Power Metrics Research Articles

C-Cube

Recent innovations significantly advance the communication capabilities of backscatter networks, while energy harvesting remains the bottleneck of these backscatters. To tackle this problem, existing studies explore distributed beamforming techniques to beam enhanced energy for a battery-free tag. However, they cannot concurrently transfer high-density power to multiple tags since the system is unable to estimate accurate channel state information (CSI). In this paper, we argue that by deploying a lightweight beamforming helper, we can push the limit of CSI estimation with negligible overhead on the tag. The big gap between the wireless charging threshold and the CSI estimation sensitivity can provide stable estimation result of CSI for the charging system even the signal experiences double fading. On this basis, we propose C-Cube, a concurrent charging system for backscatter networks. C-Cube employs distributed beamforming with the help of CSI to indicate the phase alignment process. To bring it into practice, we design a cold start scheme to activate tags by transmitting a distributed orthogonal frequency (DOF) signal. Moreover, to ensure all tags' CSIs have been successfully received, a spatial-based CSI classifier is explored in C-Cube. We further align the beamforming phase to optimize a novel metric called relative power, instead of the received power metric. The new metric can eliminate the impact of the unknown tag-to-helper channel. We implement our system with USRP/GNURadio platform and customized tags in a real environment. C-Cube can achieve the same coverage as the state-of-the-art and reduce the charge time by 44.1% and 30.2%.

Yield of EEG features as markers of disease severity in amyotrophic lateral sclerosis: a pilot study

Objective To clarify the role of electroencephalography (EEG) as a promising marker of severity in amyotrophic lateral sclerosis (ALS). We characterized the brain spatio-temporal patterns activity at rest by means of both spectral band powers and EEG microstates and correlated these features with clinical scores. Methods Eyes closed EEG was acquired in 15 patients with ALS and spectral band power was calculated in frequency bands, defined on the basis of individual alpha frequency (IAF): delta-theta band (1–7 Hz); low alpha (IAF − 2 Hz − IAF); high alpha (IAF − IAF + 2 Hz); beta (13 − 25 Hz). EEG microstate metrics (duration, occurrence, and coverage) were also evaluated. Spectral band powers and microstate metrics were correlated with several clinical scores of disabilities and disease progression. As a control group, 15 healthy volunteers were enrolled. Results The beta-band power in motor/frontal regions was higher in patients with higher disease burden, negatively correlated with clinical severity scores and positively correlated with disease progression. Overall microstate duration was longer and microstate occurrence was lower in patients than in controls. Longer duration was correlated with a worse clinical status. Conclusions Our results showed that beta-band power and microstate metrics may be good candidates of disease severity in ALS. Increased beta and longer microstate duration in clinically worse patients suggest a possible impairment of both motor and non-motor network activities to fast modify their status. This can be interpreted as an attempt in ALS patients to compensate the disability but resulting in an ineffective and probably maladaptive behavior.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts, as these stress states can lead to suboptimal growth and lower yields of target products. To enable the design of genetic circuits for reporting or mitigating the stress states, it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria. Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria, motivating computational methods that can identify robust biomarkers for experimental characterisation and verification. Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis, this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments. We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification (mean f1-score achieved at 0.99), as well as in a range of independent datasets (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

Active Stirling Thermocompressor: Modelling and effects of controlled displacer motion profile on work output

Recent work suggests that Stirling device performance can be improved using a decoupled, actively controlled displacer piston to directly shape the device’s thermodynamic behavior. In this paper, the authors validate the controlled displacer concept using a low-frequency thermocompressor platform whose displacer and power output mechanisms are not kinematically coupled. As a little-known class of Stirling devices whose work output is pneumatic rather than mechanical, the thermocompressor presents new challenges for modeling and experimental validation because few devices have actually been built and tested. The device presented here is currently the highest pressure experimental research platform of a Stirling thermocompressor known to the authors. A third-order dynamic model is derived using first principles and is validated against experimental results. The model contains parameters that are known and/or measurable and as such is not a “fitted” model. This first-principles model is used to explore the influence of different displacer motion profiles on the output of the thermocompressor. The model is able to incorporate an arbitrarily-specifiable displacer motion profile. Using a controlled displacer piston profile, we experimentally demonstrate 1.45X higher peak cycle power and 3.6X higher work output, compared to a traditional sinusoidal displacer motion. This model is appropriate for optimizing the control of the displacer motion with respect to desired thermocompressor power, efficiency, or other performance metrics for any general Stirling thermocompressor.

Three-Dimensional Activity Volcano Plot under an External Electric Field

An external electric field (EEF) can impact a broad range of catalytic processes beyond redox systems. Computational design of catalysts under EEFs targeting specific operation conditions essentially requires accurate predictions of the response of a complex physicochemical system to collective parameters such as EEF strength/direction and temperature. Here, we develop a first-principles-based multiscale approach that enables efficient EEF-dependent kinetic modeling of heterogeneous catalysis. Taking steam reforming of methanol as an example, we find that the methanol conversion rate exhibits strong nonlinear response to temperature and EEF, and the optimal field line and constant carbon concentration line defined in the temperature--EEF parameter space serve as powerful metrics for catalyst design. Assisted with a deep neural network, we establish a three-dimensional activity volcano plot under EEFs for thousands of metallic alloys.

If You Don’t Like Your Job, Can You Always Quit? Pervasive Monopsony Power and Freedom in the Labor Market

One common metric of monopsony power is the quit elasticity, measuring how much more likely a worker is to quit a job in response to a wage change. Experimental and quasi-experimental variation in wages across workers within a given job results in quit elasticities in the 2-3 range, implying that a 10% reduction in wages increases the probability of quitting by 20-30%. In a model with monopsonistic employers, a quit elasticity of 2-3 also implies that workers are paid about 80-85% of the value they produce. These results indicate that employer power is pervasive. We present observational evidence that historically disadvantaged groups have systematically lower quit elasticities, indicating they face even greater employer power. Because monopsony power comes from an inability of workers to voluntarily switch jobs, the quit rate and especially the quit elasticity can be a useful metric for judging the health of the labor market. Pervasive employer power alters the analysis of labor market policy in a number of important ways.

Competitive Racing in Virtual Cycling—Is It Possible, Realistic, and Fair?

Competitive racing through virtual cycling has established itself as an entirely new discipline within cycling. This study explores what equipment racers use and examines important power metrics for racing. Data were collected from three different races from the current ranking of the most highly regulated and professionally organized race series on the virtual cycling platform Zwift. Power output data from 116 race participants, over five power durations (5 s–20 min), and two separate power measuring sources were collected and analyzed using the Bland–Altman method. The findings indicate that the physiological efforts of these races are comparable to those found in traditional competitive cycling. Furthermore, findings also support that the equipment typically used produces similar power outputs with good agreement between different power meters for most measurement points. Finally, the implications of these results for the status of virtual racing are discussed.

Investigation of engine performance, combustion, and emissions using waste tire Oil-Diesel-Glycine max biodiesel blends in a diesel engine

This work investigated engine performance and emissions using waste tire oil-diesel-biodiesel blends. A sustainable fuel glycine max biodiesel was blended with the tire oil-diesel blends to improve performance, combustion, and exhaust emissions. The seven fuels including a 100% diesel 10–30% waste tire oil to 90-70% diesel, 10% tire oil +10% biodiesel +80% diesel, 30% tire oil+10% biodiesel+60% diesel and 10% biodiesel+90% diesel was used as fuels in a direct injection diesel engine. Up to 30% (vol) waste tire pyrolysis oil was blended with diesel. More than 30% of waste tire pyrolysis oil shows inferior solubility issues and inferior engine performance and emissions. Thus, this investigation was limited to 30% waste tire pyrolysis oil. All fuel blends showed similar properties to diesel. With similar engine performance, like torque, power, efficiency, energy, and exergy metrics, the blends showed insignificant variations in emissions (carbon dioxide, nitrogen oxide) compared to a reference diesel fuel. Interestingly, the experimental results were compared with the modelling results, and the maximum variations between them were 10%. The outcome of this research can promote waste tire pyrolysis oil as an alternative fuel for diesel engines and accords with alternative energy development initiatives all over the world.

Design of joint reconfigurable hybrid adder and subtractor using FinFET and GnrFET technologies

Many applications, such as integrated circuit based digital signal processors (DSPs) and arithmetic logical units (ALUs), rely on adders and subtractors, which are fundamental blocks. Traditional adders and subtractors were created using complementary metal oxide semi-conductor (CMOS) and field effect transistor (FET) technologies, which have increased the number of transistors, path delays, and power consumption. Therefore, this article focuses on the implementation of hybrid adders and subtractors utilizing FinFET and graphene nano-ribbon FET (GnrFET) based nanotechnologies. Initially, a multiplexer logic-based carry output predictable full adder (COPFA) is developed with a fast selection of carry-outputs, which significantly reduces the delays generated in sum estimation. Then, a path selectable reconfigurable hybrid adder (PSRHA) is developed by selecting the high-speed and low-speed carry propagation paths through the COPFA module. Further, a path selectable reconfigurable hybrid subtractor (PSRHS) also developed using two-complement PSRHA addition. Finally, a single architecture for both addition and subtraction are achieved through a joint reconfigurable hybrid adder and subtractor (JRHAS). From the simulations, the COPFA module resulted in a propagation delay (PD) of 7.2792 ns, static power consumption (SPC) of 2.4369nw, and total energy consumption (TEC) of 0.9077 nJ, while the PSRHA module resulted in PD of 2.45317 ns, SPC of 2.55681nw, TEC of 0.0912 nJ. In addition, the PSRHS module resulted in PD as 2.5297 ns, SPC as 2.8428nw, TEC as 0.0662 nJ, and the JRHAS module resulted in PD as 17.178 ns, SPC as 5.4036nw, TEC as 2.0543 nJ. Overall simulations revealed that the proposed COPFA, PSRHA, PSRHS, and JRHAS resulted in optimal performance as compared to conventional methods in terms of power, delay, and energy metrics.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman’s r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Multiobjective evolutionary computation for high-order genetic interactions

A number of research works support the relationship between Single Nucleotide Polymorphisms (SNPs) and neurodegenerative diseases (e.g. Alzheimer’s or Parkinson’s Disease). It has been proven that these neurodegenerative diseases are mainly caused by the interaction of different SNPs. The complexity of identifying genetic interactions increases exponentially with two factors: (i) the number of SNPs contained in the biological dataset under study and (ii) the number of SNPs involved in the interaction. Therefore, this paper proposes the application of two of the most successful multiobjective evolutionary algorithms to solve this problem: a Reference-point based Many-objective Fast Non-dominated Sorting Genetic Algorithm (NSGA-III) and a Multiobjective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA). These algorithms have been tested with four datasets (including a real dataset about Bipolar Disorder with 425,574 SNPs) and three interaction sizes: 2, 5, and 8 loci. In addition, they have been compared against well-known and relevant approaches published in the literature, in both multiobjective and biological terms. The results clearly show the advantages of the approach based on NSGA-III. Particularly, NSGA-III improves the results obtained by other algorithms in multiobjective terms (by means of Hypervolume and Set Coverage indicators) and in biological terms (by means of Power, Recall, Precision, and F-measure metrics). Moreover, it reveals new 2 and 5 loci interactions over a Bipolar Disorder real dataset. Therefore, NSGA-III represents a relevant approach to detect high-order genetic interactions.

Production and Characterization of Bacterial Cellulose Separators for Nickel-Zinc Batteries

The need for energy-storing technologies with lower environmental impact than Li-ion batteries but similar power metrics has revived research in Zn-based battery chemistries. The application of bio-based materials as a replacement for current components can additionally contribute to an improved sustainability of Zn battery systems. For that reason, bacterial cellulose (BC) was investigated as separator material in Ni-Zn batteries. Following the biotechnological production of BC, the biopolymer was purified, and differently shaped separators were generated while surveying the alterations of its crystalline structure via X-ray diffraction measurements during the whole manufacturing process. A decrease in crystallinity and a partial change of the BC crystal allomorph type Iα to II was determined upon soaking in electrolyte. Electrolyte uptake was found to be accompanied by dimensional shrinkage and swelling, which was associated with partial decrystallization and hydration of the amorphous content. The separator selectivity for hydroxide and zincate ions was higher for BC-based separators compared to commercial glass-fiber (GF) or polyolefin separators as estimated from the obtained diffusion coefficients. Electrochemical cycling showed good C-rate capability of cells based on BC and GF separators, whereas cell aging was pronounced in both cases due to Zn migration and anode passivation. Lower electrolyte retention was concluded as major reason for faster capacity fading due to zincate supersaturation within the BC separator. However, combining a dense BC separator with low zincate permeability with a porous one as electrolyte reservoir reduced ZnO accumulation within the separator and improved cycling stability, hence showing potentials for separator adjustment.

Passive Components in Next Generation Li-Ion Batteries

Most of the research in development of next generation and high-performance Li-ion batteries has focused on active materials (either in the cathode or anode side). This might sound obvious as active materials are the main component responsible for storing charge in batteries, and therefore, any improvement in them, will directly impact the overall performance of the battery. However, when looking at the cell level, passive components such as current collectors, conductive additives, binders, and separators constitute around 20 wt.% of each battery cell. These so-called passive components while do not directly contribute to the charge storage, their ratio, composition, and structure has a profound effect on the overall performance of the active material and therefore the battery cell. This is mainly due to their importance in charge transport inside the electrode, processing of electrodes, and also cycle life the battery cells. Despite this, they usually receive less attention in development of high power and energy density batteries. If these components are engineered properly or substitute with alternative new materials, potentially, one can improve the performance (energy and power metrics) of a Li-ion battery cell by up to 20%; a number that industry still strives to achieve through modification of active materials which usually is a more complex issue with slower development timeframes. In this talk, we take a closer look at the role of passive components in Li-ion batteries and discuss the possibilities that novel nanomaterials can offer as next generation passive components, both from the standpoint of improvements in metrics of battery cells as well as electrode processing and manufacturing in Li-ion batteries. We will also discuss the cost and safety advantages these materials can offer when used as passive components of the cells and present results and give examples that demonstrate how nanomaterials can in fact act as semi-passive components where they can substitute multiple conventionally used passive components and serve multiple purposes even if directly do not contribute to charge storage. Taking such an approach in development of battery electrodes/cells can potentially pave the way for development of batteries with higher power and energy densities using the current commercially available active materials, and further enable the use of high-capacity materials that degrade quickly and therefore, cannot be used in commercial cells using current technologies.

Effects of cannabis on congenital limb anomalies in 14 European nations: A geospatiotemporal and causal inferential study.

Cannabinoid exposure is increasing in some European nations. Europe therefore provides an interesting test environment for the recently reported link between cannabis exposure and congenital limb anomaly (CLA) rates (CLARs). Exponential genotoxic dose–response relationships make this investigation both intriguing and imperative. Annual CLAR in 14 nations were from Epidemiological Surveillance of Congenital Anomalies. Drug use rates were from European Monitoring Centre for Drugs and Drug Dependency. Median household income was from the World Bank. E-values provide a quantitative measure of robustness of results to confounding by extraneous covariates. Inverse probability weighting is an important technique for equalizing exposures across countries and removing sources of bias. Rates of CLA, hip dysplasia and the whole group of limb anomalies were higher in countries with increasing daily cannabis use (P = 1.81 × 10−16, 0.0005 and 2.53 × 10−6, respectively). In additive inverse-probability-weighted panel models, the limb reduction-resin Δ9-tetrahydrocannabinol (THC) concentration E-value estimate was 519.93 [95% lower bound (mEV) 49.56], order Resin > Herb ≫ Tobacco > Alcohol. Elevations were noted in 86% E-value estimates and 70.2% of mEVs from 57 E-value pairs from inverse-probability-weighted panel models and from spatial models. As judged by the mEV the degree of association with metrics of cannabis exposure was hip dysplasia > polydactyly > syndactyly > limb anomalies > limb reductions with median E-value estimates from 3.40 × 1065 to 7.06 and median mEVs from 6.14 × 1033 to 3.41. Daily cannabis use interpolated was a more powerful metric of cannabis exposure than herb or resin THC exposure. Data indicate that metrics of cannabis exposure are closely linked with CLAR and satisfy epidemiological criteria for causality. Along with Hawaii and the USA, Europe now forms the third international population in which this causal link has been demonstrated. Cannabis as a predictor of limb anomalies was more potent than tobacco or alcohol. Cannabinoid access should be restricted to protect public health and the community genome/epigenome transgenerationally.

Rule with an iron hand: powerful CEOs, influential shareholders and corporate performance in Russia

PurposeThis study examines whether CEO power influences the book-based and market-based performance of Russian companies when it is restricted by the presence of essential shareholders, namely, state and influential businessmen.Design/methodology/approachManagerial power is divided into structural, ownership, expert and prestige. The proposed power metrics include not only CEOs but also the board of directors' characteristics that may restrict or enhance CEO power. The empirical analysis is based on the sample of 90 large traded Russian firms, which shares are included in the Moscow Stock Exchange Broad Market Index (MICEX BMI), observed from 2012 to 2019.FindingsPanel data analysis suggests that higher board ownership and tenure may restrict CEO power, which in turn would be beneficial for corporate performance. the authors also see that in companies owned by influential businessmen, CEO power influence on M/B value is more negative, while state ownership does not moderate it. CEO power metrics, based on political experience and tenure, affect corporate performance differently in companies affiliated with extractive industries.Originality/valueFirst, the authors consider two channels through which a company in emerging markets may get additional resources: CEOs and influential owners. Second, the authors develop power metrics based on Finkelstein's managerial power classification (1992) and the idea of relative power proposed by Bebchuk et al. (2011). It allows identifying whether the board of directors' may constrain or enhance CEO power to raise corporate performance. Third, the authors analyze developing Russian markets that represent a good ground for testing the question, whereas empirical research on Russia is relatively scarce (Grosman and Leiponen, 2018). Fourth, the authors pay particular attention to the CEO power in the extractive industry, strategically important for the Russian economy.

Ocean mover's distance: using optimal transport for analysing oceanographic data.

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean.

A Comparative Performance Analysis of 6T & 9T SRAM Integrated Circuits: SOI vs. Bulk

This letter evaluates the performance of 6T & 9T static random access memory (SRAM) cells, for data stability and power metrics, with the aim to compare silicon-on-insulator (SOI) and bulk CMOS technologies. Each SRAM topology was designed & simulated in 180 nm 5 V XFAB-SOI and AMS-bulk processes, using optimized parameters and compatible devices. The fundamental variables analyzed were read noise margins, write trip current & voltage as well as leakage current (LC) and static power dissipation (SPD) under process and temperature (PT) variations. The static noise margin (SNM) butterfly curve and N-curve methodologies were used to assess the mentioned parameters. Compared to bulk technology, the SRAM cells designed with SOI were found to have lower SPD & LC, higher data stability, lower write ability, larger sensitivity to process variations and higher resilience to temperature deviations.

Designing Innovation – The Role of Engineering Design to Realise Sustainability Challenges

AbstractSustainability challenges drive innovation, yet few studies attend to the role of design to realise innovation. This paper report from a full day workshop and panel discussion with 100 delegates at the ICED 2021 conference. Industrialists, academics and societal representatives discussed how to deal with five conflicting themes. It is argued that innovation actors will need to take a joint action to the problem, industrial value chains need to co-innovate and that long term challenging targets are powerful metrics to drive transformation.

Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer.

Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5–3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.

A Coat of Many Colours—New Concepts and Metrics of Economic Power in Competition Law and Economics

Abstract The digital economy has brought new business models that rely on zero-price markets and multi-sided platforms nested in business ecosystems. The traditional concept of market power used by competition authorities cannot engage with this new reality in which (economic) power manifests beyond price and output within a relevant market. These developments have culminated in multiple recent calls for a more multidimensional concept of power. Consequently, suggestions over new concepts of power triggering antitrust/regulatory intervention, such as ‘strategic market status’, ‘conglomerate market power’, ‘intermediation power’, ‘structuring digital platforms’, or ‘gatekeepers’ have proliferated to complete, or even substitute, the archetypical concept of market or monopoly power in competition law. However, a theoretical framework for this multidimensional concept of power that can set the basis for new metrics is missing. This article makes three contributions in that direction. First, we conceptualize different forms of (economic) power that go beyond competition within a single relevant market in terms of competition law and economics. Second, we propose new metrics to measure two forms of power: panopticon power and power based on differential dependency between value co-creators. Third, we test the latter and show how they could reduce false positives and false negatives when assessing dominance.