Balanced Trade-off Research Articles

Multi-objective optimization of CMOS low noise amplifier through nature-inspired swarm intelligence

This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade-off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the ZDT1, ZDT2, and ZDT3 test functions. The optimized designs were then simulated using the advance design system (ADS) simulator. The results showed that the MOABC and MOPSO techniques are practical and effective in optimizing LNA design, resulting in better performance than previously published works, with a gain of 21.2 dB and a noise figure of 0.848 dB.

Thermal and structural response of a pavement solar collector prototype

Thermal and structural response of a pavement solar collector prototype

Data science and machine learning methods for detecting credit card fraud

Credit card fraud remains a persistent challenge in the realm of financial security, necessitating innovative approaches for detection. This paper presents a comprehensive investigation into credit card fraud detection, focusing on integrating rule-based systems and machine learning methods to enhance accuracy and efficiency. The methodology encompasses data collection from a reputable source, thorough preprocessing, model development, and online execution. Performance evaluation employs a diverse array of metrics, including precision, recall, F1 score, accuracy, confusion matrix, false positive rate, learning curve, precision-recall curve, cumulative gains curve, and ROC curve. Results demonstrate a balanced trade-off between precision and recall, essential for effective fraud detection. Detailed discussions interpret these findings, offering valuable insights and avenues for future research. This research contributes to advancing fraud detection methodologies and holds promise for enhancing financial transaction security

Multifunctional Additive Ethoxy(pentafluoro)Cyclotriphosphazene Enables Safe Carbonate Electrolyte for SiOx-Graphite/NMC811 Batteries

The silicon or silicon monoxide (SiO x )-graphite/nickel-rich (≥80% Ni) LiNi x Mn y Co z O2 (x+y+z = 1, NMC) cell chemistry is widely regarded as one of the most promising chemistries for next-generation electric vehicle batteries for due to its higher energy density compared to state-of-the-art graphite/Ni-rich NMC batteries. However, the drastic volume change during lithiation and delithiation of silicon poses a significant challenge to realize its high capacity with a practical cycle life. Besides performance degradation, it is desirable to address safety risks stemming from, e.g., the high flammability of carbonate solvents and oxygen release from highly delithiated Ni-rich NMC materials.To address these problems, one of the most effective methods is to develop advanced electrolytes that combine flame-retardant properties and excellent electrochemical performance. Generally, in consideration of the balanced trade-offs between cost, scalability, safety, and electrochemical compatibility, the use of flame retardants is a very promising approach to realize the next-generation nonflammable electrolytes.However, it is still a huge challenge to optimize the electrolyte composition to stabilize Si-based and Ni-rich lithium-ion batteries.Here, the compatibility of the flame retardant ethoxy(pentafluoro)cyclotriphosphazene (PFPN) as additive for carbonate electrolytes is studied, for the first time, in the SiO x -graphite/NMC811 full cells. [1] A synergistic effect is found between PFPN and lithium hexafluorophosphate (LiPF6) for the flame retardancy, and a modified electrolyte (1.35M LiPF6 in ethylene carbonate (EC):ethyl methyl carbonate (EMC) (3:7 by vol.) with 10 wt.% fluoroethylene carbonate (FEC) and 5 wt.% PFPN) displays a very low self-extinguishing time of only 3 s/g while also maintaining a high ionic conductivity of 8.4 mS/cm at 25 °C. Furthermore, the morphology evolution and interphase composition of both anode and cathode are investigated, gaining important insights into the decomposition mechanism of PFPN to stabilize the electrode-electrolyte interfaces. Meanwhile, the wetting properties of the modified electrolyte are also improved due to the excellent wettability of PFPN. As a result, half and full coin cells with SiO x -graphite anode and NMC811 cathode display better capacity retention during long-term cycling with the modified electrolyte than with 1M LiPF6 in EC:EMC (3:7 by vol.) with 10 wt.% FEC. In addition, the enhanced cycling performance with the modified PFPN-contained electrolyte is also confirmed for 1 Ah SiO x -graphite/NMC811 pouch cells cycled under various pressures. These results open a promising avenue for the development of safe and high-performance electrolytes for silicon-based lithium-ion batteries.

LPS-Net: Lightweight Parallel Strategy Network for Underwater Image Enhancement

Underwater images are frequently subject to color distortion and loss of details. However, previous enhancement methods did not tackle these mixed degradations by dividing them into sub-problems that could be effectively addressed. Moreover, the parameters and computations required for these methods are usually costly for underwater equipment, which has limited power supply, processing capabilities, and memory capacity. To address these challenges, this work proposes a Lightweight Parallel Strategy Network (LPS-Net). Firstly, a Dual-Attention Enhancement Block and a Mirror Large Receptiveness Block are introduced to, respectively, enhance the color and restore details in degraded images. Secondly, we employed these blocks on parallel branches at each stage of LPS-Net, with the goal of achieving effective image color and detail rendering simultaneously. Thirdly, a Gated Fusion Unit is proposed to merge features from different branches at each stage. Finally, the network utilizes four stages of parallel enhancement, achieving a balanced trade-off between performance and parameters. Extensive experiments demonstrated that LPS-Net achieves optimal color enhancement and superior detail restoration in terms of visual quality. Furthermore, it attains state-of-the-art underwater image enhancement performance on the evaluation metrics, while using only 80.12 k parameters.

Computation and Communication Efficient Adaptive Federated Optimization of Federated Learning for Internet of Things

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster.

Damage Identification in Reinforced Concrete Beams Using Wavelet Transform of Modal Excitation Responses

This study focuses on identifying damage in reinforced concrete (RC) beams using time-domain modal testing and wavelet analysis. A numerical model of an RC beam was used to generate various damage scenarios with different severities and locations. Acceleration time histories were recorded for both damaged and undamaged structures. Two damage indices, DI_MW and DI_SW, derived from the wavelet analysis, were employed to determine the location and severity of the damage. The results showed that different wavelet families and specific mother wavelets had varying effectiveness in detecting damage. The Daubechies wavelet family (db2, db6, and db9) detected damage at the center and sides of the RC beams due to good time and frequency localization. The Biorthogonal wavelet family (bior2.8 and bior3.1) provided improved time–frequency resolution. The Symlets wavelet family (sym2 and sym7) offered a balanced trade-off between time and frequency localization. The Shannon wavelet family (shan1-0.5 and shan1-0.1) exhibited good time localization, while the Frequency B-Spline wavelet family (fbsp2-1-0.1) excelled in frequency localization. Certain combinations of mother wavelets, such as shan1-0.5 with the DI_SW index, were highly effective in detecting damage. The DI_SW index outperformed DI_MW across different numerical models. Selecting appropriate wavelet analysis techniques, particularly utilizing shan1-0.5 in the DI_SW, proved effective for detecting damage in RC beams.

Effect of Plant Defenses and Plant Nutrients on the Performance of Specialist and Generalist Herbivores of Datura: A Macroevolutionary Study.

Macroevolutionary patterns in the association between plant species and their herbivores result from ecological divergence promoted by, among other factors, plants' defenses and nutritional quality, and herbivore adaptations. Here, we assessed the performance of the herbivores Lema trilineata daturaphila, a trophic specialist on Datura, and Spodoptera frugiperda, a polyphagous pest herbivore, when fed with species of Datura. We used comparative phylogenetics and multivariate methods to examine the effects of Datura species' tropane alkaloids, leaf trichomes, and plant macronutrients on the two herbivores´ performances (amount of food consumed, number of damaged leaves, larval biomass increment, and larval growth efficiency). The results indicate that species of Datura do vary in their general suitability as food host for the two herbivores. Overall, the specialist performs better than the generalist herbivore across Datura species, and performance of both herbivores is associated with suites of plant defenses and nutrient characteristics. Leaf trichomes and major alkaloids of the Datura species are strongly related to herbivores' food consumption and biomass increase. Although hyoscyamine better predicts the key components of the performance of the specialist herbivore, scopolamine better predicts the performance of the generalist; however, only leaf trichomes are implicated in most performance components of the two herbivores. Nutrient quality more widely predicts the performance of the generalist herbivore. The contrasting effects of plant traits and the performances of herbivores could be related to adaptive differences to cope with plant toxins and achieve nutrient balance and evolutionary trade-offs and synergisms between plant traits to deal with a diverse community of herbivores.

The reversibility of cancelable biometric templates based on iterative perturbation stochastic approximation strategy

Researchers have been proposing different matching frameworks for cancelable template design to improve the matching performance and security of biometric authentication systems. Despite the advantages provided by these systems, they are often vulnerable to different attacks due to their templates' invertible nature. This study utilized an iterative perturbation stochastic approximation procedure to analyze the irreversible order-based encoding approach used in cancelable biometric template transformation. The strategy begins by exploiting the scores corresponding to the encoded words, then a mixed variable-based iterative perturbation is used to generate independent random elements and consequently the corresponding template from the scores. The strategy exploits the vulnerability of three different cancelable systems it has tested, demonstrating that other techniques of similar nature fall short of the security standard for attack mitigation that cancelable biometric systems require. After analyzing the reasons that exploit the system's vulnerability, we use a parameterized thresholding non-uniform quantization as a countermeasure to boost the system's robustness while maintaining a balanced performance-security trade-off. As a result, the system can evade attacks without significantly hindering its matching performance. Finally, generality and computational complexity evaluations on different modalities and strategies validate the attack's efficacy and realism, respectively.

A simulation-based multi-objective two-level optimization decision-making approach for supporting balanced irrigation water management

In this study, a simulation-based multi-objective two-level optimization decision-making approach is developed for optimal irrigation water allocation, improving irrigation water productivity and controlling regional accumulated salts. Techniques of multi-objective programming, two-level programming and simulation model of water and salt physical movement process are incorporated into the modeling framework. The simulated processes are parameterized and calibrated with field experimental data while the optimization model is used to generate optimal solutions though predefined objectives and the associated constraints. This model is applied to a case study on irrigation water allocation in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, Northwest China. Firstly, the study area is delineated into several homogeneous irrigation decision-making units (IDMUs) for better characterizing their spatial variability because it’s spatially heterogeneous. Afterwards, decomposition-coordination algorithm is introduced to solve such an integrated simulation-based multi-objective two-level optimization model. Finally, optimal solutions of irrigation water allocation for different crops during crop growth periods in different IDMUs can be obtained for supporting sustainable strategies of irrigation. The results can achieve balanced tradeoffs between different stakeholders (i.e., the upper-level decision-makers and the lower-level farmers) and between conflicting economic objectives and environmental objectives. Moreover, optimal solutions have a slight increase in economic returns over the baseline scenario (i.e., status quo), but the irrigation water productivity is increased by nearly 60% due to less irrigation water used. Regional accumulated salts can be controlled because the soil salinity is constrained within the predetermined salt accumulation constraint. Therefore, these findings can provide evidence for efficient use of irrigation water resources and further support of sustainable irrigated agriculture.

An approach to attain a balanced trade-off solution for dynamic economic emission dispatch problem on a microgrid system

An approach to attain a balanced trade-off solution for dynamic economic emission dispatch problem on a microgrid system

Tuna Regional Fisheries Management Organizations Need to Prioritize Transition to Management Procedures

Tuna regional fisheries management organizations (tRFMO) have made strong commitments to developing and implementing management procedures as a more effective and science‐based alternative to traditional management approaches. Management procedures have become popular in fisheries management as they use management strategy evaluation testing to successfully account for uncertainty, balance tradeoffs among competing objectives, and establish an algorithm to set fishing opportunities based on stock status. While four tRFMOs have seven management procedures in place and all five tRFMOs are developing management procedures for an additional 15 stocks, delays in development are widespread. No tRFMO has adhered to its agreed‐upon adoption deadline for all 22 management procedures, with 13 management procedure deadlines being delayed by four or more years. Such delays can be reduced or avoided through increased stakeholder involvement, market pressure, and support from external organizations and governments, all of which can be achieved through capacity building as well as the establishment and regular meeting of science–management dialogue groups.

Mixing cover crops suppresses weeds and roto-till reduces urban soil penetration resistance and improves infiltration

Urban soils have been degraded by decades of industrial activities, but they also represent opportunities to improve food sovereignty for urban residents practicing urban agriculture. Urban growers often use varying practices of compost, tillage, and cover cropping, yet further integrated approaches could facilitated by model analyses of how different practices may compare or complement each other. This study examined how tillage methods representing various intensities and cover crop mixes targeting different functions affected agricultural variables including soil penetration resistance, water infiltration rate, herbaceous weedy plant pressure, and crop yield in an urban Technosol in Detroit, MI, USA. Results showed that both roto- and tractor-till significantly lowered soil penetration resistance by ∼50% overall but not yield when compared to no-till, and roto-till also improved infiltration by 15%, while tractor-till reached deeper soils but allowed 7% denser weed growth. Mixing sorghum-sudangrass, buckwheat, and cowpea cover crops significantly reduced weed density by ∼50% compared to other mixtures, and perennials appeared to increase depth to hardpan by ∼2.5 cm (∼17%) but not affect soil water infiltration under no-till. These results reveal that medium-intensity tillage may offer more balanced trade-offs for lowering soil penetration resistance, promoting infiltration, and feasibly minimizing weeds, and that cover crops can help reduce weeds under low-till strategies. Overall this study offers evidence detailing effects of various tillage and cover crop styles that can be of use for smallholder urban growers.

Semantic Segmentation in Underwater Ship Inspections: Benchmark and Data Set

In this article, we present the first large-scale data set for underwater ship lifecycle inspection, analysis and condition information (LIACI). It contains 1893 images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull. The images have been collected during underwater ship inspections and annotated by human domain experts. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. Consequently, we propose to use U-Net with a MobileNetV2 backbone for the segmentation task due to its balanced tradeoff between performance and computational efficiency, which is essential if used for real-time evaluation. Also, we demonstrate its benefits for in-water inspections by providing quantitative evaluations of the inspection findings. With a variety of use cases, the proposed segmentation pipeline and the LIACI data set create new promising opportunities for future research in underwater ship inspections.

Mass transfer enhancement in spacer-filled membrane channels by flow oscillation induced vortex shedding: Numerical study of the effect of amplitude

Techniques inducing unsteady flow have shown potential for promoting vortex shedding in membrane channels, affecting the boundary layer and increasing mass transfer. This work reports a CFD study of the effect of the characteristics of an oscillating flow (i.e., frequency and amplitude) on the occurrence of vortex shedding. The implications of imposing an oscillating flow are analysed by comparing mass transfer enhancement and required pumping power. Results show that the resonant frequency for the perpendicular velocity component does not maximise mass transfer, but it yields a significant increase. For Re = 300, a normalised amplitude of 0.01 appears as a balanced trade-off between mass transfer and pressure drop. The location of onset of vortex shedding in the channel moves upstream as the amplitude increases. Mass transfer reaches an upper limit as the amplitude increases, when the onset of vortex shedding reaches the first filament. The phenomena taking place in a 2D spacer-filled membrane channel, and their implications for real-world applications, are discussed.

REFER: Randomized Online Factor Selection Framework for portfolio Management

Portfolio management is a critical problem in both machine learning and finance communities. To predict the returns of assets, existing studies have been leveraging side information to mine price-sensitive indicators, i.e., factors. However, with the brisk expansion of factor collection, existing factor selection methods face two main issues, i.e., high-cost and low-precision. In this paper, we first formalize the task of online factor selection as an online learning problem where a learner selects a set of factors in each round and aims to minimize the long-term regret. Then, we propose a Randomized onlinE Factor sElection fRamework, named REFER, which not only is particularly devised to address the above two issues, but also can widely serve for any existing multifactor models. Specifically, by studying the regret of existing factor-selecting policies, we propose two randomized policies along with their bandit variants that achieve sublinear regrets. The bandit variants further improve computational efficiency, and achieve a balanced trade-off between cost and precision. Finally, both theoretical analysis and extensive experiments on the real-world dataset demonstrate the effectiveness of our proposed framework and policies in terms of comprehensive evaluation criteria.

UAV-Aided Dual-User Wireless Power Transfer: 3D Trajectory Design and Energy Optimization.

Unmanned aerial vehicles (UAVs) have been widely considered to enhance the communication coverage, as well as the wireless power transfer (WPT) of energy-constrained communication networks to prolong their lifetime. However, the trajectory design of a UAV in such a system remains a key problem, especially considering the three-dimensional (3D) feature of the UAV. To address this issue, a UAV-assisted dual-user WPT system was investigated in this paper, where a UAV-mounted energy transmitter (ET) flies in the air to broadcast wireless energy to charge the energy receivers (ERs) on the ground. By optimizing the UAV's 3D trajectory toward a balanced tradeoff between energy consumption and WPT performance, the energy harvested by all ERs during a given mission period was maximized. The above goal was achieved through the following detailed designs. On the one hand, on the basis of previous research results, there is a one-to-one correspondence between the UAV's abscissa and height, so only the relationship between the height and time was focused on in this work to obtain the UAV's optimal 3D trajectory. On the other hand, the idea of calculus was employed to calculate the total harvested energy, leading to the proposed high-efficiency trajectory design. Finally, the simulation results demonstrated that this contribution is capable of enhancing the energy supply by carefully designing the 3D trajectory of the UAV, compared to its conventional counterpart. In general, the above-mentioned contribution could be a promising way for UAV-aided WPT in the future Internet of Things (IoT) and wireless sensor networks (WSNs).

Determining the Most Economical Work Zone Lengths for Rural Highway Rehabilitation Projects

Despite the wealth of research that has sought to understand the effects of highway work zones, very little definitive information is available concerning the determination of work zone length (WZL) of rural highway rehabilitation projects. Despite its significant impact, adaptive models that holistically estimate reasonable WZLs are very rare. To fill this gap, this study first created a high-confidence data set through a series of scheduling and traffic simulations and subsequently identified critical factors affecting WZL through a descriptive factor analysis. Based on these data sets and findings, a novel decision support framework was developed to determine the most economical WZL in a balanced trade-off between motorists’ inconvenience level induced by traffic disruption and their opportunity cost. The practical applications of the WZL determination framework were then demonstrated through a systematic eight-step procedure, followed by an illustrative use case of an actual project. The results revealed that traffic loading and work zone duration are critical factors, with an important benchmarking point being traffic loading at approximately 41,000 vehicles per day. As the first of its kind, this study will help state transportation agencies devise sounder construction phasing plans by providing a means of establishing WZL that strikes a balance between travelers’ inconvenience and constructability.

Modeling and Efficient Passenger-Oriented Control for Urban Rail Transit Networks

Real-time timetable scheduling is an effective way to improve passenger satisfaction and to reduce operational costs in urban rail transit networks. In this paper, a novel passenger-oriented network model is developed for real-time timetable scheduling that can model time-dependent passenger origin-destination demands with consideration of a balanced trade-off between model accuracy and computation speed. Then, a model predictive control (MPC) approach is proposed for the timetable scheduling problem based on the developed model. The resulting MPC optimization problem is a nonlinear non-convex problem. In this context, the online computational complexity becomes the main issue for the real-time feasibility of MPC. To reduce the online computational complexity, the MPC optimization problem is therefore reformulated into a mixed-integer linear programming (MILP) problem. The resulting MILP problem is exactly equivalent to the original MPC optimization problem and can be solved very efficiently by existing MILP solvers, so that we can obtain the solution very fast and realize real-time timetable scheduling. Numerical experiments based on a part of Beijing subway network show the effectiveness and efficiency of the developed model and the MILP-based MPC method.

An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

The lightweight encryption algorithm based on Add-Rotation-XOR (ARX) operation has attracted much attention due to its high software affinity and fast operation speed. However, lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm. The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost. This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes. Firstly, we specify a hierarchical security framework by quantitatively evaluating the indicators based on side-channel attacks. Then, optimize the masking algorithm to achieve a trade-off balance by leveraging the software-based local masking strategies and non-full-round masking strategies. Finally, refactor the assembly instruction to improve the leaks by exploring the leakage cause at assembly instruction. To illustrate the feasibility of the proposed scheme, we further conducted a case study by designing a software-based masking method for Chaskey. The experimental results show that the proposed method can effectively weaken the impact of physical attacks.