strength structures.

Performance Trade-Off of Integrated Sensing and Communications for Multi-User Backscatter Systems

Performance Trade-Off of Integrated Sensing and Communications for Multi-User Backscatter Systems

Considerations for the use of laboratory-based and field-based estimates of environmental tolerance in water management decisions for an endangered salmonid

Water infrastructure development and operation can provide for human economic activity, health, and safety. However, this infrastructure can also impact native fish populations resulting in regulatory protections that can, in turn, alter operations. Conflict over water allocation for ecological function and human use has come to the forefront at Shasta Reservoir, the largest water storage facility in California, USA. Shasta Reservoir supports irrigation for a multibillion-dollar agricultural industry, provides water for urban and domestic use, provides flood protection for downstream communities, and power generation as part of the larger Central Valley Project in California. Additionally, an endangered run of Chinook Salmon relies on cold water management at the dam for successful spawning and egg incubation. Tradeoffs between these uses can be explored through application of models that assess biological outcomes associated with flow and temperature management scenarios. However, the utility of models for predicting outcomes of management decisions is contingent on the data used to construct them, and data collected to evaluate their predictions. We evaluated laboratory and field data currently available to parameterize temperature-egg survival models for winter run Chinook Salmon that are used to inform Shasta Dam operations. Models based on both laboratory and field data types had poor predictive performance which appears to limit their value for management decisions. The sources of uncertainty that led to poor performance were different for each data type (field or laboratory) but were rooted in the fact that neither data set was collected with the intention to be used in a predictive model. Our findings suggest that if a predictive model is desired to evaluate operational tradeoffs, data should be collected for the specific variables and life stages desired, over an appropriate range of values, and at sufficient frequency to achieve the needed level of precision to address the modeling objective.

Maximizing energy efficiency in HetNets through centralized and distributed sleep strategies under QoS constraint

This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

Low power RTL implementation of Tiny-YOLO-v2 DCNN hardware accelerator on Virtex-7 FPGA

Deep Convolution Neural Networks (DCNNs) are widely used in real-time applications, including image classification, speech recognition, and object detection. However, there are challenges for real-time applications on portable devices like mobile phones or embedded systems. Most object detection models are optimized for desktop configurations, requiring fast GPUs. Shallower networks with fewer computational complexities have been proposed for real-time detection, but ultimately compromise detection accuracy. Performance and complexity trade-offs are significant for computationally complex deep networks. This work aims to implement the CNN-based object detection model Tiny-Yolo-v2 on a Field Programmable Gate Array (FPGA) using Register Transfer Logic (RTL) as a native language. Hardware implementation is synthesized on The AMD Virtex 7 FPGA VC709 Connectivity Kit using VHDL code on Vivado 2020.1. This is the first, up to the authors' knowledge, RTL implementation of the Tiny-Yolo-v2 object identification algorithm on FPGA. The power consumed by the CNN layers equals 7.09W at a frequency of 100MHz.

Application of PV on Commercial Building Facades: An Investigation into the Impact of Architectural and Structural Features

The rapid global transition toward renewable energy necessitates innovative solar PV deployment strategies beyond conventional roof installations. In this context, commercial building facades represent an expansive yet underutilized resource for solar energy harvesting in urban areas. However, existing studies on commercial rooftop solar PV predominantly focus on European contexts, neglecting the unique design constraints and performance trade-offs present in regions such as the Middle East. This study addresses this gap by specifically investigating the impact of architectural and structural features on the utilizable facade area for PV deployment in commercial buildings within the hot desert climate of Saudi Arabia. Detailed case studies of twelve representative buildings are conducted, combining architectural drawing analysis, on-site measurements, and stakeholder surveys. The methodology identified sixteen parameters across three categories—facade functionality, orientation suitability, and surrounding obstructions—that impose technical and non-technical restrictions on photovoltaic integration 3D modeling, and irradiance simulations revealed that, on average, just 31% of the total vertical facade area remained suitable for PV systems after accounting for the diverse architectural and contextual limitations. The study considered 698 kWh/m2 of solar irradiance as the minimum threshold for PV integration. Shopping malls displayed the lowest utilizability, with near-zero potential, as extensive opaque construction, brand signage, and shading diminish viability. Offices exhibited the highest utilizability of 36%, owing to glazed facades and unobstructed surroundings. Hotels and hospitals presented intermediate potential. Overall, the average facade utilizability factor across buildings was a mere 16%, highlighting the significant hurdles imposed by contemporary envelope configurations. Orientation unsuitability further eliminated 12% of the initially viable area. Surrounding shading contributed an additional 0.92% loss. The results quantify the sensitivity of facades to aspects such as material choices, geometric complexity, building form, and urban context. While posing challenges, the building facade resource holds immense untapped potential for solar-based urban renewal. The study highlights the need for early architectural integration, facade-specific PV product development, and urban planning interventions to maximize the renewable energy potential of commercial facades as our cities rapidly evolve into smart solar energy landscapes.

Surface electronic fine-perturbation driven by distal atom coupling at W-Se pair sites for enhanced ammonia synthesis

Engineering single-atom electrocatalysts (SACs) of geometries and electronic structures at atomic level to overcome the critical trade-off in catalytic performance remains a significant challenge. Here, we report a novel metal–nonmetal pair sites with non-bonding interaction to achieve dual enhancements of activity and selectivity for nitrogen reduction reaction (NRR). The stable non-bonding structure of W-Se atom pairs on C3N4 support (W-SeC3N4) was engineered by photo-assisted cryogenic reduction. Distal Se-atom coupling drives a fine-tuning of surface-localized electronic state on center W-atom through genuine chemical interaction and modulates the adsorption energies of intermediates like *NNH, *NH and *NH2. W-SeC3N4 thus demonstrates a NH3 yield rate of 74.6 ug h−1 mgcat−1 and >38.2 % selectivity, surpassing all current non-precious metal SACs. Our work supplies an advanced strategy for the rational design of SACs in complex chemical reactions involving multi-step intermediates.

The Goodness of Nesting Containers in Virtual Machines for Server Consolidation

Virtualization and server consolidation are the technologies that govern today’s data centers, allowing both efficient management at the functionality level as well as at the energy and performance levels. There are two main ways to virtualize either using virtual machines or containers. Both have a series of characteristics and applications, sometimes being not compatible with each other. Not to lose the advantages of each of them, there is a trend to load data centers by nesting containers in virtual machines. Although there are good experiences at a functional level, the performance and energy consumption trade-off of these solutions is not completely clear. Therefore, it is necessary to study how this new trend affects both energy consumption and performance. In this work, we present an experimental study aimed to investigate the behavior of nesting containers in virtual machines while executing CPU-intensive workloads. Our objective is to understand what performance and energy nesting configurations are equivalent or not. In this way, administrators will be able to manage their data centers more efficiently.

Equipping computational pathology systems with artifact processing pipelines: a showcase for computation and performance trade-offs

BackgroundHistopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides, known as whole slide images (WSIs). Artifacts are diagnostically irrelevant areas and may result in wrong predictions from deep learning (DL) algorithms. Therefore, detecting and excluding artifacts in the computational pathology (CPATH) system is essential for reliable automated diagnosis.MethodsIn this paper, we propose a mixture of experts (MoE) scheme for detecting five notable artifacts, including damaged tissue, blur, folded tissue, air bubbles, and histologically irrelevant blood from WSIs. First, we train independent binary DL models as experts to capture particular artifact morphology. Then, we ensemble their predictions using a fusion mechanism. We apply probabilistic thresholding over the final probability distribution to improve the sensitivity of the MoE. We developed four DL pipelines to evaluate computational and performance trade-offs. These include two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs). These DL pipelines are quantitatively and qualitatively evaluated on external and out-of-distribution (OoD) data to assess generalizability and robustness for artifact detection application.ResultsWe extensively evaluated the proposed MoE and multiclass models. DCNNs-based MoE and ViTs-based MoE schemes outperformed simpler multiclass models and were tested on datasets from different hospitals and cancer types, where MoE using (MobileNet) DCNNs yielded the best results. The proposed MoE yields 86.15 % F1 and 97.93% sensitivity scores on unseen data, retaining less computational cost for inference than MoE using ViTs. This best performance of MoEs comes with relatively higher computational trade-offs than multiclass models. Furthermore, we apply post-processing to create an artifact segmentation mask, a potential artifact-free RoI map, a quality report, and an artifact-refined WSI for further computational analysis. During the qualitative evaluation, field experts assessed the predictive performance of MoEs over OoD WSIs. They rated artifact detection and artifact-free area preservation, where the highest agreement translated to a Cohen Kappa of 0.82, indicating substantial agreement for the overall diagnostic usability of the DCNN-based MoE scheme.ConclusionsThe proposed artifact detection pipeline will not only ensure reliable CPATH predictions but may also provide quality control. In this work, the best-performing pipeline for artifact detection is MoE with DCNNs. Our detailed experiments show that there is always a trade-off between performance and computational complexity, and no straightforward DL solution equally suits all types of data and applications. The code and HistoArtifacts dataset can be found online at Github and Zenodo, respectively.

Evolution and functional implications of stinger shape in ants.

Trait diversification is often driven by underlying performance tradeoffs in the context of different selective pressures. Evolutionary changes in task specialization may influence how species respond to tradeoffs and alter diversification. We conducted this study to investigate the functional morphology, evolutionary history, and tempo and mode of evolution of the Hymenoptera stinger using Ectatomminae ants as a model clade. We hypothesized that a performance tradeoff surface underlies the diversity of stinger morphology and that shifts between predatory and omnivorous diets mediate the diversification dynamics of the trait. Shape variation was characterized by X-ray microtomography, and the correlation between shape and average values of von Mises stress, as a measure of yield failure criteria under loading conditions typical of puncture scenarios, was determined using finite element analysis. We observed that stinger elongation underlies most of the shape variation but found no evidence of biomechanical tradeoffs in the performance characteristics measured. Additionally, omnivores have increased phenotypic shifts and accelerated evolution in performance metrics, suggesting the evolution of dietary flexibility releases selection pressure on a specific function, resulting in a greater phenotypic evolutionary rate. These results increase our understanding of the biomechanical basis of stinger shape, indicate that shape diversity is not the outcome of simple biomechanical optimization, and reveal connections between diet and trait diversification.

Leveraging HLS to Design a Versatile & High-Performance Classic McEliece Accelerator

By harnessing fundamental quantum properties, a large-scale quantum computer could undermine currently deployed public-key algorithms. The post-quantum, code-based cryptosystem Classic McEliece (CM) addresses this security concern. However, its large public key size (up to 1.3MB) poses various hardware implementation challenges. In this paper, we focus on the high memory bandwidth requirements of the CM encoding function, in the context of heterogeneous CPU-FPGA devices. More concretely, we target the acceleration of public-key loading and processing from any globally-shared or accelerator-private memory system. We present a novel and constant-time accelerator eEnc that exploits the elevated parallelization potential of FPGA devices to yield high-performance results. Our accelerator implements the encoding and the random error vector generation functions, which comprise the main computational load of Encapsulation. Two accelerator design variants are introduced, providing different hardware tradeoffs. Regarding intra-accelerator data communication, and unlike other state-of-the-art (SOTA) works, we combine a streaming protocol with task-level parallelization to remove the need to store the public key in accelerator-private memories. Our proposed design shows new record execution times over its SOTA counterparts, ranging on average from 3.5 × up to 7.7 × across the five security level parameter sets. Our end-to-end implementation in a Zynq SoC shows an average speedup of 2.2 × compared to a 64-bit vectorized CM software-baseline. The elevated logic resource consumption, characteristic of HLS designs, can be readily adjusted with a performance tradeoff.

Multiloop Control of Floating Wind Turbines: Tradeoffs in performance and stability

Multiloop Control of Floating Wind Turbines: Tradeoffs in performance and stability

Implementation of Skogestad’s Internal Model Control Rule in Tuning Proportional-Integral Controllers for Two-Input and Two-Output Systems Considering Individual Controller Performance Trade-off

Abstract In industrial plants, tuning of decentralized Proportional-Integral-Derivative feedback (PID) controllers in Multiple-Input and Multiple-Output (MIMO) systems is a complex problem, due to input and output interactions, that must be done to ensure individual control objectives are met, while system being sufficiently robust. In this study, a two-input and two-output (TITO) PI controller tuning algorithm was developed considering controller performance prioritization given performance trade-offs, expressed as the novel Individual Controller Performance Trade-off Coefficient (ICPTC), and system robustness, expressed as sensitivity peaks from the generalized Nyquist stability plot. Simplified effective open-loop transfer functions (EOTFs) of the two input-output channels were derived and used along with Skogestad’s Internal Model Control (SIMC) to calculate PI controller gains. Extensive simulations using Wood-Berry (WB) and Vinante-Luyben (VL) distillation column models were made to observe how the sensitivity peaks behave across the simulations, which was then used as the basis of the developed tuning algorithm. A simplified tuning algorithm, which requires less information compared to the main algorithm, was also derived. A mapping algorithm was also created, allowing specific controller gains from other studies to be mapped to an equivalent set of controller gains in the algorithm’s search field. The effectiveness of the three algorithms were tested using Wardle-Wood distillation column (WW) and Xiong and Cai (XC) process models. It was demonstrated using the main algorithm that there is a trade-off between controller performances when ICPTC is varied, at different values of design sensitivity peaks between 1.96 to 4. The usefulness of the simplified tuning and mapping algorithms were also illustrated in the study. This study is potentially useful in tuning TITO PI controllers in industrial plants, where meeting individual control objectives while maintaining robustness is more important than optimizing overall control performance.

Characterizing Performance Tradeoffs from Air Traffic Management Services for Advanced Air Mobility

The emergence of Advanced Air Mobility (AAM) has called for novel Air Traffic Management (ATM) solutions to enable the projected high number of operations, particularly in already dense and complex metroplex airspace. Understanding the multidimensional performance impacts of developing air traffic rules, procedures, and services is key to guiding this new traffic management system design. This paper presents a simulation framework to provide a quantitative assessment of the operational performance of ATM services in support of AAM. The framework embeds several modules for high-fidelity modeling of urban air traffic flows based on actual data, ATM service provision, stochastic simulation, and performance quantification. Two key services are analyzed: airspace geofencing and strategic deconfliction. We evaluate the use of these services in a scenario of air mobility operations associated with daily urban commuting in the Sao Paulo metropolitan region. Monte Carlo experiments are performed to simulate the execution of aircraft trajectories managed under different ATM service configurations, allowing for the characterization of operational performance tradeoffs, particularly in terms of safety and efficiency. The work contributes to quantifying the value of novel ATM services for integrating AAM operations in dense metroplex airspace, supporting their design and implementation.