Multi-dimensional sinusoidal order estimation using angles between subspaces

Multi-dimensional harmonic retrieval (HR) in white noise is required in numerous applications such as channel estimation in wireless communications and imaging in multiple-input multiple-output radar. In this paper, we propose two R-dimensional (R -D) extensions of the subspace-based MUSIC model order selection scheme, for R≥2R≥2, to detect the number of multi-dimensional cisoids. The key idea in the algorithm development is to utilize the principle angles between multilinear signal subspaces via the truncated higher-order singular value decomposition. The first method is designed for multiple-snapshot scenarios. It considerably outperforms existing algorithms in terms of both detection accuracy and identifiability particularly when a large number of snapshots are available. However, its computational cost is relatively quite high. The second method is computationally much simpler and performs almost as well as the first one when the number of snapshots is small. Simulation results are conducted to demonstrate the performance of the proposed estimators

Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning

Accurate and reliable forecasting of PM2.5 and PM10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM2.5 and PM10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial–temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM2.5 and PM10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial–temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1–4 h ahead predictions in three major cities of PM2.5 and PM10, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution

Statistical analysis of comparative experiments based on large strip on-farm trials

Statistical methods used for small plot analyses are unsuitable for large-scale on-farm experiments because they fail to take into account the spatial variability in treatment effects within paddocks. Several new methods have recently been proposed that are inspired by geostatistical analyses of spatially-varying treatment effects, which are typical for site-specific crop management trials with quantitative treatments. However, these methods do not address the objective of comparative experiments, where the overall assessment of treatments’ performance is of interest. Moreover, most biometricians, who routinely analyse data from field trials, are either unfamiliar with the new geostatistical techniques or reluctant to include these in their regular analytical toolkits due to the unavailability of easy-to-use software tools. The linear mixed model is widely used for analysing small plot field trials because it is extremely versatile in modelling spatial and extraneous variability and is accessible through user-friendly software implementation. Motivated by comparative experiments, conducted in large strip trials using qualitative treatment factors, and yield data obtained from harvest monitor, we propose a linear mixed effects model for determining the best treatment at both local and global spatial scales within a paddock, based on yield predictions and profit estimates. To account for the large spatial variation in on-farm strip trials, we divide the trial into smaller regions or pseudo-environments (PEs), each containing at least two replicates. We propose two approaches for creating these PEs. In the presence of appropriate spatial covariates, a clustering method is proposed; otherwise, the trial area is stratified into equal-sized rectangular blocks using a systematic partitioning scheme. PEs are used to estimate the treatment effects by incorporating treatment-by-PE interactions in linear mixed effects models. The optimum treatment within each PE is found by either comparing the best linear unbiased predictions solely or incorporating profit and comparing economic performance. To illustrate the applicability of our method, we have analysed two large strip trials conducted in Western Australia

An Extensible Framework for Sharing Clinical Guidelines and Services

Abstract — Accurate and descriptive information from clinical studies guides improvements in health care. Clinical guidelines established by authoritative medical organizations provide such information in a standard form for medical professionals’ reference. Previous work on electronically sharing clinical guidelines focuses on the idea of building unified clinical terminologies and sharing resources through centralized data repositories. In this paper we propose a novel five-layer framework called the Extensible Clinical Guidelines and Services Sharing Architecture (ECGSSA). This framework provides for clinical guideline sharing among autonomous service providers over a distributed architecture. Requests for exchange of guidelines are disseminated through Web Services through a registry mechanism. Currently we have adopted the Guideline Interchange Format (GLIF) from InterMed as the representation format and use the Open Grid Services Architecture (OGSA) to attain virtual organization of shared guideline and service resources. This approach will allow more flexibility for medical professionals to exchange their practice guidelines in an effort to improve quality of health care. Also, it extends the possibility of solving clinic-related computational problems through collaborative sharing of analytical services. A sample scenario is presented to explain the application of ECGSSA in distributed task assignment and service matching in medical image processing

Recent applications of biological technologies for decontaminating hormones in livestock waste and wastewater

Large quantities of natural and synthetic hormones contained in livestock waste and wastewater (LWW) can cause serious problems in our environment. Composting and anaerobic digestion cannot remove hormones efficiently, so they should be modified to enhance the treatment processes. In addition, constructed wetlands show decent rates for removal of hormones. Advanced technologies such as membrane biological reactors and microalgae-based systems efficiently eliminate hormones from LWW. However, more practical studies are needed to investigate their actual performances. The categories, degradation mechanisms, and enzymes of hormone-degrading microorganisms are presented, and related hormone-degrading microorganism-based technologies are introduced. Finally, composting, anaerobic digestion, constructed wetlands, membrane biological reactors, and microalgae-based systems are compared in terms of their applicability in LWW treatment.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Sanitary Engineerin

Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments

© 2020 With increasing interest in on-farm experiments, there is a pressing need to develop rigorous statistical methods for analysing these experiments. The adoption of advanced technologies such as yield monitors and variable-rate fertilizer applicators has enabled farmers and researchers to collect biophysical data linked to spatial information at a scale which allows them to investigate the role of spatial variability in the development of optimum management practices. A relevant topic for investigation could be: “what are the optimum rates of nitrogen and how/why do these differ across the field”? Although it has been recently understood that traditional statistical methods that are appropriate for analysing small-plot experiments are inappropriate for answering these questions, a unifying approach to inference for on-farm experiments is still missing and this limits the adoption of the technique. In this paper we propose a unifying approach to the analysis of on-farm strip experiments adapting the core ideas of local likelihood or geographically weighted regression. We propose a statistical model that allows spatial nonstationarity in modelled relationships and estimates spatially-varying parameters governing these relationships. A crucial step is bandwidth selection in implementing these models, and we develop bandwidth selection methods for two important scenarios relevant to the modelling of yield monitor data in on-farm experiments. Local t-scores have been introduced for inferential purposes and the associated problem of multiple testing has been described in the context of analysing on-farm experiments. We demonstrate in this paper how local p-values can be adjusted to overcome this problem. To illustrate the applicability of our proposed method, we analysed two publicly available datasets. Graphical displays are created to guide practitioners to make informed decisions on optimal management practices