Efficiency Tradeoff Research Articles

Variations and Coordination of Leaflet and Petiole Functional Traits Within Compound Leaves in Three Hardwood Species

Leaf morphology and anatomy traits are key determinants for plant performance; however, their roles within compound leaves—comprising both leaflets and petioles—remain insufficiently studied. This study examined the anatomy, morphology, and biomass allocation of leaflets and petioles in three temperate species (Fraxinus mandshurica Rupr., Juglans mandshurica Maxim., and Phellodendron amurense Rupr.). The results showed pronounced anatomical variations within the whole leaf. Specifically, as phyllotaxy increased, the number of conduits significantly increased in petioles but showed less variation. Within the same growth position, the number of conduits was highest in the petiole, followed by the petiolule, main vein, and minor veins. In the terminal leaf vascular network, thinner conduits of minor veins may result in a lower hydraulic efficiency but a higher resistance to embolism. Biomass allocation favored leaflets over petioles in all three examined species. Additionally, the specific leaf area slightly increased with an increase in the degree of phyllotaxy. These findings underscore the trade-offs of efficiency and safety in vascular tissues, as well as the expanding leaf and investment between the leaflet and petiole.

Multi-stage algorithm for energy efficiency and data rate trade-off in imperfect CSI downlink NOMA systems

Multi-stage algorithm for energy efficiency and data rate trade-off in imperfect CSI downlink NOMA systems

Aerodynamic Challenges and Innovations in Wing Designs for Supersonic Flight: A Comprehensive Review

Supersonic flight has been of interest for decades due to the advantages it offers in speed and efficiency of transport, which are advantageous to both commercial and military aircraft. This paper covers a comprehensive review of various wing configurations for supersonic flight. We explore the aerodynamic and structural challenges associated with the various wing types. Supersonic flight offers significant benefits, but also poses complex engineering challenges including concerns with drag, stability, and sonic booms. This review examines the performance of straight, low-sweep, swept-back, delta, forward-swept, and oblique wings and highlights their respective advantages and limitations across different regimes. Straight and low-sweep wings excel at subsonic speeds, but struggle with high drag at supersonic velocities, while swept-back wings and delta wings offer superior performance at high speeds but come with trade-offs in lift and low-speed efficiency. Forward-swept wings provide potential for noise reduction and overall favorable supersonic flight characteristics, but face challenges like aeroelastic instability. Oblique wings, though promising in reducing drag and sonic booms, have significant structural challenges at high Mach numbers. Ongoing research in materials science and aerodynamic modeling continues to advance supersonic aircraft design. These studies and development efforts are paving the way for next-generation supersonic aircraft. Keywords: Aerospace and Aeronautical Engineering; Computation and Theory; Supersonic Flight; Wing Design; Aerodynamics and Sonic Booms

PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT

This paper explores the intersection of machine learning and personal data privacy, examining the challenges and solutions for preserving privacy in data-driven systems. As machine learning algorithms increasingly rely on large datasets, concerns about data leakage and breaches have intensified. To address these issues, we investigate various privacy-preserving techniques, including differential privacy, federated learning, adversarial training, and data anonymization. The findings highlight the effectiveness of these methods in protecting sensitive information while maintaining model performance. However, trade-offs in accuracy, computational efficiency, and model interpretability remain significant challenges. The paper also emphasizes the need for transparent and explainable models to ensure ethical data use and foster trust in AI systems. Ultimately, the study concludes that while privacy-preserving machine learning methods show great promise, ongoing research is essential to balance privacy and performance in future applications.

Semi-Implicit Numerical Integration of Boundary Value Problems

The numerical solution to boundary differential problems is a crucial task in modern applied mathematics. Usually, implicit integration methods are applied to solve this class of problems due to their high numerical stability and convergence. The known shortcoming of implicit algorithms is high computational costs, which can become unacceptable in the case of numerous right-hand side function calls, which are typical when solving boundary problems via the shooting method. Meanwhile, recently semi-implicit numerical integrators have gained major interest from scholars, providing an efficient trade-off between computational costs, stability, and precision. However, the application of semi-implicit methods to solving boundary problems has not been investigated in detail. In this paper, we aim to fill this gap by constructing a semi-implicit boundary problem solver and comparing the performance of explicit, semi-implicit, semi-explicit, and implicit methods using a set of linear and nonlinear test boundary problems. The novel blinking solver concept is introduced to overcome the main shortcoming of the semi-implicit schemes, namely, the low convergence on exponential solutions. The numerical stability of the blinking semi-implicit solver is investigated and compared with existing methods by plotting the stability regions. The performance plots for investigated methods are obtained as a dependence between global truncation error and estimated computation time. The experimental results confirm the assumption that semi-implicit numerical methods can significantly outperform both explicit and implicit solvers while solving boundary problems, especially in the proposed blinking modification. The results of this study can be efficiently used to create software for solving boundary problems, including partial derivative equations. Constructing semi-implicit numerical methods of higher-accuracy orders is also of interest for further research.

Staleness-Controlled Asynchronous Federated Learning: Accuracy and Efficiency Tradeoff

Staleness-Controlled Asynchronous Federated Learning: Accuracy and Efficiency Tradeoff

Inter-call intervals, but not call durations, adhere to Menzerath's Law in the submissive vocal bouts of meerkats.

Diverse information encoding systems, including human language, the vocal and gestural systems of non-human animals and the structure of DNA and proteins, have been found to conform to 'Menzerath's Law'-a negative relationship between the number of units composing a sequence, and the size of those units. Here, we test for the presence of Menzerath's Law in the vocal bouts produced in a submissive context by meerkats (Suricata suricatta). Using a suite of Bayesian mixed effects models, we examined 1676 vocal bouts produced by 89 wild meerkats, ranging from 1 to 590 calls in length, to determine whether the number of calls composing each bout had a negative relationship with the duration of those calls or their inter-call intervals. In contradiction to Menzerath's Law, we found that the duration of vocalizations had a positive relationship with the number of calls in a bout. However, the duration of intervals between calls did have a negative relationship with bout size. Moreover, both calls and intervals had longer durations the closer they were positioned to the end of the bout. These findings highlight the multi-faceted ways in which efficiency trade-offs can occur in the vocal repertoires of non-human animals, shaping variability in the production of signal forms.

Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer

To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE tradeoff is originally constructed as a constrained large-scale multi-objective problem (CLSMOP) for the power allocation of users. To solve this CLSMOP, a constrained large-scale multi-objective evolutionary algorithm (CLSMOEA), considering the dimensionality reduction as well as the balance of objectives and constraints, is explored. The Lagrange multiplier is first used to construct a two-scale optimization model, bridging original large-scale decision space of variables and small-scale decision space of coefficients of Lagrange multiplier. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces emphasizing objectives and constraints, respectively. In summary, the optimization in large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in small-scale space helps the population cross the infeasible areas to approach constrained PF, and the GD-based reproduction of offspring further guarantees the solution convergence. Ten representative and state-of-the-art constrained multi-objective evolutionary algorithms (MOEAs) and unconstrained MOEA have been compared to the proposed CLSMOEA to demonstrate its effectiveness through comparative experiments on some well-known benchmark problems (with 1000 variables), and MaMIMO-LU (with 1024 antennas and 256, 512, and 1024 users). Experimental results show that the proposed CLSMOEA can obtain the best SE-EE tradeoff.

Thermal and Power Management Challenges in High-Performance Mobile Processors

A significant computational capability to just a few cubic centimeters has been enabled by highperformance mobile processors that are rapidly evolving. Unfortunately, the increase in transistor density and clock speed, along with the integration of additional features, create prohibitive challenges with thermal output and power consumption. In this paper, we critically discuss key thermal and power management issues in modern mobile processors: thermal design power (TDP), energy efficiency tradeoffs, and the implications of thermal throttling on performance. Dynamic voltage and frequency scaling (DVFS), thermal aware task scheduling and sophisticated cooling mechanisms are reviewed. Emerging challenges posed by the integration of AI accelerators and 5G connectivity are also presented. Insights for future directions in sustainable processor design conclude the study

On the Numerical Investigation of Natural-Convection Heat Sinks Across a Wide Range of Flow and Operating Conditions

Many designs for natural-convection heat sinks and semi-empirical correlations have been proposed in the recent years, but they are only valid in a limited range of Elenbaas numbers El and were mostly tested for laminar flows. To alleviate those limits, parametric studies with 2D and quasi-3D models were carried out, in ranges of Grashof numbers up to 1.55×1011 and Elenbaas numbers up to 3.42×107. Ansys Fluent’s laminar, transition-SST, SST k-ω and k-ϵ models were applied. In addition, when used in this valid range, i.e., mean Elenbaas numbers, with the simplified quasi-3D model, the transition-SST model could predict better results, overestimating the heat flux by 10 to 15% compared to semi-empirical correlations. The 2D model was not deemed satisfying, regarding turbulence models. Consequently, a quasi-3D model was developed: it appeared to be an efficient trade-off between computational time and prediction accuracy, in particular for turbulence models. New grouping factors were also found, to ensure proper dimensioning of natural-convection heat sinks. They corresponded to non-dimensional parameters that dictated the physical behaviour of the heat sink with respect to the semi-empirical correlations. Typically, the ratio of the spacing to the optimal spacing predicted by Bar-Cohen’s correlation turned out to be an appropriate grouping factor with a threshold of 1, above which the fins could safely be considered as isolated, thus greatly simplifying all further calculations.

Impact of freeze drying on the properties of palmitic acid extracted from Plantain stalk waste

The challenge of effectively preserving and concentrating palmitic acid from plantain stalk waste is compounded by the loss and degradation of the compound during traditional drying and extraction methods, which may lead to reduced yield and altered properties of the extracted palmitic acid. This study was aimed at evaluating the impact of the freeze-drying process on the properties of palmitic acid extracted from plantain stalk waste. The study involved preparing plantain stalks, applying freeze-drying or non-freeze-drying methods, extracting palmitic acid, and characterizing the samples using GC-MS, FTIR, XRD, EDX, and SEM. The GC-MS analysis revealed that freeze-drying significantly increased the concentration of palmitic acid compared to non-freeze-drying, as indicated by the higher peak area and height. FTIR spectra demonstrated that freeze-drying better preserved the structural integrity of palmitic acid, while XRD analysis showed enhanced crystalline structure and increased crystallite size in the freeze-dried sample. Additionally, EDX showed that freeze-drying increased the carbon content and decreased the oxygen content of the material, while SEM analyses indicated that freeze-drying resulted in a more porous and irregular surface compared to the denser, smoother structure of the non-freeze-dried sample. The mass, energy, and cost analysis revealed that freeze-drying resulted in a higher yield of palmitic acid compared to non-freeze-drying methods, which, despite being more cost-effective and requiring less energy, demonstrated a notable trade-off in extraction efficiency. The study showed that freeze-drying significantly improved the preservation, concentration, and structural integrity of palmitic acid extracted from plantain stalk waste compared to the non-freeze-drying method.

Psychometrics of drift-diffusion model parameters derived from the Eriksen flanker task: Reliability and validity in two independent samples.

The flanker task is a widely used measure of cognitive control abilities. Drift-diffusion modeling of flanker task behavior can yield separable parameters of cognitive control-related subprocesses, but the parameters' psychometrics are not well-established. We examined the reliability and validity of four behavioral measures: (1) raw accuracy, (2) reaction time (RT) interference, (3) NIH Toolbox flanker score, and (4) two drift-diffusion model (DDM) parameters-drift rate and boundary separation-capturing evidence accumulation efficiency and speed-accuracy trade-off, respectively. Participants from two independent studies - one cross-sectional (N = 381) and one with three timepoints (N = 83) - completed the flanker task while electroencephalography data were collected. Across both studies, drift rate and boundary separation demonstrated comparable split-half and test-retest reliability to accuracy, RT interference, and NIH Toolbox flanker score, but better incremental convergent validity with psychophysiological measures (i.e., the error-related negativity; ERN) and neuropsychological measures of cognitive control than the other behavioral indices. Greater drift rate (i.e., faster and more accurate responses) to congruent and incongruent stimuli, and smaller boundary separation to incongruent stimuli were related to 1) larger ERN amplitudes (in both studies) and 2) faster and more accurate inhibition and set-shifting over and above raw accuracy, reaction time, and NIH Toolbox flanker scores (in Study 1). Computational models, such as DDM, can parse behavioral performance into subprocesses that exhibit comparable reliability to other scoring approaches, but more meaningful relationships with other measures of cognitive control. The application of these computational models may be applied to existing data and enhance the identification of cognitive control deficits in psychiatric disorders.

Exploring the geographical equity-efficiency tradeoff in cycling infrastructure planning

Cycling is affordable, healthy, and sustainable, but access to destinations on low-stress safe cycling routes in most cities is both limited and unevenly distributed. Many cities are expanding cycling networks to improve safety, increase cycling mode share, and increase diversity in access to cycling, however resources remain limited which requires prioritization of infrastructure. When proposed infrastructure locations are optimized to provide the highest average access to opportunities using a utilitarian definition of accessibility, marginalized groups and locations may be further left behind. This occurs since the greatest gains to network connectivity, using a utility definition, come from expansions inside or directly adjacent to the densest network areas. We compare utilitarian and equity-driven planning strategies for cycling network expansion and explore tradeoffs in spatial coverage, equity, and efficiency, using Toronto, Canada as a case study. We find that optimizing accessibility in several small regions instead of city-wide leads to an infrastructure plan that is more spatially dispersed. Further, we show that an optimization model targeting low-access areas produces an infrastructure plan with more regions meeting a minimum threshold of accessibility but with lower average accessibility gains, indicating the presence of an equity-efficiency tradeoff. We also find that infrastructure projects that maximize a region's accessibility to jobs are often located outside that region, challenging political perceptions of "local" infrastructure and benefits. These results inform planning, advocacy, design, and policy, and shed light on spatial and socio-demographic equity tradeoffs in deciding where to add cycling infrastructure.

Fuzzy optimization of the photo-Fenton process on o-toluidine degradation in the aspect wastewater treatment

Significant volumes of wastewater, particularly from the textile industry, pose environmental concerns due to the presence of hazardous substances such as ortho-toluidine (OT). The photo-Fenton process can be used to break down and remove this hazardous organic compound. Previous studies on the photo-Fenton process have focused on local optimization of operating variables without considering cost factors. The photo-Fenton process is studied in this paper with UVA irradiation, Fe2+ dosage, and H2O2 concentration considered as variables. The study uses fuzzy optimization in a multi-objective framework for making decisions to determine the optimal values of OT degradation with its corresponding cumulative uncertainty error (YA), and the total operating cost (CT), both of which are essential for assessing the techno-economic feasibility of the process. The Pareto front was generated from the objective functions to establish the boundary limits for YA and CT. The results show an overall satisfaction level of 71.81% for the objective functions, indicating a partially satisficing solution for maximizing OT degradation while minimizing operating cost. The optimum conditions of the variables require 85.70 W m−3 UVA irradiation, 0.5177 mM for Fe2+ dosage, and 7.85 mM for the H2O2 concentration. These conditions yielded an OT degradation value of 83.22% and a total operating cost of 768.61 USD·m−3. Comparison with previous literature showed an OT degradation efficiency that was 16.78% lower. However, this tradeoff in the process efficiency is offset by a total operating cost that is 2.28 times cheaper, emphasizing the cost-effectiveness of the fuzzy optimized solution.

Improved PointNet with accuracy and efficiency trade-off for online detection of defects in laser processing

Structural light vision sensing is widely applied in online detecting defects of laser processing due to their anti-interference ability for laser beams. However, the existing algorithms cannot extract the characteristics of weld defects with high precision. The computing cost of large-scale point cloud data is high. The balance between them is the main challenge to achieve online detection. To improve accuracy and reduce computation costs, this study uses point cloud data with depth information and proposes a point cloud segmentation method. It is a novelty method based on PointNet framework that has been verified for laser welding defect detection. Specifically, it used the PointNet framework as the backbone. It extracted enough local features of weld defects by multi-scale feature fusion, which concatenated features from different feature extraction layers to learn enough features to improve detection accuracy. The experiments were conducted on the real dataset of welds. The results showed its competitive performance in weld bead measurement and classification segmentation, and the accuracy of this method is 97.4 %. The proposed method improved mean intersection-over-union (mIoU) by 2.1 % compared with its backbone (PointNet), indicating a better segmentation accuracy. In addition, the proposed method improved detection speed compared with PointNet++. It can reach 60 frames per second, 7.5 times faster than PointNet++ and meet the online monitoring requirements. To conclude, the new detection method based on the improved PointNet with higher accuracy and faster speed of detection has a wide application prospect thanks to its novel model.

Exploiting Axisymmetry to Optimize CFD Simulations—Heave Motion and Wave Radiation of a Spherical Buoy

Simulating the free decay motion and wave radiation from a heaving semi-submerged sphere poses significant computational challenges due to its three-dimensional complexity. By leveraging axisymmetry, we reduce the problem to a two-dimensional simulation, significantly decreasing computational demands while maintaining accuracy. In this paper, we exploit axisymmetry to perform a large ensemble of Computational Fluid Dynamics (CFDs) simulations, aiming to evaluate and maximize both accuracy and efficiency, using the Reynolds Averaged Navier–Stokes (RANS) solver interFOAM, in the opensource finite volume CFD software OpenFOAM. Validated against highly accurate experimental data, extensive parametric studies are conducted, previously limited by computational constraints, which facilitate the refinement of simulation setups. More than 50 iterations of the same heaving sphere simulation are performed, informing efficient trade-offs between computational cost and accuracy across various simulation parameters and mesh configurations. Ultimately, by employing axisymmetry, this research contributes to the development of more accurate and efficient numerical modeling in ocean engineering.

Drone delivery problem with multi-flight level: Machine learning based solution approach

This study provides a new perspective on the drone delivery problems (DDP) by conceptualizing the vertical space in multiple flight levels. The main advantage of drone delivery is efficiency in utilizing free three-dimension aerial space, enabling numerous travels at multiple flight levels. However, the operational efficiency tradeoff exists according to the flight level, particularly in metropolitan cities with countless skyscrapers. Operation on the upper level requires less detour on horizontal movement, but it needs more time on the vertical movement of drones to reach the upper level. This study introduces a novel DDP by dividing the vertical airspace into multiple flight levels, thereby providing an opportunity to increase overall delivery efficiency based on realistic constraints faced by cities. We formulate this problem into a mathematical model and suggest a new supervised machine learning approach called SPML (Sequential Prediction Machine Learning). The SPML has three phases. In the first phase, customers are sequenced by priority. The second phase uses a supervised machine learning model trained by the data collected from solving the mixed-integer linear programming (MILP) model to assign customers to the depot. The third phase is distributing jobs to drones by using dynamic programming.

Using a negative spatial auto-correlation index to evaluate and improve intrinsic tag map’s multi-scale visualization capabilities

ABSTRACT The popularity of tag clouds has sparked significant interest in the geographic research community, leading to the development of map-based adaptations known as intrinsic tag maps. However, existing methodologies for tag maps primarily focus on tag layout at specific scales, which may result in large empty areas or close proximity between tags when navigating across multiple scales. This issue arises because initial tag layouts may not ensure an even distribution of tags with varying sizes across the region. To address this problem, we incorporate the negative spatial auto-correlation index into tag maps to assess the uniformity of tag size distribution. Subsequently, we integrate this index into a TIN-based intrinsic tag map layout approach to enhance its ability to support multi-scale visualization. This enhancement involves iteratively filtering out candidate tags and selecting optimal tags that meet the defined index criteria. Experimental findings from two representative areas (the USA and Italy) demonstrate the efficacy of our approach in enhancing multi-scale visualization capabilities, albeit with trade-offs in compactness and time efficiency. Specifically, when retaining the same number of tags in the layout, our approach achieves higher compactness but requires more time. Conversely, when reducing the number of tags in the layout, our approach exhibits reduced time requirements but lower compactness. Furthermore, we discuss the effectiveness of various applied strategies aligned with existing approaches to generate diverse intrinsic tag maps tailored to user preferences. Additional details and resources can be found on our project website: https://github.com/TrentonWei/Multi-scale-TagMap.

Optimizing blockchain for healthcare IoT: a practical guide to navigating scalability, privacy, and efficiency trade-offs

The adoption of blockchain technology provides significant disruptive benefits to internet-of-things (IoT) applications in healthcare in vital aspects like security, integrity, transparency, and efficiency. Nevertheless, in order to fully realize the potential of blockchain-driven solutions, healthcare organizations have to address intricate compromises between essential factors including scalability, privacy and resource utilization considering that the data sensitivity alongside strict regulatory compliance requirements characterize this sector. This research discusses the fundamental aspects of these trade-offs, including the range of consensus protocols (e.g. proof-of-work, proof-of-stake) and cryptographic techniques (e.g. zero-knowledge proofs, homomorphic encryption). A systematic choice matrix is created, which relates specific use cases of the healthcare IoT to the optimal tailored blockchain structures on such critical metrics as transaction volume, frequency, privacy level and resource restrictions. The suggested framework provides solid, actionable recommendations to healthcare organizations in order to help them benefit from the enormous promise of the blockchain for connected IoT healthcare by finding a balance between decentralization advantages and performance, security and compliance requirements.

Instantaneous growth: a compact measure of efficient carbon and nitrogen allocation in leaves and roots of C3 and C4 plants.

Elucidating plant functions and identifying crop productivity bottlenecks requires the accurate quantification of their performance. This task has been attained through photosynthetic models. However, their traditional focus on the leaf's capacity to uptake CO2 is becoming increasingly restrictive. Advanced bioengineering of C3 plants has made it possible to increase rates of CO2 assimilation by packing photosynthetic structures more densely within leaves. The operation of mechanisms that concentrate CO2 inside leaves can boost rates of assimilation while requiring a lower investment in carboxylating enzymes. Therefore, whether in the context of spontaneous plants or modern manipulation, considering trade-offs in resource utilization efficiency emerges as a critical necessity. I've developed a concise and versatile analytical model that simulates concurrent leaf and root growth by balancing instantaneous fluxes of carbon and nitrogen. Carbon is made available by leaf photosynthesis, encompassing all types of biochemistries, while nitrogen is either taken up by roots or remobilized after senescence. The allocation of leaf nitrogen between light or carbon reactions was determined using a fitting algorithm: growth maximisation was the only reliable fitting goal. Both the leaf nitrogen pool and the root-to-leaf ratio responded realistically to various environmental drivers (CO2 concentration, light intensity, soil nitrogen), replicating trends typically observed in plants. Furthermore, modifying the strength of CO2 concentrating mechanisms proved sufficient to alter the root-to-leaf ratio between C3 and C4 types. This direct and mechanistic one-to-one link convincingly demonstrates, for the first time, the functional dependence of a morphological trait on a single biochemical property.