Performance Trade-offs Research Articles

Towards silent and efficient flight by combining bioinspired owl feather serrations with cicada wing geometry

As natural predators, owls fly with astonishing stealth due to the serrated feather morphology that produces advantageous flow characteristics. Traditionally, these serrations are tailored for airfoil edges with simple two-dimensional patterns, limiting their effect on noise reduction while negotiating tradeoffs in aerodynamic performance. Conversely, the intricately structured wings of cicadas have evolved for effective flapping, presenting a potential blueprint for alleviating these aerodynamic limitations. In this study, we formulate a synergistic design strategy that harmonizes noise suppression with aerodynamic efficiency by integrating the geometrical attributes of owl feathers and cicada forewings, culminating in a three-dimensional sinusoidal serration propeller topology that facilitates both silent and efficient flight. Experimental results show that our design yields a reduction in overall sound pressure levels by up to 5.5 dB and an increase in propulsive efficiency by over 20% compared to the current industry benchmark. Computational fluid dynamics simulations validate the efficacy of the bioinspired design in augmenting surface vorticity and suppressing noise generation across various flow regimes. This topology can advance the multifunctionality of aerodynamic surfaces for the development of quieter and more energy-saving aerial vehicles.

Extreme elevational migration spurred cryptic speciation in giant hummingbirds

The ecoevolutionary drivers of species niche expansion or contraction are critical for biodiversity but challenging to infer. Niche expansion may be promoted by local adaptation or constrained by physiological performance trade-offs. For birds, evolutionary shifts in migratory behavior permit the broadening of the climatic niche by expansion into varied, seasonal environments. Broader niches can be short-lived if diversifying selection and geography promote speciation and niche subdivision across climatic gradients. To illuminate niche breadth dynamics, we can ask how "outlier" species defy constraints. Of the 363 hummingbird species, the giant hummingbird (Patagona gigas) has the broadest climatic niche by a large margin. To test the roles of migratory behavior, performance trade-offs, and genetic structure in maintaining its exceptional niche breadth, we studied its movements, respiratory traits, and population genomics. Satellite and light-level geolocator tracks revealed an >8,300-km loop migration over the Central Andean Plateau. This migration included a 3-wk, ~4,100-m ascent punctuated by upward bursts and pauses, resembling the acclimatization routines of human mountain climbers, and accompanied by surging blood-hemoglobin concentrations. Extreme migration was accompanied by deep genomic divergence from high-elevation resident populations, with decisive postzygotic barriers to gene flow. The two forms occur side-by-side but differ almost imperceptibly in size, plumage, and respiratory traits. The high-elevation resident taxon is the world's largest hummingbird, a previously undiscovered species that we describe and name here. The giant hummingbirds demonstrate evolutionary limits on niche breadth: when the ancestral niche expanded due to evolution (or loss) of an extreme migratory behavior, speciation followed.

Balcony design to improve natural ventilation and energy performance in high-rise mixed-mode office buildings

Balconies have the potential to promote airflow and regulate solar heat gain, which can reduce energy usage for cooling in buildings. However, designing these elements can be challenging due to complex trade-offs for natural ventilation and energy performance, highlighting the need for advanced simulation tools to guide balcony design decisions. This study aims to evaluate the impacts of balcony design on natural ventilation and thermal and energy performance in high-rise mixed-mode office buildings in São Paulo, Brazil. To this end, an integrated method that involves computational fluid dynamics (CFD) and building performance simulations (BPS) is developed to identify optimal solutions. Through this method, the performance of 40 balcony designs is studied, comprising diverse alternatives for three balcony design parameters (width, depth, and location on the façade). Furthermore, the research is performed considering the balcony effects on two façade orientations (north and south), two wind incidences (windward and leeward), and three floor levels (upper, medium, and lower floors). Results are analysed through a sensitivity analysis, percentage of change and cross-analysis. The findings indicate that narrow and shallow balconies (2 m × 0.5 m) are the best design option for the south façade; they enhance natural ventilation and reduce energy consumption for cooling across all floor levels and balcony locations. Conversely, for the north, sun-facing façade, it is recommended to use wide and deep balconies (5 m × 2 m) to reduce solar gains and associated energy consumption for cooling, despite potential limitations in natural ventilation.

Novel adjustable backflow structure in electric coolant pump for balancing hydraulic performance and reducing temperature rise

An electric coolant pump serves as the source for coolant circulation in the thermal management of electric vehicles. To address the thermal issues for the printed circuit board integrated within the electric coolant pump, a novel backflow structure with an adjustable orifice diameter is designed from the impeller outlet to the printed circuit board to promote convective heat dissipation. Eventually, this flow returns to the impeller inlet, thereby altering the hydraulic performance of pump. The balance between hydraulic performance and temperature rise is achieved by adjusting the orifice diameter. Considering the variations in coolant properties with respect to temperature, numerical simulations of the electric coolant pump are performed using the unsteady compressible Reynolds-Averaged Navier-Stokes Equations. There is good agreement between experimental and numerical simulations results in both cases with and without an orifice. On an average, by increasing the orifice diameter by 1.5 mm which results in a 1.11 % decrease in efficiency, a 0.008 decrease in pressure coefficient, and a reduction of temperature by 2.98 K on printed circuit board. As the orifice diameter increases, leakage losses in the rear side chamber increases with the improvement of heat dissipation. The clash between the backflow jet and the inflow from the suction pipe results in significant entropy generation, forming jet vortices and increasing in pressure fluctuation. However, when the orifice diameter exceeds 3 mm, the cooling advantages no longer justify the trade-off in hydraulic performance. Thus, 3 mm orifice diameter is an optimal compromise between hydraulic performance and temperature control.

Assessing the Interpretability–Performance Trade-Off of Artificial Neural Networks Using Sentinel Fish Health Data

A number of sentinel species are regularly sampled from the environment near the Oil Sands Region (OSR) in Alberta, Canada. In particular, trout-perch are sampled as a proxy for the health of the aquatic ecosystem. As the development of the OSR began before the environmental monitoring program was in place, there is currently no established measure for the baseline health of the local ecosystem. A common solution is to calculate normal ranges for fish endpoints. Observations found to be outside the normal range are then flagged, alerting researchers to the potential presence of stressors in the local environment. The quality of the normal ranges is dependent on the accuracy of the estimates used to calculate them. This paper explores the use of neural networks and regularized regression for improving the prediction accuracy of fish endpoints. We also consider the trade-off between the prediction accuracy and interpretability of each model. We find that neural networks can provide increased prediction accuracy, but this improvement in accuracy may not be worth the loss in interpretability in some ecological studies. The elastic net offers both good prediction accuracy and interpretability, making it a safe choice for many ecological applications. A hybridized method combining both the neural network and elastic net offers high prediction accuracy as well as some interpretability, and therefore it is the recommended method for this application.

Modeling the performance of early fault-tolerant quantum algorithms

Progress in fault-tolerant quantum computation (FTQC) has driven the pursuit of practical applications with (EFTQC). These devices, limited in their qubit counts and fault-tolerance capabilities, require algorithms that can accommodate some degrees of error, which are known as EFTQC algorithms. To predict the onset of early quantum advantage, a comprehensive methodology is needed to develop and analyze EFTQC algorithms, drawing insights from both the methodologies of noisy intermediate-scale quantum and traditional FTQC. To address this need, we propose such a methodology for modeling algorithm performance on EFTQC devices under varying degrees of error. As a case study, we apply our methodology to analyze the performance of randomized Fourier estimation (RFE) [Kshirsagar, Katabarwa, and Johnson, ], an EFTQC algorithm for phase estimation. We investigate the runtime performance and the fault-tolerant overhead of RFE in comparison to the traditional quantum phase estimation algorithm. Our analysis reveals that RFE achieves significant savings in physical qubit counts while having a much higher runtime upper bound. We anticipate even greater physical qubit savings when considering more realistic assumptions about the performance of EFTQC devices. By providing insights into the performance trade-offs and resource requirements of EFTQC algorithms, our work contributes to the development of practical and efficient quantum computing solutions on the path to quantum advantage. Published by the American Physical Society 2024

Trade-Off in Energy Efficiency and Response Performance in Green CRNs: Perspective of Two Sleep Timers and Bursty Traffic

Trade-Off in Energy Efficiency and Response Performance in Green CRNs: Perspective of Two Sleep Timers and Bursty Traffic

Closing the Loop: Unexamined Performance Trade-Offs of Integrating Direct Air Capture with (Bi)carbonate Electrolysis.

CO2 from carbonate-based capture solutions requires a substantial energy input. Replacing this step with (bi)carbonate electrolysis has been commonly proposed as an efficient alternative that coproduces CO/syngas. Here, we assess the feasibility of directly integrating air contactors with (bi)carbonate electrolyzers by leveraging process, multiphysics, microkinetic, and technoeconomic models. We show that the copresence of CO32- with HCO3- in the contactor effluent greatly diminishes the electrolyzer performance and eventually results in a reduced CO2 capture fraction to ≤1%. Additionally, we estimate suitable effluents for (bi)carbonate electrolysis to require 5-14 times larger contactors than conventionally needed contactors, leading to unfavorable process economics. Notably, we show that the regeneration of the capture solvent inside (bi)carbonate electrolyzers is insufficient for CO2 recapture. Thus, we suggest process modifications that would allow this route to be operationally feasible. Overall, this work sheds light on the practical operation of integrated direct air capture with (bi)carbonate electrolysis.

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performance trade-offs and vulnerability to cyber-attacks. One approach to address these concerns is minimising computational overhead and adopting lightweight intrusion detection techniques. In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in IoT environments. The proposed OCFSDA model incorporates feature selection, data compression, pruning, and deparameterization. We deployed the model on a Raspberry Pi4 using the TFLite interpreter by leveraging optimisation and inferencing with semi-supervised learning. Using the MQTT-IoT-IDS2020 and CIC-IDS2017 datasets, our experimental results demonstrate a remarkable reduction in the computation cost in terms of time and memory use. Notably, the model achieved an overall average accuracies of 99% and 97%, along with comparable performance on other important metrics such as precision, recall, and F1-score. Moreover, the model accomplished the classification tasks within 0.30 and 0.12 s using only 2KB of memory.

Impact of channel thickness on device scaling in vertical InGaZnO channel charge-trap memory transistors with ALD Al2O3 tunneling layer

This study investigates the impact of channel thickness (TCH) variation on memory performance and its physical origins in vertical channel charge trap memory (V-CTM) using InGaZnO (IGZO) channels for X–Y scaling. When the TCH decreased, the subthreshold slope (SS) increased from 0.21 to 0.30 V/dec, while the minimum program voltage decreased from 12 to 8 V, indicating a performance trade-off. The analysis of bulk trap density extracted by SS, density of states distribution, and x-ray photoelectron spectroscopy confirmed an increase in the number of trap states and hydrogen-related defects in the IGZO channel when a thin TCH is employed. As a result, it was expected that the barrier height at the active layer/TL would be lowered during program operations at relatively thin channel. However, the SS would also be degraded due to an increase in sub-gap states, such as OH- and VOH, induced by hydrogen incorporation. The V-CTM with an optimal TCH of 7 nm had a maximum memory window of 5.7 V. The program/erase states were maintained for 106 s before experiencing a 50 % charge loss, and 5-level memory states were verified to be available within a memory window of 3.2 V for 108 s with little degradation. It was noteworthy that the long-term reliability and multilevel implementation could be secured for the V-CTM with a TCH as thin as 7 nm, demonstrating the sound feasibility of its X–Y scaling. This work presents new key parameters and understanding of device integration issues for future 3D structured memory transistors, and aims to contribute to the advancement of memory technologies using oxide semiconductor channels.

Generalized modulation for distance-aware optical camera communication beyond oversampled and undersampled schemes

Oversampled modulation (OM) and undersampled modulation (UM) are two commonly used optical camera communication (OCC) modulation schemes for high-speed communication in short-range and reliable communication at long distances, respectively. However, the relationship between these two schemes and the tradeoff in performance for arbitrary communication distances have not been thoroughly investigated. In this study, we analyze the impact of distance and modulation parameters on pixel efficiency and packet delivery rate performance, demonstrating the underlying unity of traditional OM and UM schemes. Furthermore, we propose a generalized modulation scheme that allows for achieving predefined link performance at a given distance by adjusting the modulation parameters, such as packet length and repetition counts. Simulation and experimental results show that the proposed generalized modulation scheme provides OCC with a unique distance-aware capability other than the traditional OM and UM schemes, which are two special cases focusing on effectiveness and reliability, respectively. This research enhances our understanding of OCC data modulation and establishes a theoretical foundation for achieving efficient and reliable OCC transmission in complex environments.

Simulation and analysis of 5G waveforms to reduce BER for vehicular communications

In today's rapidly evolving world, wireless communication has become a pervasive force, profoundly impacting various facets of our daily lives. Wireless Vehicular Networks stand out as a captivating realm of research, with a key focus on fostering information exchange among autonomous vehicles. As researchers witness surging demand in this domain, there is a growing emphasis on devising advanced techniques to augment network performance, particularly within the context of Fifth-generation (5G) applications, such as vehicular communication. The concept of Vehicle-to-vehicle (V2V) communications is poised to play a pivotal role in the future, presenting formidable challenges for the air interface by accommodating asynchronous multiple access and high mobility. Within this dynamic landscape, security and privacy issues loom large for 5G-enabled vehicle networks, many of which remain largely unexplored. The conventional waveforms, including Orthogonal Frequency Division Multiplexing (OFDM), may fall short of meeting these evolving standards. In this paper, authors delve into a comparative exploration of two waveform families, namely Filter Bank Multicarrier (FBMC) and Universal Filtered Multi-Carrier (UFMC), concerning their design and performance trade-offs. authors also examine their compatibility with various digital modulation schemes like 4-Quadrature Amplitude Modulation (QAM), 16-QAM, Offset Quadrature Phase Shift Keying (OQPSK), and Shaped offset OQPSK (SOQPSK). Through MATLAB simulations, our research vividly demonstrates the superior performance of UFMC when juxtaposed with OFDM and FBMC, especially concerning Bit Error Rate (BER) in both Rayleigh and Nakagami fading channels. In particular, authors consider a Nakagami shape parameter of 10, which yields a remarkable minimum BER for UFMC.

Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations

Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200 000 samples that yields < 6% mean average percent error for the predictions of all relevant beam output parameters from defining RFQ design parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables—common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.

Multiphase flow dynamics in metal foam proton exchange membrane fuel cell

Porous metal foam holds substantial promise as a material for bipolar plates in high-performance proton exchange membrane fuel cells (PEMFC) due to its exceptional properties in reactant redistribution. However, the intricate structure of metal foam flow field (MFF) presents challenges for mass transfer and water management. In this study, a three-dimensional two-phase flow model for metal foam is developed to analyze and capture flow patterns. MFFs are reconstructed based on detailed structural analysis and various pore sizes are discussed. Employing the Volume of Fluid (VOF) method, we delve into multiphase flow dynamics, specifically focusing on the removal of liquid droplets within the MFF. It reveals the MFF with moderate pore sizes exhibit a trade-off performance, striking a balance between optimal mass transfer and minimal parasitic loss. The porous structure's pivotal role is highlighted, contributing significantly to both through-plane and in-plane mass transfer and convection. Furthermore, we classify three flow patterns of liquid droplet, with the “split-up” pattern emerging as the most effective for water management and mass transfer. This study illuminates water behavior in porous metal foam bipolar plates, introduces a systematic methodology for assessing mass transfer and water management capabilities in MFFs for PEMFC.

Bridged Bicyclic Lactam Enables Chemically Recyclable Nylon.

Nylon, a widely-used high-performance thermoplastic, boasts exceptional durability and resistance to various solvents and weak acids, making it indispensable across diverse applications. However, its nonbiodegradable nature has led to alarming environmental pollution in land and oceans. Chemical recycling to monomers (CRM) stands as a crucial strategy for establishing a circular plastic economy, but the CRM of nylon remains largely unexplored. Herein, we introduce the bridged bicyclic lactam 5-azabicyclo[2.2.1]octan-6-one (5/6-LM), evolved from δ-valerolactam and pyrrolidone, to solve the trade-off in depolymerizability and performance. Notably, 5/6-LM exhibits nearly 95 % conversion in mild polymerization conditions and efficient depolymerization catalyzed by lewis acids. This compound is synthetically accessible from commercially available chemicals in a single step at room temperature, demonstrating high efficiency and scalability up to 50 g in laboratory. Furthermore, the resulting polyamide displays remarkable attributes including high crystallinity and thermostability up to 283 °C, significantly broadening the scope of chemically recyclable nylons.

Methodology for Assessing Retrofitted Hydrogen Combustion and Fuel Cell Aircraft Environmental Impacts

Hydrogen (H2) combustion and solid oxide fuel cells (SOFCs) can potentially reduce aviation-produced greenhouse gas emissions compared to kerosene propulsion. This paper outlines a methodology for evaluating performance and emission tradeoffs when retrofitting conventional kerosene-powered aircraft with lower-emission H2 combustion and SOFC hybrid alternatives. The proposed framework presents a constant-range approach for designing liquid hydrogen fuel tanks, considering insulation, sizing, center of gravity, and power constraints. A lifecycle assessment evaluates greenhouse gas emissions and contrail formation effects for carbon footprint mitigation, while a cost analysis examines retrofit implementation consequences. A Cessna Citation 560XLS+ case study shows a 5% mass decrease for H2 combustion and a 0.4% mass decrease for the SOFC hybrid, at the tradeoff of removing three passengers. The lifecycle analysis of green hydrogen in aviation reveals a significant reduction in CO2 emissions for H2 combustion and SOFC systems, except for natural-gas-produced H2 combustion, when compared to Jet-A fuel. However, this environmental benefit is contrasted by an increase in fuel cost per passenger-km for green H2 combustion and a rise for natural-gas-produced H2 SOFC compared to kerosene. The results suggest that retrofitting aircraft with alternative fuels could lower carbon emissions, noting the economic and passenger capacity tradeoffs.

Investigating the impact of smart manufacturing on firms' operational and financial performance

PurposeSmart manufacturing (SM) lies at the core of Industry 4.0. Uniform adoption of SM across business partners is crucial to exploit its value creation potential. However, firms' willingness to invest in SM is limited by insufficient or inconclusive evidence on its performance-related benefits. To close this gap, this paper develops and tests a model linking SM adoption to firms' financial performance. Improvements along the four dimensions of operational performance (i.e. cost quality, delivery and flexibility) mediate this relation.Design/methodology/approachThis study follows an empirical research approach. In particular, survey data from 234 automotive component suppliers are analyzed via covariance-based structural equation modeling to explore the link between SM adoption and operational performance. Survey data are then matched with secondary data from balance sheets of 81 firms to investigate the impact of SM on financial performance via partial least square structural equation modeling.FindingsFindings highlight that adoption of SM results in improvements in cost, quality, delivery performance, thus suggesting that SM is a mean to overcome performance trade-offs. Improvements in operational performance enabled by SM do not give rise to superior financial performance, thus implying that SM might support firms in maintaining the competitive position in the market, but could be insufficient to generate higher margin.Originality/valueResults have implications for SM research and for manufacturing executives engaged in the adoption of SM, as they provide a detailed analysis of the impact of SM on operational performance and clarify the effect that SM adoption has on financial performance.

Projected seasonal flooding in Canada under climate change with statistical and machine learning

Study regionCanada Study focusFloods are among the costliest and deadliest natural hazards in the world. To date, little is known about future seasonal flooding across all Canada. In this paper, data-driven models for flood occurrence (i.e., happening of a flood) and impact (i.e., displaced population) were calibrated for spring and summer seasons in 14,000 watersheds across Canada. Generalized Additive Models (GAM), Random Forests (RF) and Gradient Boosting Machines (GBM) were considered to model seasonal floods. The best-performing flood models were then used with regional climate models to assess the effect of climate change on flooding for three time horizons: historical, medium-term (∼2050) and long-term (∼2080). New hydrological insights for the regionGAM offered the best out-of-sample performance trade-off in both seasons for predicting flooding in Canada. Projections with GAM showed a general increase in summer flooding occurrence and impact in 2050 and 2080, mainly in the Yukon, western British Columbia, southern Prairies, Ontario and Quebec and some Atlantic provinces. Results for spring flooding were more mixed, but there seemed to be a slight decrease in the impact of spring flooding in the southern Prairies, particularly in 2080. The combination of statistical/machine learning and climate models have provided a more detailed and contrasted picture of the projected seasonal flooding situation over Canada that will help authorities better mitigate future flood risks.

Carbon nanofiber electrodes without commercial polymer precursors deliver superior electrochemical performance

Carbon nanofiber (CNF) electrodes are conductive and electrochemically stable, making excellent supercapacitor components. However, such fibers are commonly made from commercial polymer precursors, such as polyacrylonitrile (PAN), which are expensive to produce. Further, PAN carbon fibers are inert and lack porous surfaces, limiting their use as supercapacitor electrodes without further modification. We demonstrate the use of coal-derived polyurethane (CPU) in lieu of PAN, producing a cheap alternative precursor. Electrospinning and carbonization of this precursor produces porous fibers which are rich in nitrogen and oxygen species, creating an active surface facilitating fast faradaic reactions (FFRs). These electrodes offer superior electrochemical performance, with 604 F g−1 at 1 A g−1. After an initial activation period, this capacitance is stable over 10,000 cycles. In a symmetrical two electrode cell, this activated carbon offers superior rate performance and power-energy density trade-off compared to commercial and literature activated carbon electrodes with 20.9 Wh kg−1 energy density at a power density of 0.5 kW kg−1.

Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior

Recent vision-language pre-trained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a method to improve the pre-trained VLP model by adding a learnable context vector to the inputs of the text encoder. In a few-shot learning scenario of the downstream task, MLE training can lead the context vector to over-fit dominant image features in the training data. This overfitting can potentially harm the generalization ability, especially in the presence of a distribution shift between the training and test dataset. This paper presents a Bayesian-based framework of prompt tuning, which could alleviate the over-fitting issues on few-shot learning application and increase the adaptability of prompts on unobserved instances. Specifically, modeling data-dependent prior enhances the adaptability of text features for both seen and unseen image features without the trade-off of performance between them. Based on the Bayesian framework, we utilize the Wasserstein gradient flow in the estimation of our target posterior distribution, which enables our prompt to be flexible in capturing the complex modes of image features. We demonstrate the effectiveness of our method on benchmark datasets for several experiments by showing statistically significant improvements on performance compared to existing methods.