True Zero Research Articles

Feasibility analysis of solar electric vehicles

Feasibility analysis of solar electric vehicles

Analysis of environmentally friendly commercial aviation development ways

The goal of this study is to identify effective directions to decrease the greenhouse gas emissions of commercial aviation. The subject matter of this research is to analyze ways known from literature to decrease the greenhouse gas emissions of commercial aviation: continued evolution (which includes a lot of various methods for partial reduction of greenhouse gas emissions with decreasing in fuel consumption), «net zero» (which includes the following methods: offsets and sustainable aviation fuel utilization), electric hybrid power plants (parallel, series, series/parallel, turboelectric, and partial turboelectric), «zero carbon» (replacing kerosene combustion with hydrogen combustion in modified gas-turbine engines), «true zero» (transition to electric cruise motors with hydrogen fuel cells or electric batteries). The tasks to be solved are as follows: learning of the ways and detection of advantages and problems from the point of view of efficiency, technical complexity, economy, ecology, and implementation possibility in conditions of limited funding. The methods used are: search of the corresponding information sources in the Internet and their analysis on the basis of operational experience in the aviation branch. The following results were obtained: in terms of found information sources, data about existing greenhouse gas emissions and their predictive estimations, history of international agreement development as for greenhouse gas emission reduction were briefly stated; actuality of this problem with a view to mitigate the environmental impact was stressed; and the advantages and problems, which should be solved to implement each of the considered ways, were summarized. Conclusions. The scientific novelty of the results obtained is as follows: 1) information from numerous sources of literature that clarifies classification, the advantages, and the problems that should be overcome for each ways implementation, was summed up in the review article; 2) additional inherent disadvantages, which are integral to some of the ways (low efficiency, high technical complexity, schedule delays, cost overruns, funding instability, doubts in their reasonability from ecological considerations), are shown as a result of analysis and historical analogy. The direction of the following research in this field is outlined.

Zero-truncated Poisson regression for sparse multiway count data corrupted by false zeros

Abstract We propose a novel statistical inference methodology for multiway count data that is corrupted by false zeros that are indistinguishable from true zero counts. Our approach consists of zero-truncating the Poisson distribution to neglect all zero values. This simple truncated approach dispenses with the need to distinguish between true and false zero counts and reduces the amount of data to be processed. Inference is accomplished via tensor completion that imposes low-rank tensor structure on the Poisson parameter space. Our main result shows that an $N$-way rank-$R$ parametric tensor $\boldsymbol{\mathscr{M}}\in (0,\infty )^{I\times \cdots \times I}$ generating Poisson observations can be accurately estimated by zero-truncated Poisson regression from approximately $IR^2\log _2^2(I)$ non-zero counts under the nonnegative canonical polyadic decomposition. Our result also quantifies the error made by zero-truncating the Poisson distribution when the parameter is uniformly bounded from below. Therefore, under a low-rank multiparameter model, we propose an implementable approach guaranteed to achieve accurate regression in under-determined scenarios with substantial corruption by false zeros. Several numerical experiments are presented to explore the theoretical results.

Bayesian inference of heavy metals exposure in crayfish for assessing human non-carcinogenic health risk.

Contaminant concentrations often presented left censorship that below the limit of detection (LOD), which may contains true zero values because of no residue. In this study, we analyzed the concentrations of lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), and chromium (Cr) in 391 crayfish samples collected from 24 provinces of China, modelled the concentrations with two different models in a Bayesian framework, and evaluated health risk by estimating the target hazard quotient (THQ). The highest concentration was associated with Cr in Northwest China (0.1753 ± 0.1520 mg/kg w. w.), and the minimum concentration was Cd in Southwest China (0.0052 ± 0.0144 mg/kg w. w.), all heavy metal concentrations were below their safety limits. The posterior means of not detect rates P0 of Pb, Cd, Hg, As, and Cr obtained with two models were both nearly equal to the observed not detect rates (51.15%, 36.83%, 27.37%, 64.71%, and 43.73%, respectively). The posterior probabilistic density lines for Pb, Hg, and Cd concentrations obtained with two models were similar, and fitted the empirical distributions well. The posterior density lines of THQs showed that the non-carcinogenic risk of As and Hg were significant high. Moreover, Bayesian approach presented a better understanding of the percentage of population exposed to potential risk.

Estimation of high-dimensional vector autoregression via sparse precision matrix

Summary We consider the problem of estimating sparse vector autoregression (VAR) via penalized precision matrices. This matrix is the output of the underlying directed acyclic graph of the VAR process, whose zero components correspond to the zero coefficients of the graphical representation of the VAR. The sparsity-based precision matrix estimator is deduced from the D-trace loss with convex and nonconvex penalty functions. We establish the consistency of the penalized estimator and provide the conditions for which all true zero entries of the precision matrix are actually estimated as zero with probability tending to one. The relevance of the method is supported by simulated experiments and a real data application.

Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations

This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in the context of topic models. The data consists of observed multinomial counts of p words across n independent documents. In topic models, the p×n expected word frequency matrix is assumed to be factorized as a p×K word-topic matrix A and a K×n topic-document matrix T. Since columns of both matrices represent conditional probabilities belonging to probability simplices, columns of A are viewed as p-dimensional mixture components that are common to all documents while columns of T are viewed as the K-dimensional mixture weights that are document specific and are allowed to be sparse. The main interest is to provide sharp, finite sample, ℓ1-norm convergence rates for estimators of the mixture weights T when A is either known or unknown. For known A, we suggest MLE estimation of T. Our nonstandard analysis of the MLE not only establishes its ℓ1 convergence rate, but also reveals a remarkable property: the MLE, with no extra regularization, can be exactly sparse and contain the true zero pattern of T. We further show that the MLE is both minimax optimal and adaptive to the unknown sparsity in a large class of sparse topic distributions. When A is unknown, we estimate T by optimizing the likelihood function corresponding to a plug in, generic, estimator Aˆ of A. For any estimator Aˆ that satisfies carefully detailed conditions for proximity to A, we show that the resulting estimator of T retains the properties established for the MLE. Our theoretical results allow the ambient dimensions K and p to grow with the sample sizes. Our main application is to the estimation of 1-Wasserstein distances between document generating distributions. We propose, estimate and analyze new 1-Wasserstein distances between alternative probabilistic document representations, at the word and topic level, respectively. We derive finite sample bounds on the estimated proposed 1-Wasserstein distances. For word level document-distances, we provide contrast with existing rates on the 1-Wasserstein distance between standard empirical frequency estimates. The effectiveness of the proposed 1-Wasserstein distances is illustrated by an analysis of an IMDB movie reviews data set. Finally, our theoretical results are supported by extensive simulation studies.

A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance

After new drugs enter the market, adverse events (AE) induced by their use must be tracked; rare AEs may not be detected during clinical trials. Some organizations have been collecting information on suspected drugs and AEs via a spontaneous reporting system to conduct post-market drug safety surveillance. These organizations use the information to detect a signal representing potential causality between drugs and AEs. The drug and AE data are often hierarchically structured. Accordingly, the tree-based scan statistic can be used as a statistical data mining method for signal detection. Most of the AE databases contain a large number of zero-count cells. Notably, not only an observational zero from the Poisson distribution, but also a true zero exists in zero-count cells. True zeros represent theoretically impossible observations or possible but unreported observations. The existing tree-based scan statistic assumes that all zeros are zero-valued observations from the Poisson distribution. Therefore, true zeros are not considered in the modeling, which can lead to bias in the inferences. In this study, we propose a tree-based scan statistic for zero-inflated count data in a hierarchical structure. According to our simulation study, in the presence of excess zeros, our proposed tree-based scan statistic provides better performance than the existing tree-based scan statistic. The two methods were illustrated using Korea Adverse Event Reporting System data from the Korea Institute of Drug Safety and Risk Management.

CcImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data

BackgroundIn recent years, the introduction of single-cell RNA sequencing (scRNA-seq) has enabled the analysis of a cell’s transcriptome at an unprecedented granularity and processing speed. The experimental outcome of applying this technology is a M times N matrix containing aggregated mRNA expression counts of M genes and N cell samples. From this matrix, scientists can study how cell protein synthesis changes in response to various factors, for example, disease versus non-disease states in response to a treatment protocol. This technology’s critical challenge is detecting and accurately recording lowly expressed genes. As a result, low expression levels tend to be missed and recorded as zero - an event known as dropout. This makes the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult.ResultsTo address this problem, we propose an approach to measure cell similarity using consensus clustering and demonstrate an effective and efficient algorithm that takes advantage of this new similarity measure to impute the most probable dropout events in the scRNA-seq datasets. We demonstrate that our approach exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.ConclusionsccImpute is an effective algorithm to correct for dropout events and thus improve downstream analysis of scRNA-seq data. ccImpute is implemented in R and is available at https://github.com/khazum/ccImpute.

Information, Experience, and Willingness to Mitigate Mental Health Consequences From Flooding Through Collective Defence

AbstractDemand for reducing mental health impacts from flooding through collective flood defence is elicited using a contingent valuation method with a sequential hypothetical scenario, which accounts for human resilience and experience. A two‐step model fits the survey data: it combines a binary sample selection rule to distinguish protesters and participants with a Tobit model to accommodate true zero responses among participants. Results show that non‐symptoms‐specific information on mental health risk may bias the willingness to pay downward. Risk‐averse individuals who have taken self‐insurance protection measures are willing to pay for additional protection through collective defence. Feelings, such as worries and anxiety related to flooding, drive the demand, which supports the risk‐as‐feelings hypothesis for mental health protection from flooding. Inexperience rather than experience of flooding is found to increase demand, which indicates that individual mental resilience to flooding may increase after an event as posited by the inoculation hypothesis.

Bayesian computation for the common coefficient of variation of delta-lognormal distributions with application to common rainfall dispersion in Thailand.

Rainfall fluctuation makes precipitation and flood prediction difficult. The coefficient of variation can be used to measure rainfall dispersion to produce information for predicting future rainfall, thereby mitigating future disasters. Rainfall data usually consist of positive and true zero values that correspond to a delta-lognormal distribution. Therefore, the coefficient of variation of delta-lognormal distribution is appropriate to measure the rainfall dispersion more than lognormal distribution. In particular, the measurement of the dispersion of precipitation from several areas can be determined by measuring the common coefficient of variation in the rainfall from those areas together. Herein, we compose confidence intervals for the common coefficient of variation of delta-lognormal distributions by employing the fiducial generalized confidence interval, equal-tailed Bayesian credible intervals incorporating the independent Jeffreys or uniform priors, and the method of variance estimates recovery. A combination of the coverage probabilities and expected lengths of the proposed methods obtained via a Monte Carlo simulation study were used to compare their performances. The results show that the equal-tailed Bayesian based on the independent Jeffreys prior was suitable. In addition, it can be used the equal-tailed Bayesian based on the uniform prior as an alternative. The efficacies of the proposed confidence intervals are demonstrated via applying them to analyze daily rainfall datasets from Nan, Thailand.

Toward a zero trust metric

Zero trust assumes that all points of trust will be questioned and mitigated, that the individual resources are protected, and that there is no reliance on the network for protection. This helps to limit threat mobility and contain damage. Rules for multifactor authentication and micro-segmentation are often cited as a Zero Trust Architecture (ZTA), but these so-called architectures lack guidelines for the major points of trust in the system. True zero trust is not achievable—only minimal trust can be achieved. Certain trust points are inevitable, such as certificate authorities, policy evaluation, and decision points. There are no metrics measuring whether or not zero trust objectives have been met. It is the goal of this paper to move toward a general metric of trust.

How Much Will You Pay to Use Open Data?: Evidence from the Seoul Metropolitan Government

In line with the emerging data economy, governments and businesses are exploring how to drive value through data usage. As a part of this notable trend, open government data (OGD), through which a variety of public information is available for free use at online websites, has been shaping new opportunities for nations, society, and enterprises. While researchers and policymakers have focused on how and what to provide for better practices in innovative digital public service, the actual value created from the data offering has received scant attention. This study fills the gap by measuring the monetary value of the OGD service as a stated willingness to pay (WTP). To do this, we collaborated with the Seoul Metropolitan Government, which operates one of the largest and most active online OGD portals in the world, offering over 20 million documents and other forms of information. We consider three stakeholder groups—people using OGD, people not using OGD, and city officials—to compare their assessments of emerging public digital goods. Our estimates suggest that people using OGD are willing to pay $0.8 a month for the service, on average, in the most conservative approach; however, this value is not significantly different from that of people not using OGD ($0.6). Instead, we find that a higher proportion of people using OGD refuse to pay any amount for the service than people not using OGD, whereas a higher proportion of city officials give true zero responses than people not using OGD. We discuss the merits and limitations of quantifying intangible digitized public goods and propose future directions.

Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)

We present a novel technique for sparse principal component analysis. This method, named Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix from the eigenvalues of the full matrix and associated sub-matrices. We explore two versions of the EESPCA method: a version that uses a fixed threshold for inducing sparsity and a version that selects the threshold via cross-validation. Relative to the state-of-the-art sparse PCA methods of Witten et al., Yuan & Zhang and Tan et al., the fixed threshold EESPCA technique offers an order-of-magnitude improvement in computational speed, does not require estimation of tuning parameters via cross-validation, and can more accurately identify true zero principal component loadings across a range of data matrix sizes and covariance structures. Importantly, the EESPCA method achieves these benefits while maintaining out-of-sample reconstruction error and PC estimation error close to the lowest error generated by all evaluated approaches. EESPCA is a practical and effective technique for sparse PCA with particular relevance to computationally demanding statistical problems such as the analysis of high-dimensional data sets or application of statistical techniques like resampling that involve the repeated calculation of sparse PCs.

Confidence Intervals for the Coefficient of Quartile Variation of a Zero-inflated Lognormal Distribution

There are many types of skewed distribution, one of which is the lognormal distribution that is positively skewed and may contain true zero values. The coefficient of quartile variation is a statistical tool used to measure the dispersion of skewed and kurtosis data. The purpose of this study is to establish confidence and credible intervals for the coefficient of quartile variation of a zero-inflated lognormal distribution. The proposed approaches are based on the concepts of the fiducial generalized confidence interval, and the Bayesian method. Coverage probabilities and expected lengths were used to evaluate the performance of the proposed approaches via Monte Carlo simulation. The results of the simulation studies show that the fiducial generalized confidence interval and the Bayesian based on uniform and normal inverse Chi-squared priors were appropriate in terms of the coverage probability and expected length, while the Bayesian approach based on Jeffreys' rule prior can be used as alternatives. In addition, real data based on the red cod density from a trawl survey in New Zealand is used to illustrate the performances of the proposed approaches. Doi: 10.28991/esj-2021-01289 Full Text: PDF

In-use energy and carbon performance of a true zero carbon housing development in England

This paper systematically examines the in-use energy and carbon performance of a large case study housing development in England, designed to be net true zero carbon. Remote monitoring during a one-year period was used to gather high-frequency data on dwelling heat use, grid electricity use, solar PV electricity generation and export and community heating system performance. Based on data from 74 dwellings, mean energy use of 76 kWh/m2/year and electricity use of 27.4 kWh/m2/year per dwelling placed the case study among the lowest energy housing developments in the UK. Nonetheless, heat usage and designed fabric efficiency fell short compared to other true zero carbon housing. The mean self-consumption rate of generated energy of 23% calls for the provision of home batteries. Heat usage variance was more prominent compared to findings in other studies. Based on the 2018/19 carbon factors, dwellings emitted 20.2 kgCO2e/m2/year on average, missing the zero carbon target. As found in other studies, this was attributed to the underperforming community heating system. This study comes timely in the context of the widespread calls for net zero carbon dwellings. The findings confirm the argument that the anticipated mainstreaming of zero carbon dwellings demands shifting toward an outcome-focused design approach.

Synthetic Fuels Based on Dimethyl Ether as a Future Non-Fossil Fuel for Road Transport From Sustainable Feedstocks

In this review we consider the important future of the synthetic fuel, dimethyl ether (DME). We compare DME to two alternatives [oxymethylene ether (OMEx) and synthetic diesel through Fischer-Tropsch (FT) reactions]. Finally, we explore a range of methodologies and processes for the synthesis of DME.DME is an alternative diesel fuel for use in compression ignition (CI) engines and may be produced from a range of waste feedstocks, thereby avoiding new fossil carbon from entering the supply chain. DME is characterised by low CO2, low NOx and low particulate matter (PM) emissions. Its high cetane number means it can be used in CI engines with minimal modifications. The key to creating a circular fuels economy is integrating multiple waste streams into an economically and environmentally sustainable supply chain. Therefore, we also consider the availability and nature of low-carbon fuels and hydrogen production. Reliable carbon dioxide sources are also essential if CO2 utilisation processes are to become commercially viable. The location of DME plants will depend on the local ecosystems and ideally should be co-located on or near waste emitters and low-carbon energy sources. Alternative liquid fuels are considered interesting in the medium term, while renewable electricity and hydrogen are considered as reliable long-term solutions for the future transport sector. DME may be considered as a circular hydrogen carrier which will also be able to store energy for use at times of low renewable power generation.The chemistry of the individual steps within the supply chain is generally well known and usually relies on the use of cheap and Earth-abundant metal catalysts. The thermodynamics of these processes are also well-characterised. So overcoming the challenge now relies on the expertise of chemical engineers to put the fundamentals into commercial practice. It is important that a whole systems approach is adopted as interventions can have detrimental unintended consequences unless close monitoring is applied. This review shows that while DME production has been achieved and shows great promise, there is considerable effort needed if we are to reach true net zero emissions in the transport sector, particularly long-haul road use, in the require timescales.

Peter Rabbit is a Badger in Disguise: Deconstructing the Belief System of the Institute for Clinical and Economic Review in Health Technology Assessment.

The Institute for Clinical and Economic Review (ICER), a Boston-based consulting group, has seen itself as the lead organization in the US for evaluating pharmaceuticals and, at product launch, making recommendations for pricing and access. Previous commentaries in Innovations in Pharmacy have made the case that the ICER analytical framework is nonsensical. It abandons the standards of normal science in favor of inventing evidence through unsupported assertions regarding measurement properties and lifetime assumption driven simulations. It has been labeled pseudoscience. Yet ICER persists in its belief that all preference scales have ratio properties. ICER believes it can disregard these standards, notably in respect of the axioms of fundamental evidence, and continue its technology assessment activities. Challenging a belief system is not undertaken lightly, although in the case of ICER the belief system is built on such shaky foundations that the effort seems almost superfluous. This deeply held belief, shared apparently by the majority of health economists according to ICER, that all preference scores have ratio properties with a true zero, is easily overturned: if it has ratio properties how is it that preferences scores have been known for over 30 years to recognize health states worse than death? In other words, they can have negative preferences. Recognizing this manifest contradiction is important because it brings into relief the wider belief system of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) who share the same mythical certainties. A key issue is one of cultural relativity: can we accept with equanimity the parallel existence of two belief systems in health technology assessment when one is clearly nonsense? The answer proposed here is clearly no; although unfortunately the blowback by ICER and ISPOR will ensure the survival at least in the near term of their unfortunate meme.

Simultaneous modeling of detection rate and exposure concentration using semi-continuous models to identify exposure determinants when left-censored data may be a true zero.

Background:Most methods for treating left-censored data assume the analyte is present but not quantified. Biased estimates may result if the analyte is absent such that the unobserved data represents a mixed exposure distribution with an unknown proportion clustered at zero.Objective:We used semi-continuous models to identify time and industry trends in 52,457 OSHA inspection lead sample results.Method:The first component of the semi-continuous model predicted the probability of detecting concentrations ≥0.007 mg/m3 (highest estimated detection limit, 62% of measurements). The second component predicted the median concentration of measurements ≥0.007 mg/m3. Both components included a random-effect for industry and fixed-effects for year, industry group, analytical method, and other variables. We used the two components together to predict median industry- and time-specific lead concentrations.Results:The probabilities of detectable concentrations and the median detected concentrations decreased with year; both were also lower for measurements analyzed for multiple (vs. one) metals and for those analyzed by inductively-coupled plasma (vs. atomic absorption spectroscopy). The covariance was 0.30 (standard error=0.06), confirming the two components were correlated.Significance:We identified determinants of exposure in data with over 60% left-censored, while accounting for correlated relationships and without assuming a distribution for the censored data.

Fundamental Measurement: The Need Fulfilment Quality of Life (N-QOL) Measure.

The quality adjusted life year (QALY) has serious problems related to its failure to adhere to measurement theory. If a QALY is to be meaningful, the utility score that translates time spent to an equivalent time spent in so-called perfect health must have ratio properties (i.e., it must support multiplication). Multiattribute utility scores (e.g. those generated by the EQ-5D-5L) fail to meet this standard. The multiattribute instruments produce ordinal scores that lack a true zero and they generate negative values. The manifest deficiencies of multiattribute utility instruments render them unfit, not only as a measure of therapy response but also in generating QALY claims. After 30 years of belief in their use, utilities and QALYs are clearly analytical dead ends. The purpose of this commentary is to demonstrate a coherent way forward in health technology assessment by focusing, not on clinical attributes as surrogates for quality of life, but on measures that are based on a conceptual model describing patient value in terms of need-fulfilment. Building on an extensive, yet often overlooked literature, need-based measures that fit Rasch Measurement Theory criteria are converted from ordinal scores to interval scores to evaluate response to therapy. These measures meet the requirements of single attribute fundamental measurement which is the standard in the physical sciences. It is proposed that a translation from a Rasch interval scale (defined by logits) can be transformed to a bounded ratio scale. Need based Quality of Life (N-QOL) scales bounded by 0 (where no needs are fulfilled) to 1 (where all needs are fulfilled) form such scales. The N-QOL supports the full range of arithmetic operations. Multiattribute utilities and mathematically invalid QALYs can be put to one side as unfortunate historical curiosities in favor of a disease or target population specific N-QOL scale. Such a scale has the required properties to evaluate disease specific response to therapy This can also support N-QOL adjusted life years with a need- fulfillment life year (NALY) metric with ratio properties.

Research Progress on Zero Discharge and Resource Utilization of Industrial High‐Salt Wastewater

AbstractWith the continuous expansion of industrial enterprises, a large amount of difficult‐to‐treat high‐salt wastewater with complex components is produced. If it is not treated properly and discharged directly, it can not only be a wasted resource but also cause great damage to the ecological environment. Therefore, how to achieve zero discharge and resource utilization, reduce the treatment cost, and the generation of secondary pollution have become the bottlenecks that need to be broken through in current wastewater treatment. Among many methods, the concentrated crystallization coupling technology has broad development and application prospects due to its advantages such as low energy consumption, green economy, and high purity of salt‐separated products. Combining with source and composition of high‐salt wastewater, the research progress of wastewater treatment technology is reviewed, and its actual application status is analyzed, to provide a certain reference for the further resource treatment of high‐salt wastewater and the realization of true zero discharge in the future.