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It is crucial to evaluate ethical issues regarding evacuation orders, especially after a nuclear disaster. After the Fukushima accident in 2011, the Japanese government ordered the affected people to evacuate. The evacuation orders have now been lifted in many areas. A risk trade-off analysis between benefits and risk associated with returning home would help in evaluating the justification for the lifting of the evacuation order in the aftermath of a nuclear disaster. Here, we performed a risk trade-off analysis among people returning home after the lifting of an evacuation order between an increase in emotional happiness (positive effect) and loss of life expectancy due to additional radiation exposure (negative effect), using a happy life expectancy (HpLE) indicator. Emotional happiness was estimated using questionnaires distributed among the affected people who lived in municipalities where evacuation orders were lifted. Loss of life expectancy was estimated under a scenario that returnees received 20 mSv in the year of return and subsequent radiation exposure. Increase in emotional happiness due to returning home was ~1–2 orders of magnitude higher among women aged 20, 40 and 65 years than the loss of life expectancy due to additional radiation exposure. This finding has implications for the justification for the lifting of evacuation orders.

The article describes the methodology of weighted modelling to increase the reliability of redundant fibre-optic communication systems. In a specific example, a network graph of a many-node fibre-optic communication system is considered. Applying general ideas to determine the reliability characteristics of systems consisting of a large number of different types of elements with different functional relationships between them is quite a difficult task. The materials presented in this article are intended to solve this problem. Along with their relative simplicity, they are highly accurate. The numerical examples in this chapter show that the use of these methods for highly reliable systems can reduce the variance of the estimate by several orders of magnitude compared to the direct modelling method, and thus reduce the time required for calculations on electronic computers by several orders of magnitude. The purpose of the study is to increase the reliability of fibre-optic communication systems. The research methodology is based on models with a limited number of monotonous failure chains, which is available for visual enumeration of the reliability of highly reliable systems. As a result, it is proposed to obtain an approximate formula for assessing the reliability of a highly reliable system, both by modelling and analytically, and calculations using it can be performed using the quadrature method or moment methods. This allows you to build a model according to the block principle, including full-scale blocks or records of the results of their tests, simplifies the interpretation of the results, and creates convenience in software implementation.

Modern scientific instruments readily record various dynamical phenomena at high frequency and for extended durations. Spanning timescales across several orders of magnitude, such “high-throughput” (HTP) data are routinely analyzed with parametric models in the frequency domain. However, the large size of HTP datasets can render maximum likelihood estimation prohibitively expensive. Moreover, HTP recording devices are operated by extensive electronic circuitry, producing periodic noise to which parameter estimates are highly sensitive. This article proposes to address these issues with a two-stage approach. Preliminary parameter estimates are first obtained by a periodogram variance-stabilizing procedure, for which data compression greatly reduces computational costs with minimal impact to statistical efficiency. Next, a novel test with false discovery rate control eliminates most periodic outliers, to which the second-stage estimator becomes more robust. Extensive simulations and experimental results indicate that for a widely-used model in HTP data analysis, a substantial reduction in mean squared error can be expected by applying our methodology.

The reverse bias current of the tunneling field-effect transistor (TFET) could cause serious damage to static random-access memory (SRAM) circuit performance. To address this issue, a novel reverse bias current eliminated, read-separated, and write-enhanced TFET 12T SRAM bitcell is proposed for ultra-low power applications in this brief. It can prevent reverse bias currents, increase the hold/read static noise margin (H/RSNM) and dramatically decrease the static power consumption. The static power consumption of the proposed bitcell is reduced by four orders of magnitude compared with that of the 7T bitcell, demonstrating its great potential for ultra-low power applications. At a supply voltage of 0.6 V, the H/RSNM of the proposed bitcell is 25% larger than that of the 7T bitcell. It also includes a write assist circuit, thus increasing its write static noise margin (WSNM) and considerably decreasing its write power consumption. The WSNM of the proposed bitcell is more than twice that of the 7T bitcell, and is 60% larger than that of the combinational access bitcell. In addition, the write power consumption of the proposed bitcell is reduced by 95% compared with that of the combinational access bitcell at a supply voltage of 0.6 V. In terms of layout, the area of the proposed 12T bitcell is 92% larger than that of the 7T bitcell, but is 6% smaller than the area of the combinational access bitcell.

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but its scaling properties for this purpose have not been widely studied. Machine learning algorithms are typically evaluated to learn SISO communications and low modulation orders, whereas current wireless standards use MIMO and high-order modulation schemes to increase capacity. The memory requirements of current machine learning algorithms for wireless communications increase exponentially with the number of antennas and thus they cannot be used for advanced physical layers and massive MIMO. In this paper, we study the requirements of end-to-end machine learning models for large-scale MIMO systems, determine the bottlenecks of the architecture, and design different solutions that vastly reduce overhead and allow training higher MIMO and modulation orders. We show that by training the autoencoder in a bit-wise manner, the memory requirements are reduced by several orders of magnitude, which is a critical step for machine learning-based physical layer design in practical scenarios. Besides the reduced memory requirements, our design also improves performance over the classical autoencoder for MIMO systems. TRUE inpress

Generative probabilistic models of biological sequences have widespread existing and potential applications in analyzing, predicting and designing proteins, RNA and genomes. To test the predictions of such a model experimentally, the standard approach is to draw samples, and then synthesize each sample individually in the laboratory. However, often orders of magnitude more sequences can be experimentally assayed than can affordably be synthesized individually. In this article, we propose instead to use stochastic synthesis methods, such as mixed nucleotides or trimers. We describe a black-box algorithm for optimizing stochastic synthesis protocols to produce approximate samples from any target generative model. We establish theoretical bounds on the method’s performance, and validate it in simulation using held-out sequence-to-function predictors trained on real experimental data. We show that using optimized stochastic synthesis protocols in place of individual synthesis can increase the number of hits in protein engineering efforts by orders of magnitude, e.g. from zero to a thousand.

We have conducted a series of experiments to study how defectivity depends on NILS, aerial image intensity and depth-of-focus. Three sets of illuminations were designed to vary only one of these parameters while keeping the others constant for a 48 nm pitch orthogonal contact hole array. We focus on the after etch defectivity, for which two different etch processes were studied. We correlate on-wafer LCDU and defectivity for both etch processes to the different illumination settings. In summary, we found that defect levels correlate best with the maximum intensity for missing holes and minimum intensity for merging holes. Stochastic LCDU is however better predicted by NILS. By improving NILS by 35 percent and the maximum intensity by 32 percent the defect levels could be reduced by 2 orders of magnitude. Depth-of-focus appears to have little or no impact on defectivity for the nominal focus condition.

Significance Facilitating electrochemical reactions on heterogeneous surfaces is key to achieving sustainability through renewable electricity-based production of fuels and chemicals. With prior efforts focused on the electroconversion of small molecules (e.g., H 2 O and CO 2 ), advances involving complex molecules that are abundant and of high value, such as biomass-derived compounds, have been limited. This work proposes an electrochemical method, so-called potential cycling, which enables standard Pt nanoparticles to exhibit high activity for electrooxidation of biomass-derived polyols. Turnover is enhanced by orders of magnitude, overcoming the severe limitation of negligible activity exhibited by the same catalyst under typical electrochemical conditions. Moreover, the demonstration of a two-electrode system that couples polyol electrooxidation with water reduction to H 2 highlights the potential applicability of the method.

The search for exoplanets is pushing adaptive optics systems on ground-based telescopes to their limits. A major limitation is the temporal error of the adaptive optics systems. The temporal error can be reduced with predictive control. We use a linear data-driven integral predictive controller that learns while running in closed-loop. This is a new algorithm that has recently been developed. The controller is tested in the lab with MagAO-X under various conditions, where we gain several orders of magnitude in contrast compared to a classic integrator. With the current schedule, the new data-driven predictive controller will be tested on-sky in spring 2021. We will present both the lab results and the on-sky results, and we will show how this controller can be implemented with current hardware for future extremely large telescopes.

In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer.We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers.

Approximate circuits (ACs) relax the constraint of 100% functional correctness, and can be utilized in many emerging applications, which increases the importance of their reliability analysis. Here, a model and an algorithm are proposed for computing the failure probability of ACs and for determining the critical nodes in these circuits. The presented method is not limited to the type and scale of ACs. Comparison of the results on three series of ACs shows that, the proposed analytical method is more than 98% accurate and four orders of magnitude faster than the random fault injection method. Experiments on circuits in the EvoApprox8b library demonstrate the plausibility of the calculated failure probability and distribution of critical nodes.

Ecotoxicological studies that try to describe the effects of particulate matter (PM) on human health are important in order to gain a deeper understanding of their effects in disease outcomes. Because exposure protocols are not easily comparable, evaluating human PM exposure is a difficult task. Thus, interpreting ambiguous or conflicting results from different experiments could lead to misleading conclusions about the true nature of PM effects. To address these issues, we compiled a collection of relevant research articles in order to compare present PM exposure methods and extract data related to concentration, inhalation rates (IR), and doses. We also compare the experimental exposure levels reported in these articles to PM levels around the world. In particular, our dataset covers reported results from 75 research articles. To allow for comparison between protocols, we used this data to fit a normalization equation that depends upon concentration, exposure time, dose, inhalability, and physiological parameters. Based on the collected research papers, instillation is the prevalent exposure method. Also, the median PM IR from these experiments is three orders of magnitude higher than the PM IR found in environmental conditions (EAP). Experiments employing inhalation of concentrated PM show IR results that are two orders of magnitude higher than EAP; these results are cause for concerns, since the PM exposure were acute, sudden, and higher than the worst-case exposure scenarios reported by the world megacities. We also found that different PM exposure protocols are sources for the observed variability in physiological response results found from animal models. We discuss these findings and make suggestions for future exposure methodologies. Such considerations should be valuable for quantifying PM exposure in disease outcomes.

Sensor networks deployed for environmental monitoring of ecosystems require battery operation that could last years. Energy conservation is essential to enable a prolonged operation. Inactive states or sleep mode usually consumes orders of magnitude less energy compared to data collection and transmission periods. However, they also last orders of magnitude longer. In this paper, we propose the utilization of ultra-low-power timers to increase sensor nodes' lives for monitoring ecosystems. Our experimental data indicates possible battery life extension up to 80 times compared to native sleep mode of the microcontroller. We propose a hardware design for battery saving timer module for sensor data acquisition and transmission.

Cellular and intracellular processes are inherently complex due to the large number of components and interactions, which are often nonlinear and occur at different spatiotemporal scales. Because of this complexity, mathematical modeling is increasingly used to simulate such systems and perform experiments in silico, many orders of magnitude faster than real experiments and often at a higher spatiotemporal resolution. In this article, we will focus on the generic modeling process and illustrate it with an example model of membrane lipid turnover.

In this paper, we propose and evaluate efficient sample point reuse methodologies for Importance Splitting- based yield estimation of memory designs to assess the impact of manufacturing variability induced design center shifts. The proposed methodologies enable yield estimation at almost no additional simulation cost to that incurred due to studying the yield at the nominal design center. In order to unbias the Importance Splitting conditional probabilities with respect to the manufacturing variability centers, we evaluate three techniques that rely on Importance Sampling, center projections and geometric ratios. The geometric-based approach is shown to be most accurate for center shifts up-to three sigma away from the nominal. The proposed reuse methodologies achieve up-to 4 orders of magnitude speedup for the analysis of a 132-D 16nm SRAM design compared to traditional Importance Splitting.

AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to solve AC-OPF problems with high computational efficiency. Its unique design predicts voltages of all buses and then uses them to reconstruct the remaining variables without solving non-linear AC power flow equations. A fast post-processing process is developed to enforce the box constraints. The effectiveness of DeepOPF-V is validated by simulations on IEEE 118/300-bus systems and a 2000-bus test system. Compared with existing studies, DeepOPF-V achieves decent computation speedup up to four orders of magnitude and comparable performance in optimality gap and preserving the feasibility of the solution., 4 pages, 1 figure

Ions are fundamental biological regulators enabling the communication between cells, regulating metabolic and bioenergetic processing and playing a key role in pH regulation and hydration. The in-situ quantification of the ion concentration is gathering relevant interest in biomedical diagnostics and healthcare. State-of-art transistor-based ion sensors show an intrinsic trade-off between sensitivity, operating range and supply voltage. To overcome these limitations, here we focus on ion sensor amplifiers where complementary OECTs are integrated in a push-pull configuration, providing sensitivity larger than 1 V/dec at a supply voltage down to 0.5 V and operating in the physiological range. Ion detection over a range of five orders of magnitude and real-time monitoring of variations two orders of magnitude lower than the detected concentration are achieved. The ion-sensitive amplifier sets a new benchmark for ion-sensing devices, opening possibilities for predictive diagnostics and personalized medicine.

A case study is presented in which several articles and patents suggested a specific outcome. When the actual experimental work was performed, the results were found to be several orders of magnitude away from predicted values. Close re-inspection of the literature suggested that most of the authors actually extrapolated the results to conditions that were not applicable to their specific studies, resulting in the reporting errors. It is important to use literature to assist in research, but it is equally important not to blindly follow it either.

The Legofit statistical package uses genetic data to estimate parameters describing population history. Previous versions used computer simulations to estimate probabilities, an approach that limited both speed and accuracy. This article describes a new deterministic algorithm, which makes Legofit faster and more accurate. The speed of this algorithm declines as model complexity increases. With very complex models, the deterministic algorithm is slower than the stochastic one. In an application to simulated data sets, the estimates produced by the deterministic and stochastic algorithms were essentially identical. Reanalysis of a human data set replicated the findings of a previous study and provided increased support for the hypotheses that (a) early modern humans contributed genes to Neanderthals, and (b) a “superarchaic” population (which separated from all other humans early in the Pleistocene) was either large or deeply subdivided.

Side-channel leakage detection methods based on statistical tests, such as t-test or \(\chi ^2\)-test, provide a high confidence in the presence of leakage with a large number of traces. However, practical limitations on testing time and equipment may set an upper-bound on the number of traces available, turning the number of traces into a limiting factor in side-channel leakage detection. We describe a statistical technique, based on statistical bootstrapping, that significantly improves the effectiveness of leakage detection using a limited set of traces. Bootstrapping generates additional sample sets from an initial set by assuming that it is representative of the entire population. The additional sample sets are then used to conduct additional leakage detection tests, and we show how to combine the results of these tests. The proposed technique, applied to side-channel leakage detection, can significantly reduce the number of traces required to detect leakage by one, or more orders of magnitude. Furthermore, for an existing measured sample set, the method can significantly increase the confidence of existing leakage hypotheses over a traditional (non-bootstrap) leakage detection test. This paper introduces the bootstrapping technique for leakage detection, applies it to three practical cases, and describes techniques for its efficient computation.

Several of the latest GAN-based vocoders show remarkable achievements, outperforming autoregressive and flow-based competitors in both qualitative and quantitative measures while synthesizing orders of magnitude faster. In this work, we hypothesize that the common factor underlying their success is the multi-resolution discriminating framework, not the minute details in architecture, loss function, or training strategy. We experimentally test the hypothesis by evaluating six different generators paired with one shared multi-resolution discriminating framework. For all evaluative measures with respect to text-to-speech syntheses and for all perceptual metrics, their performances are not distinguishable from one another, which supports our hypothesis., Comment: Accepted to Interspeech 2021

Same-day delivery problems are challenging stochastic vehicle routing problems, where dynamically arriving orders have to be delivered to customers within a short time while minimizing costs. In this work, we consider the short-horizon planning of a problem variant where every order has to be delivered with the goal to minimize delivery tardiness, travel times, and labor costs of the drivers involved. Stochastic information as spatial and temporal order distributions is available upfront. Since timely routing decisions have to be made over the planning horizon of a day, the well-known sampling approach from the literature for considering expected future orders is not suitable due to its high runtimes. To mitigate this, we suggest to use a surrogate function for route durations that predicts the future delivery duration of the orders belonging to a route at its planned starting time. This surrogate function is directly used in the online optimization replacing the myopic current route duration. The function is trained offline by data obtained from running full day-simulations, sampling and solving a number of scenarios for each route at each decision point in time. We consider three different models for the surrogate function and compare with a sampling approach on challenging real-world inspired artificial instances. Results indicate that the new approach can outperform the sampling approach by orders of magnitude regarding runtime while significantly reducing travel costs in most cases.

The recent rollout of 5G telecommunications systems has spawned a renewed call to re-examine the possibility of so-called "non-thermal" harmful effects of radiofrequency (RF) radiation. The possibility of calcium being affected by low-level RF has been the subject of research for nearly 50 years and there have been recent suggestions that voltage-gated calcium channels (VGCCs) are "extraordinarily sensitive" to ambient RF fields. This article examines the feasibility of particularly modulated RF coupling to gating mechanisms in VGCCs and also reviews studies from the literature from the last 50 years for consistency of outcome. We conclude that the currents induced by fields at the ICNIRP guideline limits are many orders of magnitude below those needed to affect gating, and there would need to be a biological mechanism for detection and rectification of the extremely-low-frequency (ELF) modulations, which has not been demonstrated. Overall, experimental studies have not validated that RF affects Ca2+ transport into or out of cells.

Intermittent computing (IC) promises long lifetimes for IoT edge devices. Running directly from energy harvesting sources enables these devices to be deployed and left, potentially for decades. As the field of IC progresses from proof-of-concept to deployable devices, the research focus must shift from processor-centric schemes to consideration of the whole system. The non-volatile memory (NVM) technology, as well as the way it is used, will have a significant effect. Properties such as latency, read/write energy, and endurance can vary by orders of magnitude, and this may affect the viability of many schemes presented in the literature. This paper presents a review of the characteristics of both commercially-available and future NVM technologies, and recommends design considerations for IC systems which incorporate these.

The intensity of cosmic radiation can differ over five orders of magnitude within a few hours or days during Solar Particle Events (SPEs), thus increasing the probability of Single-Event Upsets (SEUs) in space applications for several orders of magnitude. Therefore, it is vital to employ the early detection of the SEU rate changes in order to ensure timely activation of the radiation hardening measures. In this paper, a hardware accelerator for forecasting the SPEs by the prediction of in-flight SEU variation is proposed. An embedded on-chip SRAM is used as the real-time particle detector. The dedicated hardware accelerator implements a supervised machine learning model to forecast the SRAM SEUs one hour in advance with fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions. The whole design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications. Therefore, the target system can drive the appropriate radiation hardening mechanisms before the onset of high radiation levels.

To assess terrestrial ecosystems damages from acidification, life cycle impact assessment (LCIA) can be applied using characterisation factors, which integrate the quantification of adverse effects via effect factors (EFs), linking decreasing soil pH to declines in species richness. With a species coverage of 0.6%, the currently-existing EFs defined for 14 biomes carry important uncertainties and lack the appropriate spatial resolution to capture the effects of terrestrial acidification. In our study, we thus used georeferenced observational data with 113 million data points to develop a comprehensive, global inventory covering 189,185 vascular plant species, which is nearly 100 times more species than existing inventories used in LCIA (raising the species coverage to 49%). EFs were then calculated for 825 ecoregions. Great spatial variability is observed between ecoregions within a same biome spanning up to 3 orders of magnitude, and EFs developed at biome level tend to underestimate acidification effects. We find up to 2 orders of magnitude differences between our EFs and the ones currently recommended for use in LCIA. These results call for updating current LCIA practice to assess acidification impacts with our more differentiated and environmentally representative EFs and to implement our approach to other site-dependent environmental impacts where large georeferenced datasets exist.

The ubiquitous presence of more than 80,000 chemicals in thousands of consumer products used on a daily basis stresses the need for screening a broader set of chemicals than the traditional well‐studied suspect chemicals. This high‐throughput screening combines stochastic chemical‐product usage with mass balance‐based exposure models and toxicity data to prioritize risks associated with household products. We first characterize product usage using the stochastic SHEDS‐HT model and chemical content in common household products from the CPDat database, the chemical amounts applied daily varying over more than six orders of magnitude, from mg to kg. We then estimate multi‐pathways near‐ and far‐field exposures for 5,500 chemical‐product combinations, applying an extended USEtox model to calculate product intake fractions ranging from 0.001 to ∼1, and exposure doses varying over more than nine orders of magnitude. Combining exposure doses with chemical‐specific dose–responses and reference doses shows that risks can be substantial for multiple home maintenance products, such as paints or paint strippers, for some home‐applied pesticides, leave‐on personal care products, and cleaning products. Sixty percent of the chemical‐product combinations have hazard quotients exceeding 1, and 9% of the combinations have lifetime cancer risks exceeding 10−4. Population‐level impacts of household products ingredients can be substantial, representing 5 to 100 minutes of healthy life lost per day, with users’ exposures up to 103 minutes per day. To address this issue, present mass balance‐based models are already able to provide exposure estimates for both users and populations. This screening study shows large variations of up to 10 orders of magnitude in impact across both chemicals and product combinations, demonstrating that prioritization based on hazard only is not acceptable, since it would neglect orders of magnitude variations in both product usage and exposure that need to be quantified. To address this, the USEtox suite of mass balance‐based models is already able to provide exposure estimates for thousands of product‐chemical combinations for both users and populations. The present study calls for more scrutiny of most impacting chemical‐product combinations, fully ensuring from a regulatory perspective consumer product safety for high‐end users and using protective measures for users.

The use of down-the-drain products and the resultant release of chemicals may lead to pressures on the freshwater environment. Ecotoxicological impact assessment is a commonly used approach to assess chemical products but is still influenced by several uncertainty and variability sources. As a result, the robustness and reliability of such assessments can be questioned. A comprehensive and systematic assessment of these sources is, therefore, needed to increase their utility and credibility. In this study, we present a method to systematically analyse the uncertainty and variability of the potential ecotoxicological impact (PEI) of chemicals using a portfolio of 54 shampoo products. We separately quantified the influence of the statistical uncertainty in the prediction of physicochemical properties and freshwater toxicity as predicted from Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) respectively, and of various sources of spatial and technological variability as well as variability in consumer habits via 2D Monte Carlo simulations. Overall, the variation in the PEIs of shampoo use was mainly the result of uncertainty due to the use of toxicity data from three species only. All uncertainty sources combined resulted in PEIs ranging on average over seven orders of magnitude (ratio of the 90th to the 10th percentile) so that an absolute quantification of the ecological risk would not be meaningful. In comparison, variation in shampoo composition was the most influential source of variability, although less than compared to uncertainty, leading to PEIs ranging over three orders of magnitude. Increasing the number of toxicity data by increasing the number of species, either through additional measurements or ecotoxicological modelling (e.g. using Interspecies Correlation Equations), should get priority to improve the reliability of PEIs. These conclusions are not limited to shampoos but are applicable more generally to the down-the-drain products since they all have similar data limitations and associated uncertainties relating to the availability of ecotoxicity data and variability in consumer habits and use. Keywords: Ecotoxicity, Shampoo, Environmental impact, Monte Carlo, QSAR, Parameter uncertainty

Streambeds are critical hydrological interfaces: their physical properties regulate the rate, timing, and location of fluxes between aquifers and streams. Streambed vertical hydraulic conductivity (Kv) is a key parameter in watershed models, so understanding its spatial variability and uncertainty is essential to accurately predicting how stresses and environmental signals propagate through the hydrologic system. Most distributed modeling studies use generalized Kv estimates from column experiments or grain-size distribution, but Kv may include a wide range of orders of magnitude for a given particle size group. Thus, precisely predicting Kv spatially has remained conceptual, experimental, and/or poorly constrained. This usually leads to increased uncertainty in modeling results. There is a need to shift focus from scaling up pore-scale column experiments to watershed dimensions by proposing a new kind of approach that can apply to a whole watershed while incorporating spatial variability of complex hydrological processes. Here we present a new approach, Multi-Stemmed Nested Funnel (MSNF), to develop pedo-transfer functions (PTFs) capable of simulating the effects of complex sediment routing on Kv variability across multiple stream orders in Frenchman Creek watershed, USA. We find that using the product of Kv and drainage area as a response variable reduces the fuzziness in selecting the “best” PTF. We propose that the PTF can be used in predicting the ranges of Kv values across multiple stream orders.

Route topography is an important boundary condition for the regulated real driving emission (RDE) test. However, accurately and comprehensively evaluating the influence of route topography on the RDE test is difficult, because the effect cannot be easily separated from those of other test boundaries. We selected two light-duty gasoline vehicles to complete two rounds of RDE tests on four different test routes, and conducted the correlation analysis between pollutant emissions and route topography quantified by the cumulative positive altitude gains of the test routes based on the moving averaging window method. Since the small number of sample data at the total trip and road section level were not sufficiently representative of the population, we proposed to use the pollutant emission data of the data windows to analyze the complex coupling effect of the cumulative positive altitude gains and trip dynamic parameters of v·apos[95] on the RDE tests. At data window level, thousands of data windows were treated as the road section subsets of the RDE test, and the sample space of road section emission data was expanded by several orders of magnitude. With the help of the large data sample space, the influence mechanism of the random test boundaries on the RDE tests was demonstrated.

Micromechanical photoswitches (MPs) have shown orders of magnitude lower standby power consumption compared to their solid-state technology-based counterparts. In this paper, we demonstrate that the infrared detection threshold (i.e., the lowest detectable power of incident infrared radiation) of this new type of photoswitch can be effectively decreased by applying a bias voltage across the switch without compromising its key feature of near-zero standby power consumption and low false-alarm rate. We show that by exploiting the electrostatic pull-in effect (using bias voltage of ~7 V), a threshold as low as 76 nW can be achieved, which is the lowest threshold demonstrated to date for MPs. A mathematical model predicting the device threshold under a bias voltage is derived and verified by experiments. Furthermore, the effects of the bias on the device’s stability are carefully studied suggesting the existence of a tradeoff between the detection threshold and the false-alarm rate when the bias is higher than 85% of the pull-in voltage. The source of the instability that causes false alarms is found to originate from charge accumulation over time in the dielectric substrate surrounding the bottom contact, which we believe can be mitigated by an improved contact design. Another effect associated with high bias is the contact stiction after infrared-triggered pull-in. To address this, a microheater is integrated into the switch to reopen the contacts while consuming only $\sim 0.5~\mu \text{J}$ per operation. This paper thus presents an effective solution for post-fabrication threshold adjustment of MPs, which is useful for applications requiring variable thresholds.

Molecular conductivity is the quantum flow of electrons through a molecule. Since its conception by Aviram and Ratner, molecular conductivity has been realized experimentally in molecules and molecular-scale circuits. Significant challenges, however, remain for its prediction with popular theoretical methods often overpredicting conductance by as much as an order of magnitude. Here we report a current-constrained, electronic structure-based variational principle for molecular conductivity. Unlike existing theories, which set the voltage to compute the current, the current-constrained variational principle determines the voltage from an electronic structure calculation in which the current is added as a constraint. We apply the variational principle to benezenedithiol with gold and nickel leads where it matches experimental values and trends, improving upon previous theory by as much as 1–2 orders of magnitude. The current constraint produces a conducting steady state that includes all many-body effects treatable by the electronic structure calculation. Molecular conductivity, the quantum flow of electrons through a molecule, is typically overpredicted by theoretical methods to date. Here, the authors report a current-constrained, electronic structure-based method improving on existing techniques for calculating conductance by up to two orders of magnitude.

Mining high utility itemsets is considered to be one of the important and challenging problems in the data mining literature. The problem offers greater flexibility to a decision maker in using item utilities such as profits and margins to mine interesting and actionable patterns from databases. Most of the current works in the literature, however, apply a single minimum utility threshold value and fail to consider disparities in item characteristics. This paper proposes an efficient method (MHUI) to mine high utility itemsets with multiple minimum utility threshold values. The presented method generates high utility itemsets in a single phase without an expensive intermediate candidate generation process. It introduces the concept of suffix minimum utility and presents generalized pruning strategies for efficiently mining high utility itemsets. The performance of the algorithm is evaluated against the state-of-the-art methods (HUI-MMU-TE and HIMU-EUCP) on eight benchmark datasets. The experimental results show that the proposed method delivers two to three orders of magnitude execution time improvement over the HUI-MMU-TE method. In addition, MHUI delivers one to two orders of magnitude execution time improvement over the HIMU-EUCP method, especially on moderately long and dense benchmark datasets. The memory requirements of the proposed algorithm was also found to be significantly lower.

Depending on the level of electrification ([PH]EV, [M]HEV) and cell format (pouch, prismatic, cylindrical), the number of individual cells in a vehicle's battery pack can span several orders of magnitude (from tens to thousands). In this paper, we develop a novel analytical framework to investigate the impact of cell-level manufacturing variability on pack performance. Statistical distributions of pack energy and pack power are derived for any NsMp pack configurations and any level of cell-to-cell variability. These distributions are used to develop vehicle-dependent cell-level manufacturing requirements. The degree to which the series direction negatively affects pack statistics, and the degree to which the parallel direction improves pack statistics, is first quantified under a random sampling scenario. PHEV packs (96s1p) are found to have the highest pack-level statistical penalty, while EV packs made of small-format cylindrical cells (96s74p) incur virtually no statistical penalty. Pack-level statistics for Ns1p packs are shown to greatly benefit from the clustering of low-performing cells. Cell binning strategies for NsMp packs present both advantages and disadvantages. The impacts of cell aging and pack-level voltage limits are further discussed. The results of this study apply even in the presence of cell balancing and under partial SOC usage.

Proton-induced degradation and catastrophic failures in 650 V SiC diode and 900 V SiC MOSFET under different bias voltage and different proton energy are investigated. The experimental results show that when the bias voltage of SiC diode and MOSFET reaches 88% of rated voltage, 20 MeV protons will not cause the single event burnout (SEB) of two SiC devices. When SiC devices are burned out, a wave-shaped pulse current appears, the pulse current peak value gradually decreases, and the total pulse current width is within 5 microseconds. With the increase of bias voltage, the SEB fluence of SiC device is significantly reduced. After SiC diode is burned out, its reverse breakdown voltage is lower than 1 V, and the forward current when device has not been turned on is up to 8 orders of magnitude higher than before irradiation; the reverse breakdown voltage of SiC MOSFET is also lower than 1 V after burning out, only some SiC MOSFETs still maintain transfer characteristics under a voltage bias of 700 V, and the gate-source current has increased by 7–8 orders of magnitude compared to before irradiation. The Monte Carlo simulation results show that the linear energy transfer (LET) of 20 MeV proton in the epitaxial layer is lower than 2 MeV·cm2/mg, which is not enough to generate charge required for SEB. And the randomness of interaction between protons and device materials leads to different SEB fluences for SiC devices under different proton energies.

This paper presents a joint spectrum and energy efficiency maximization mechanism for device to device communication enabled wireless networks. Most approaches in the literature fail to take into account link heterogeneity in terms of their demands and the traffic they inject into the network. This paper presents a linear optimization formulation to allocate bandwidth to links, not only based on network topology but also based on traffic injection patterns of devices and link demands. We derive bounds on the achievable spectrum efficiency. The achieved spectrum efficiency and energy efficiency are further enhanced by deploying power control using $ {\mathcal {M}-}$ matrix theory. The proposed approach is shown to provide an improvement between 25% to up to four orders of magnitude in spectrum efficiency and between 25% to up to five orders of magnitude in energy efficiency compared to existing mechanisms, depending on the network topology and traffic injection patterns of the nodes.

We present new algorithms to synthesize exact universal reversible gate library for various types of gates and costs. We use the powerful algebraic software GAP for implementation and examination of our algorithms and the reversible logic synthesis problems have been reduced to group theory problems. It is shown that minimization of arbitrary cost functions of gates and orders of magnitude are faster than its previously counterparts for reversible logic synthesis. Experimental results show that a significant improvement over the previously proposed synthesis algorithm is obtained compared with the existing approaches to reversible logic synthesis.

High-velocity microparticle impacts are relevant to many fields from space exploration to additive manufacturing and can be used to help understand the physical and chemical behaviors of materials under extreme dynamic conditions. Recent advances in experimental techniques for single microparticle impacts have allowed fundamental investigations of dynamical responses of wide-ranging samples including soft materials, nano-composites, and metals, under strain rates up to 108 s-1. Here we review experimental methods for high-velocity impacts spanning 15 orders of magnitude in projectile mass and compare method performances. This review aims to present a comprehensive overview of high-velocity microparticle impact techniques to provide a reference for researchers in different materials testing fields and facilitate experimental design in dynamic testing for a wide range of impactor sizes, geometries, and velocities. Next, we review recent studies using the laser-induced particle impact test platform comprising target, projectile, and synergistic target-particle impact response, hence demonstrating the versatility of the method with applications in impact protection and additive manufacturing. We conclude by presenting the future perspectives in the field of high-velocity impact.

Low-density parity-check (LDPC) codes have been widely adopted in NAND flash in recent years to improve data reliability. However, their error-correction capability degrades due to inaccurate log-likelihood ratio (LLR). To improve LLR accuracy of LDPC decoding, this paper proposes a threshold voltage distribution aware LLR optimization scheme, called VaLLR. Firstly, we build a threshold voltage distribution model for 3D triple-level cell (TLC) NAND flash. Then, by exploiting the model, we introduce the VaLLR scheme to quantize LLR during soft-decision decoding. And by amplifying a portion of small LLRs, which is essential in the layer minsum decoder, more precise LLR can be obtained. Finally, we study the influence of the reference voltage arrangement on LLR calculation and apply the VaLLR scheme during decoding. The simulation shows that the proposed approach can improve the FER performance for several orders of magnitude.

Future observatories capable of detecting Earth-like planets around other stars will have to be large and exquisitely stable, a so-called ultra-stable system (USS). The stability requirements for a USS are orders of magnitude greater stability than current systems. The analysis, design and verification of these systems will require detailed knowledge of all the factors that affect its performance. At performance levels measured in picometers, forces and effects that are negligible for current systems can become important actors. To assure there are no bad actors waiting in the wings to spoil the performance of a future ultra-stable system, a systematic and comprehensive study has been carried out to assess the impact to system performance for many of these small and typically ignored effects. The work presented includes the effects of system self-interactions and system interaction with the environment and the impact of both on the stability of the figure in a spatial and temporal sense.

Electroporation uses high electric field gradients to create pores within the membrane of living cells in order to deliver a substance, for example a gene, into the cytoplasm. To achieve such gradients, current electroporation devices deliver voltage pulses in the kV range to the cell medium. We describe here a new device based on gold-microtube membranes that can accomplish electroporation with voltage pulses that are orders of magnitude smaller, 4 V. The percentages of electroporated bacteria were found to be more than an order of magnitude higher than obtained with a commercial electroporator.

We present a novel high-dynamic optical network control architecture based on spatiotemporal synchronization. By introducing high-precision time synchronization, control coordination among nodes and controllers can be established to improve the network dynamic performance by orders of magnitude.

Cosmic radiation phenomena such as Solar Particle Events cause high radiation flux lasting from hours to days, thus increasing the probability of Single-Event Upsets (SEUs) for several orders of magnitude. In space applications it is necessary, therefore, to monitor the SEU rate in order to ensure timely detection of high radiation levels and efficient protection of radiation-sensitive circuits. This work proposes an approach combining the SEU monitoring and data storage functions in the same on-chip Static Random Access Memory (SRAM) module, with negligible cost and overheads compared to traditional stand-alone SEU monitors. Furthermore, it also enables the detection of permanent faults in SRAM. The proposed monitor is intended to be further integrated into a highly dependable and self-adaptive multiprocessing platform in which it will drive the selection of the multiprocessor operating modes. Thus, a dynamic trade-off between reliability, performance and power consumption in real-time can be achieved.

Lebesgue-sampling-based prognostic algorithms have demonstrated computational advantages and execution time savings of several orders of magnitude over Riemmanbased prognostic methods. In this regard, some remarkable achievements have been obtained when using Lebesgue-sampling(LS) methods to build empirical degradation models in terms of statistics of the Time-of-Failure distribution, such as the Just-in-Time Point (JITP). This article studies some theoretical issues related to the implementation of LS-based prognostic methods, and analyzes the impact associated with the utilization of alternative procedures for the computation of Time-of-Failure probability density function statistics.

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. Both diagnosis and prognosis are complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we will present the application of a proof-of-concept methodology to generate big data training datasets for intercalation batteries. We will use a mechanistic approach that combines both modeling and experimental techniques to provide a universal tool for the creation of synthetic voltage curves practically indistinguishable from real data. This approach offers the benefits of the broad applicability of the model to various cell chemistries, designs, and operating modes, as well as the high fidelity. The methodology could be applied to the generation of training datasets encompassing the entire degradation spectrum as well as different operating conditions such as rate and temperature. Scenarios leading to lithium plating with adjustable reversibility can also be added and the addition of random noise on the data could be considered to make the datasets even more realistic and consider the impact of real-life testing machines' precision and accuracy The public proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data. The synthetic datasets will cover the entire spectrum of possible degradations and degradation paths for several battery chemistries. These complete datasets are unprecedented and they will be used to identify meaningful learning parameter by comparing and evaluating different features of interest on the voltage curves to understand the relationship between data and model. For example, early prognosis conditions will be tested to establish whether a single point early prognosis is possible.

International audience; Liquid Chromatography-Mass Spectrometry (LC/MS) provides large datasets from which one needs to extract the relevant information. Since these data are made of non-negative mixtures of non-negative mass spectra, non-negative matrix factorization (NMF) is well suited for its processing, but it has barely been used in LC/MS. Also, these data are very difficult to deal with since they are usually contaminated with non-Gaussian noise and the intensities vary on several orders of magnitude. In this article, we show the feasibility of the NMF approach on these data. We also propose an adaptation of one of the algorithms aiming at specifically dealing with LC/MS data. We finally perform experiments and compare standard NMF algorithms on both simulated data and an annotated LC/MS dataset. This lets us evaluate the influence of the noise model and the data model on the recovery of the sources.

Aim: Experimental variation in dose–response data of drugs tested on cell lines result in inaccuracies in the estimate of a key drug sensitivity characteristic: the IC50. We aim to improve the precision of the half-limiting dose (IC50) estimates by simultaneously employing all dose–responses across all cell lines and drugs, rather than using a single drug–cell line response. Materials & methods: We propose a multilevel mixed effects model that takes advantage of all available dose–response data. Results: The new estimates are highly concordant with the currently used Bayesian model when the data are well behaved. Otherwise, the multilevel model is clearly superior. Conclusion: The multilevel model yields a significant reduction of extreme IC50 estimates, an increase in precision and it runs orders of magnitude faster.

The trigger systems of the LHC detectors play a crucial role in determining the physics capabilities of the experiments. A reduction of several orders of magnitude of the event rate is needed to reach values compatible with detector readout, offline storage and analysis capability. The CMS experiment has been designed with a two-level trigger system: the Level-1 Trigger (L1), implemented on custom-designed electronics, and the High Level Trigger (HLT), a streamlined version of the CMS offline reconstruction software running on a computer farm. Both systems need to provide an efficient and fast selection of events, to keep the average write-out rate below 1 kHz. For Run II, the doubling of both the center of mass energy to 13 TeV and the collision rate to 40 MHz, will imply increased cross sections and out-of-time pile-up. We will present the improvements brought to both L1 and HLT strategies to meet those new challenges.

This Perspectives paper is on the ever-interesting topic of energy-efficient computing. It shows us that future goals are orders of magnitude higher energy efficiency (measured in Joule per operation) and a means to achieve that is operating at near-threshold voltage.

This paper demonstrates an approach to identify optimal monitoring designs that minimize the time to first detection of CO2 leakage from a subsurface storage formation. This research is part of the National Risk Assessment Partnership (NRAP), a DOE project tasked with conducting risk and uncertainty analysis in the areas of reservoir performance, natural leakage pathways, wellbore integrity, groundwater protection, monitoring, and systems level modeling. Our approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating potential monitoring designs. A user-defined leakage signature based on change from initial conditions is implemented to infer CO2 leakage had occurred. An example application was performed to demonstrate the effectiveness and efficiency of this method compared to an exhaustive search of the entire solution space. We accounted for uncertainty in the example by evaluating the performance of potential monitoring designs across a set of simulated leakage realizations. The example application approached the optimal time to leakage detection in a few minutes using a standard workstation, which was several orders of magnitude faster than an exhaustive search of the solution space, thus exhibiting efficiency and effectiveness. A user-friendly tool, DREAM (Designs for Risk Evaluation and Management), is being developed for use on personal computers to make this method accessible to stakeholders, regulators, and researchers.