Efficiency Tradeoff Research Articles

Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)

Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)

Enhanced mental health prediction with deep neural networks for accurate diagnosis

This research investigates machine learning models for predicting mental health consequences using survey data. The study employs a two-phase approach first, it utilizes TensorFlow for initial Deep Neural Network (DNN) model building, and then it applies Random Forest (RF), Naive Bayes classifier, and decision tree methods for comparative analysis. The DNN model demonstrates strong performance, achieving high accuracy in mental health prediction. Metrics such as testing time, precision, mean absolute error, and accuracy are compared to provide insight into the advantages and disadvantages of each model. While the DNN model excels in accuracy and precision, other models offer trade-offs in computational efficiency. The results clarify the role of machine learning in mental wellness evaluation and intervention, providing guidance for further research and real-world applications. This research enhances the discourse on predictive modeling for mental health outcomes, facilitating advancements in leveraging machine learning to improve mental health assessment and intervention strategies.

Accelerated Lifetime Model-Based Design Optimization Strategy With Efficiency, Reliability, and Cost Trade-Off for High-Power Modular AFE Rectifiers

Accelerated Lifetime Model-Based Design Optimization Strategy With Efficiency, Reliability, and Cost Trade-Off for High-Power Modular AFE Rectifiers

Robust Beamforming Design for RSMA-Integrated Full-Duplex Communications: Energy and Spectral Efficiency Trade-off

Robust Beamforming Design for RSMA-Integrated Full-Duplex Communications: Energy and Spectral Efficiency Trade-off

Energy-Spectral Efficiency Trade-Off in IRS-Assisted NOMA Systems: A Weighted Product Method

Energy-Spectral Efficiency Trade-Off in IRS-Assisted NOMA Systems: A Weighted Product Method

Revisiting CES utility functions for distributional preferences: Do people face the equality–efficiency trade-off?

Revisiting CES utility functions for distributional preferences: Do people face the equality–efficiency trade-off?

Influence of ‘period four’ transition metal doping in graphene on adsorption and transduction characteristics for CO gas- A detailed ab-initio perspective

This work analyses the comparative effects of period-four transition metal (TM) dopants for CO molecular adsorption on the monolayer Graphene (Gr) supercell using the density functional theory (DFT) based ab initio method for the first time. Ten different TM dopant species (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cr, Zn) have been incorporated and extensively studied in the context of Carbon Monoxide (CO) adsorption. The study elaborates on the effects of metallic doping in Gr on structural stability, electronic properties, adsorption strength, transduction efficacy, and CO recovery time. The study reveals that introducing each period-four TM dopant in the Gr lattice changes the semi-metallic nature, wherein distinct modulations in the energy band structure and the total density of state profiles can be observed after CO adsorption in each doped Gr matrix. The C atom of the polar CO molecule preferentially adsorbed on the doped TM, forming physical C-X (X: metal) bonds and resulting in slight vertical displacement of the dopant towards adsorbed CO. The results exhibit that depending on the strength of CO adsorption, the metallic dopants can be placed in the following order: Ti > V > Cr > Mn > Fe > Co > Ni > Cu > Zn > Sc, with a significant improvement in charge transfer during CO adsorption after Sc, Co, Ni, V, and Zn doping in Gr. Specifically, the Ni, Zn, and Sc-doped Gr ensures an efficient trade-off between adsorption stability and recovery time with high selectivity in CO2 and N2 environments.

The Effectiveness of Slightly Thick Liquids for Improving Swallowing in Bottle-Fed Children With Aerodigestive Concerns.

Strategies for facilitating safe and functional bottle feeding in children with dysphagia include selecting nipples that reduce flow rate, pacing, altered positioning, and thickening liquid consistencies. We aimed to determine the impact of slightly thick liquids on swallowing through retrospective review of a convenience sample of clinical videofluoroscopic swallowing studies (VFSS) from 60 bottle-fed children (21 boys and 39 girls, mean age of 9.9 months) referred due to suspected aspiration. Eligible VFSS exams were those in which the child swallowed both thin and slightly thick barium (40% w/v Varibar barium) using the same nipple. VFSS sequences (i.e., uninterrupted portions of the VFSS recording) were randomly assigned in duplicate for rating by trained raters; discrepancies were resolved by consensus. Parameters measured included number of swallows/sequence, sucks/swallow, swallow and sequence duration, number and timing of penetration or aspiration events, laryngeal vestibule closure integrity, and pharyngeal residue. Chi-square tests, linear mixed-model analyses of variance, and Wilcoxon signed-ranks tests identified consistency effects. There were no aspiration events in these recordings. Slightly thick liquids resulted in significantly fewer penetration events (p < .05), increased sucks/swallow, fewer swallows/sequence, and longer swallow and sequence durations. The number of children with ≥ 1 sequence showing pyriform sinus residue was significantly higher with slightly thick liquids. Slightly thick liquids can be effective in reducing penetration in bottle-fed children with dysphagia. However, slightly thick liquids may also lead to a safety-efficiency trade-off, with increased risk of pyriform sinus residue. Thickening for children with dysphagia should be considered only when other approaches are not effective. Overthickening should be avoided to limit negative impact on swallowing efficiency.

90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework

Purpose90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy–efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods.MethodsOur unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections and CT, and the output is the absorbed dose-rate map. For training and testing under realistic conditions, we generated a series of virtual patient phantom activity/density maps from post-therapy images of patients treated with 90Y-RE at our clinic. To train the scatter estimation network, we use the scatter projections for phantoms generated from MC simulation as the ground truth (GT). To train the dosimetry network, we use MC dose-rate maps generated directly from the activity/density maps of phantoms as the GT (Phantom + MC Dose). We compared performance of our framework (SPECT w/CNN SC + DblurDoseNet) and MC dosimetry (SPECT w/CNN SC + MC Dose) using normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) relative to GT.ResultsWhen testing on virtual patient phantoms, our CNN predicted scatter projections had NRMSE of 4.0% ± 0.7% on average. For the SPECT reconstruction with CNN SC, we observed a significant improvement on NRMSE (9.2% ± 1.7%), compared to reconstructions with no SC (149.5% ± 31.2%). In terms of virtual patient dose-rate estimation, SPECT w/CNN SC + DblurDoseNet had a NMAE of 8.6% ± 5.7% and 5.4% ± 4.8% in lesions and healthy livers, respectively; compared to 24.0% ± 6.1% and 17.7% ± 2.1% for SPECT w/CNN SC + MC Dose. In patient dose-rate maps, though no GT was available, we observed sharper lesion boundaries and increased lesion-to-background ratios with our framework. For a typical patient data set, the trained networks took ~ 1 s to generate the scatter estimate and ~ 20 s to generate the dose-rate map (matrix size: 512 × 512 × 194) on a single GPU (NVIDIA V100).ConclusionOur deep learning framework, trained using true activity/density maps, has the potential to outperform non-learning voxel dosimetry methods such as MC that are dependent on SPECT image quality. Across comprehensive testing and evaluations on multiple targeted lesions and healthy livers in virtual patients, our proposed deep learning framework demonstrated higher (66% on average in terms of NMAE) estimation accuracy than the current “gold-standard” MC method. The enhanced computing speed with our framework without sacrificing accuracy is highly relevant for clinical dosimetry following 90Y-RE.

Group efficiency and individual fairness tradeoff in making wise decisions

In group decision making, the consensus model with minimum cost has been researched with the aim of improving group efficiency and saving resources. However, one limitation of the minimum cost consensus model is that the reach of consensus is usually at the expense of some group members. We consider two issues that we see as keys in group consensus: efficiency and fairness. We propose the price of fairness in the opinion revision process and give two kinds of fairness schemes. According to the individual's perception of inequity, we introduce inequity aversion parameters and classify experts into two types: experts with non-cooperative behaviors and with altruistic behaviors. Experts with altruistic behaviors will be allowed to contribute more than the recommended number of modifications. Then, we discuss how to achieve the tradeoff between efficiency and fairness. Furthermore, with the rapid development of social media, cloud, and e-government platforms, collective intelligence (CI), i.e., groups of individuals doing things collectively that seem intelligent, has been a hot topic. We expand our work to a crowd context with many individuals. We investigate how the opinion revision process and fairness schemes can influence the emergence of CI. Results suggest that the proportional fairness and max-min fairness have similar performance in stimulating CI. Moreover, the improvement of group accuracy is mainly related to two factors: the group consensus level of initial opinions and the relative distance between group aggregated opinion and the ground truth.

Use of Health Technology Assessment for the Continued Funding of Health Technologies: The Case of Immunoglobulins for the Management of Multifocal Motor Neuropathy.

Funding decisions for many health technologies occur without undergoing health technology assessment (HTA), in particular, without assessment of cost effectiveness (CE). Immunoglobulins in Australia are an interesting case study because they have been used for a long time for various rare disorders and their price is publicly available. Undertaking an HTA enables us to assess CE for an intervention for which there is limited clinical and economic evidence. This study presents a post-market review to assess the CE of immunoglobulins for the treatment of multifocal motor neuropathy (MMN) compared with best supportive care. A Markov model was used to estimate costs and quality-adjusted life-years (QALYs). Input sources included randomised controlled trials, single-arm studies, the Australian clinical criteria for MMN, clinical guidelines, previous Medical Services Advisory Committee (MSAC) reports and inputs from clinical experts. Sensitivity analyses were conducted to assess the uncertainty and robustness of the CE results. The cost per patient of treating MMN with immunoglobulin was AU$275,853 versus AU$26,191when no treatment was provided, with accrued QALYs of 6.83 versus 6.04, respectively. The latter translated into a high incremental cost-effectiveness ratio (ICER) of AU$317,552/QALY. The ICER was most sensitive to the utility weights and the price of immunoglobulins. MSAC advised to continue funding of immunoglobulins on the grounds of efficacy, despite the high and uncertain ICER. Beyond the ICER framework, other factors were acknowledged, including the high clinical need in a patient population for which there are no other active treatments available. This case study highlights the challenges of conducting HTA for already funded interventions, and the efficiency trade-offs required to fund effective high-cost therapies in rare conditions.

Toward a moral approach to stakeholder management: insights from the inclusion of marginalized stakeholders in the operations of social enterprises

PurposePrevious literature on sustainable supply chain management has largely adopted an instrumental view of stakeholder management and has focused on understanding the effect of powerful stakeholders who have a more decisive influence on an organization's supply chain decisions. Social enterprises have emerged as organizations that often aim to create impact by integrating marginalized stakeholders into their operations and supply chains. This study examines the trade-offs that social enterprises experience due to their moral stance toward stakeholder engagement, evidenced in their commitment to serving marginalized stakeholders, as well as the responses adopted to these trade-offs.Design/methodology/approachThe study follows a theory elaboration approach through a multiple case study design. The authors draw on insights from stakeholder theory and use the empirical insights to expand current constructs and relationships in a novel empirical context. Based on an in-depth analysis of primary and secondary qualitative data on ten social enterprises, the authors examine how these organizations integrate marginalized stakeholders into various roles in their operations.FindingsWhen integrating marginalized customers, suppliers and employees, social enterprises face affordability, reliability and efficiency trade-offs. Each trade-off represents conflicts between the organization's needs and the needs of marginalized stakeholders. In response to these trade-offs, social enterprises choose to internalize the costs through slack creation or vertical integration or externalize the costs to stakeholders. The ability to externalize is contingent on the growth orientation of the organization and the presence of like-minded B2B (Business-to-Business) customers. These responses reflect whether organizations accept the trade-offs at the expense of one or more stakeholders or if they avoid the trade-offs and find mutually beneficial solutions.Originality/valueBuilding on the empirical insights, the authors elaborate on stakeholder theory with a focus on the integration of marginalized stakeholders by emphasizing a moral justification for stakeholder engagement, identifying the nature of the underlying trade-offs which can arise when various stakeholder needs are in conflict and examining the contingencies affecting organizational responses to these trade-offs.

Using multiple outcomes in intervention studies: improving power while controlling type I errors

Background The CONSORT guidelines for clinical trials recommend use of a single primary outcome, to guard against the raised risk of false positive findings when multiple measures are considered. It is, however, possible to include a suite of multiple outcomes in an intervention study, while controlling the familywise error rate, if the criterion for rejecting the null hypothesis specifies that N or more of the outcomes reach an agreed level of statistical significance, where N depends on the total number of outcome measures included in the study, and the correlation between them. Methods Simulations were run, using a conventional null-hypothesis significance testing approach with alpha set at .05, to explore the case when between 2 and 12 outcome measures are included to compare two groups, with average correlation between measures ranging from zero to .8, and true effect size ranging from 0 to .7. In step 1, a table is created giving the minimum N significant outcomes (MinNSig) that is required for a given set of outcome measures to control the familywise error rate at 5%. In step 2, data are simulated using MinNSig values for each set of correlated outcomes and the resulting proportion of significant results is computed for different sample sizes,correlations, and effect sizes. Results The Adjust NVar approach can achieve a more efficient trade-off between power and type I error rate than use of a single outcome when there are three or more moderately intercorrelated outcome variables. Conclusions Where it is feasible to have a suite of moderately correlated outcome measures, then this might be a more efficient approach than reliance on a single primary outcome measure in an intervention study. In effect, it builds in an internal replication to the study. This approach can also be used to evaluate published intervention studies.

Upregulation of a Small-World Brain Network Improves Inhibitory Control: An fNIRS Neurofeedback Training Study.

The aim of this study was to investigate the inner link between the small-world brain network and inhibitory control. Functional near-infrared spectroscopy (fNIRS) was used to construct a neurofeedback (NF) training system and regulate the frontal small-world brain network. The small-world network downregulation group (DOWN, n = 17) and the small-world network upregulation group (UP, n = 17) received five days of fNIRS-NF training and performed the color-word Stroop task before and after training. The behavioral and functional brain network topology results of both groups were analyzed by a repeated-measures analysis of variance (ANOVA), which showed that the upregulation training helped to improve inhibitory control. The upregulated small-world brain network exhibits an increase in the brain network regularization, links widely dispersed brain resources, and reduces the lateralization of brain functional networks between hemispheres. This suggests an inherent correlation between small-world functional brain networks and inhibitory control; moreover, dynamic optimization under cost efficiency trade-offs provides a neural basis for inhibitory control. Inhibitory control is not a simple function of a single brain region or connectivity but rather an emergent property of a broader network.

Efficiency and fairness trade-offs in two player bargaining games

Recent work on the evolution of social contracts and conventions has often used models of bargaining games, with reinforcement learning. A recent innovation is the requirement that every strategy must be invented either through through learning or reinforcement. However, agents frequently get stuck in highly-reinforced “traps” that prevent them from arriving at outcomes that are efficient or fair to the both players. Agents face a trade-off between exploration and exploitation, i.e. between continuing to invent new strategies and reinforcing strategies that have already become highly reinforced by yielding high rewards. In this paper I systematically study the relationship between rates of invention and the efficiency and fairness of outcomes in two-player, repeated bargaining games. I use a basic reinforcement learning model with invention, and five variations of this model, designed introduce various forms of forgetting, to prioritize more recent reinforcement, or to maintain a higher rate of invention. I use computer simulations to investigate the outcomes of each model. Each models shows qualitative similarities in the relationship between the efficiency and fairness of outcomes, and the relative amount of exploration or exploitation that takes place. Surprisingly, there are often trade-offs between the efficiency and the fairness of the outcomes.

Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

Attacks on Encrypted Response-Hiding Range Search Schemes in Multiple Dimensions

In this work, we present the first database reconstruction attacks against response-hiding private range search schemes on encrypted databases of arbitrary dimensions. Falzon et al. (VLDB 2022) present a number of range-supporting schemes on arbitrary dimensions exhibiting different security and efficiency trade-offs. Additionally, they characterize a form of leakage, structure pattern leakage, also present in many one-dimensional schemes e.g., Demertzis et al. (SIGMOD 2016) and Faber et al. (ESORICS 2015). We present the first systematic study of this leakage and attack a broad collection of schemes, including schemes that allow the responses to contain false-positives (often considered the gold standard in security). We characterize the information theoretic limitations of a passive persistent adversary. Our work shows that for range queries, structure pattern leakage can be as vulnerable to attacks as access pattern leakage. We give a comprehensive evaluation of our attacks with a complexity analysis, a prototype implementation, and an experimental assessment on real-world datasets.

Secured Clustering Multilevel Underwater Acoustic Networks with Dynamic Routing Discovery (SCDRD) Protocol

Abstract: Due to multiple rising automated industry and combat field applications, researchers have developed a growing interest in the area of Underwater Acoustic Sensor Networks (UASNs) over the previous decade. Extending the lifetime of the energy and the consumption of energy in UASNs are enormous challenges. One of the primary difficulties in UASNs is the development of energy-saving routing mechanisms. In recent days the security of the data collected is the main concern because of widely accessible wireless sensors, because these low-power devices are incompatible with complicated encryption methods. As a result, for multi-hop Underwater Acoustic Sensor Networks (UASNs) with non-replenish able energy resources, lifetime optimization and security are two opposing design concerns. This work introduces an Efficient Energy Clustering with Dynamic Routing Discovery protocol (SCDRD protocol), which addresses two competing difficulties by taking into account several characteristics such as probability random walking and the energy balance control mechanism. The suggested protocol detects that energy consumption in the existing network topology is disproportional to energy deployment, which has an impact on the lifetime of wireless sensor networks. Through a quantitative security analysis, this work proposes a non-uniform efficient deployment technique to handle existing energy resource optimization problems and addresses the security location privacy concern. In all circumstances, the proposed Protocol produces the efficient trade-off between the energy balance and the routing mechanisms, allowing sensor networks to last substantially longer. In addition, the deployment of energy in the non-uniform manner results in the increased network’s lifetime and overall packets sent. The results of proposed work is compared with existing cost-aware routing protocols and outperform the existing protocol with respect to various performance parameters like energy, throughput and delivery ratio.

Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems

Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

A review on energy efficiency in autonomous mobile robots

PurposeThis paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.Design/methodology/approachThe systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.FindingsThe review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.Research limitations/implicationsThe review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.Originality/valueThis paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.