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Sacrum located IMU sensors were used to monitor three-axis acceleration and three-axis rotation from elite swimmers in competition conditions. The intrastroke velocity was determined for each swimmer using their preferred swim stroke (freestyle, backstroke, and breaststroke) using three different calculation techniquesdual-axis acceleration, dual-axis acceleration eliminating the static gravity constant, and altitude and heading reference system. The mean intrastroke velocity variation (averaged over one 50-m lap) in freestyle swimming was less than 0.6 in all cases. This resulted in a timing under-estimate of less than 0.60ms for freestyle, 4.0ms for breaststroke, and a timing overestimate of less than 6.2ms for backstroke. The difference was less than 5 over the complete stroke (one way ANOVA p > 0.05), indicating no significant difference in the velocity profiles. These simple, robust analysis techniques can be used to quantify variations in every stroke as the swimmer fatigues, providing significant information to coaching staff and athletes.

This paper addresses the H2 robust analysis problem for linear time-invariant continuous and discrete-time systems. New linear matrix inequality conditions are proposed for H2 robust analysis in both cases. The proposed conditions stem from a Lyapunov function that considers an augmented state vector containing, for continuous-time systems, higher order state derivatives and, for discrete-time systems, higher order state shifts. The Lyapunov matrix depends on the uncertain system matrices and also on the polynomial structure imposed to a set of matrices used in the analysis problem. Examples borrowed from the literature illustrate the performance, in terms of scalar decision variables and LMI rows, of the proposed method when compared to other approaches.

MicroRNAs (miRNAs) have recently emerged as promising biomarkers for the profiling of diseases. Translation of miRNA biomarkers to clinical practice, however, remains a challenge due to the lack of analysis platforms for sensitive, quantitative, and multiplex miRNA assays that have simple and robust workflows suitable for translation. The platform we present here utilizes functionalized hydrogel posts contained within isolated nanoliter well reactors for quantitative and multiplex assays directly from unprocessed cell samples without the need of prior nucleic acid extraction. Simultaneous reactor isolation and delivery of miRNA extraction reagents is achieved by sealing an array of wells containing the functionalized hydrogel posts and cells against another array of wells containing lysis and extraction reagents. The nanoliter well array platform features >100x better sensitivity compared to previous technology utilizing hydrogel particles without relying on signal amplification and enables >100 parallel assays in a single device. These advances provided by this platform lay the groundwork for translatable and robust analysis technologies for miRNA expression profiling in samples with small populations of cells and in precious, material-limited samples.

The practice of correction and republication is a mechanism for identifying and updating non‐maleficent yet message‐distorting errors in the biomedical literature. Inappropriate use of anomalous literature is evinced by citation of invalidated scholarly works, and though it is known that republished versions of articles are cited more often than corrected versions, the strength of the effect of invalidation, correction, and republication has not been previously quantified. Robust analysis of 15,000+ citations to 548 articles indexed in PubMed indicates that the practice of correction and republication is a strong predictor of reduced post‐republication citation relative to controls. This bibliometric analysis shows that corrected articles are cited on average 51% less than controls overall and that the practice of correction and republication results in a fast‐acting and long‐lasting reduction in citation of flawed works by downstream researchers.

Systematic assessment of non-material benefits from healthy ecosystems is a critical complement to economic valuations in policy. Several frameworks of increasing nuance and complexity have been proposed to facilitate collection of data on non-material benefits from healthy ecosystems, as well as transmission of these data to policymakers. However, systematic incorporation of such data in policy arenas lags behind the theory. We argue that failure to incorporate data on non-material benefits, and thus failure to account for the full range of values at stake for impacted parties, leaves natural resource policy processes especially vulnerable to partisan influences. We examine a recently published account of conflicting economic benefit-cost analyses and their policy contexts to illustrate opportunities for robust analysis of non-material benefits to strengthen the policy process. Building on this analysis, we call for practical applications, complementing recent theoretical advances, to more comprehensively research the full range of benefits from ecosystems and incorporate such research into policy decisions. Doing so would enhance both the scientific and democratic merit of natural resource policy efforts.

To understand the preponderance of stereotyping of Earth Sciences as a male-oriented science in Africa, we perform a robust analysis of two electronic surveys. The current perception of female students' activities and performances was evaluated through a primary survey (N = 151; 28% female), which consisted of responses from academic professionals working in Universities and other Earth Science-related organizations. The second survey (N = 795; 38% female) investigated the ratio of male to female students currently enrolled in, or those that have graduated from Earth Science programs across Africa. Our approach for analyzing the collected data was based on descriptive statistics such as skewness, kurtosis , and Pearson correlation coefficients . Importantly, our data and analyses revealed under-representation of female student respondents, perhaps due to lower female students' enrolment among the sampled respondents. The responses to the main research questions are largely normally distributed and platykurtic. Thus, there is an overall lack of preference for female over male students across different ages, years of teaching, and cognate experience brackets in most countries represented. Many female academic professionals also slightly perceive male students are not more active than their female counterparts during laboratory and field activities. Majority of these female respondents think that geosciences are meant for female students also, but do not expressively consider it as a female profession. Respondents within the age bracket of 25–35 years and with cognate experience of >10 years do not consider male students to be better than their female counterparts. Significantly, our work shows that the gender of the respondents has no positive impact on their preference for male or female students albeit there were more male than female respondents.

A method for robustness in linear models is to assume that there is a mixture of standard and outlier observations with a different error variance for each class. For generalised linear models (GLM...

This article revisits the carbon dioxide (CO2) emissions-GDP causal relationships in the Middle Eastern and North African (MENA) countries by employing the Rossi (Economet Theor 21:962-990, 2005) instability-robust causality test. We show evidence of significant causality relationships for all considered countries within the instability context, whereas the standard Granger causality test fails to detect causal links in any direction, except for Egypt, Iran, and Morocco. An important policy implication resulting from this robust analysis is that the income is not affected by the cuts in the CO2 emissions for only two MENA countries, the UAE and Syria.

A new approach is here presented for the calculation of a robust reference value and the corresponding robust standard uncertainty to be used in proficiency test data analysis. The proposed method takes into account both the spread of the measured values and the measurement uncertainty declared by the participants in the proficiency test, while the robust standard ISO 13528 methods take only the measured values into account, neglecting measurement uncertainty. The zeta- and z-score performance statistics are consequently modified to adapt the proposed method. Examples of application of the method to actual proficiency test data are offered permitting to appreciate its validity and to facilitate insight.

The human skin microbiota is essential for maintaining homeostasis and ensuring barrier functions. Over the years, the characterization of its composition and taxonomic diversity has reached outstanding goals, with more than 10 million bacterial genes collected and cataloged. Nevertheless, the study of the skin microbiota presents specific challenges that need to be addressed in study design. Benchmarking procedures and reproducible and robust analysis workflows for increasing comparability among studies are required. For various reasons and because of specific technical problems, these issues have been investigated in gut microbiota studies, but they have been largely overlooked for skin microbiota. After a short description of the skin microbiota, the review tackles methodological aspects and their pitfalls, covering NGS approaches and high throughput culture-based techniques. Recent insights into the “core” and “transient” types of skin microbiota and how the manipulation of these communities can prevent or combat skin diseases are also covered. Finally, this review includes an overview of the main dermatological diseases, the changes in the microbiota composition associated with them, and the recommended skin sampling procedures. The last section focuses on topical and oral probiotics to improve and maintain skin health, considering their possible applications for skin diseases.

The growing importance of power systems in the development of modern society has increasingly drawn attention to the various dangers to which these systems are exposed. This paper proposes a dynamic response process analysis framework of power systems with the aim of exploring the response of the system when it encounters a fault and the repair process after the disaster. The analysis framework establishes three models to analyze power system structure and functional robustness: a purely topological model, an artificial flow model, and a direct current power flow model. We present different analysis metrics under different models, simulate three fault scenarios, and conduct an evaluation and analysis. According to the analysis results and considering the robust analysis conclusion and the concept of graph theory, we have formulated four repair strategies and made a simulated comparison of the repair processes. Taking IEEE39 and IEEE300 as examples, the effectiveness of the analysis framework is verified, the repair effect is verified, and the optimal repair strategy is determined.

This paper analyzes the influence of macroeconomic factors on the introduction of antidumping (AD) cases in Brazil and in Argentina. Since this type of research does not have large samples, the relevant variables were selected via Sala†i†Martin's (1997) test. The results, estimated using Poisson models, revealed that Brazil and Argentina are influenced by macroeconomic factors. However, the traditional (aggregated) models tend to minimize this influence on Brazil and overestimate it on Argentina. We also found that domestic and foreign income growth, exchange rate devaluation, and a reduction in domestic prices could reduce the number of AD cases in both countries. Therefore, there is evidence that these countries are using the AD law as a political tool, shifting it from its main objective of avoiding unfair trade.

We provide a comprehensive and robust analysis of the role of renewable energy consumption and institutions on economic growth and in combating CO2 emissions across the regions and income groups. For our empirical model, we use annual data from 85 developed and developing economies across the world over the period from 1991 to 2012. We employ various econometric techniques from panel estimations to obtain the robust results. Our findings confirm that there is significant heterogeneity across the sub-samples. Overall, results from the system-GMM and fully modified OLS indicate that the growth of renewable energy consumption has a significant positive and negative impact on economic output and CO2 emissions, respectively. Institutions have a positive influence on economic growth and a reducing effect on CO2 emissions. Our findings suggest that both renewable energy deployment and institutions are significant in promoting economic growth and reducing CO2 emissions. Finally, we suggest that institutional alignment is necessary to promote the use of renewable energy across economic activities to ensure sustainable economic development.

In this study, the effects of open innovation practices on enterprise value were discussed based on the supply chain perspective. A nonlinear programming mode was constructed considering the uncertainty of open innovation effect. On this basis, the prototype was analyzed using a robust optimization approach with comprehensive considerations to the infeasible probability of constraint and goal accuracy. Findings show that parameter $$\Gamma $$ could convert the nonlinear programming model with uncertainty into a robust model with a strong stability. Furthermore, $$\Gamma $$ could be used to regulate the preference of the manufacturer to profit and the uncertainties in innovative practices. A small $$\Gamma $$ indicates manufacturers can refer to more information, and the manufacturers are more inclined to a mass production, so the profit is larger. Meanwhile, a high $$\Gamma $$ means that manufacturers can refer to less information, so that they pay more attention to making the product output adapt to the uncertainty of innovative effect. Manufacturers could select the appropriate $$\Gamma $$ value and reasonably arrange the production outputs of different goods according to the technological level of open innovation subject and their preference to profit and uncertainty. These conclusions were verified by a case study. In this work, the uncertainty of the open innovation effect was investigated from the supply chain perspective, which is of important significance to the decision making on optimal supply chain output and innovation risk control under an uncertain environment.

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require.

Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In ...

In geodesy and geophysics, continuous GNSS observations have been used globally. As the number of GNSS observing stations increases, GNSS time series analysis software should be developed with more flexible format support, better man---machine interaction, and robust analysis characteristics. To meet this requirement, a new software package called TSAnalyzer was written in Python and was developed for preprocessing and analyzing continuous GNSS position time series individually, as well as with batch processing. This software can read GNSS position time series with different formats, pick epochs of offsets or seismic events interactively, remove outliers, and estimate linear, polynomial, and harmonic signals. It also provides Lomb---Scargle spectrum analysis. Since it is based on Python, it is cross-platform.

The Economic Order Quantity (EOQ) model is a simple starting point for modeling inventory and resupply. We will explore models and quantitative approaches for making a variety of strategic, tactica...

This paper investigates the role of Muslim CEOs on the performance of Malaysian Shariah firms. Based on firm-level data from Bursa Malaysia from 2007 to 2013, we find no statistical difference between the performance of Shariah and non-Shariah firms; however, firms with Muslim CEOs do show lower performance levels relative to firms with non-Muslim CEOs. We further examine a sub-sample of the results for firms with widely held shareholder bases and inactive board members and find an obvious increase in the negative effect of Muslim CEOs under these two scenarios. Focusing on Shariah firms, we investigate whether a Muslim owner, Muslim chairman and Muslim-dominated board of directors facilitate the negative effects of Muslim CEOs. No supporting evidence is documented under a return on assets (ROA) model, but a Muslim-dominated board of directors is found to facilitate the negative effects of a Muslim CEO under a stock-return model in a robust analysis. Finally, we investigate whether a degree of the ownership concentration, a degree of board activeness and a degree of board independence moderates the negative effects of Muslim CEOs. We document consistently positive effects of ownership concentration on Muslim CEOs under an ROA as well as all other performance models.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual-guide RNAs in three cell lines, altogether comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified and these patterns replicated with combinatorial drugs at 75% precision. Based on these results we anticipate cellular context will be critical to synthetic-lethal therapies.

This paper considers the asymptotic and finite-time synchronization of drive-response memristive neural networks (MNNs) with time-varying delays. It is known that the parameters of MNNs are state-dependent, and hence the traditional robust control and analytical techniques cannot be directly applied. This difficulty is overcome by using the concept of Filippov solution. However, the special characteristics of MNNs may lead to unexpected parameter mismatch issue when different initial conditions are chosen. Based on a new robust control design, the mismatching issue is solved. Sufficient conditions are derived to guarantee the asymptotic synchronization of the considered MNNs with delays, which may be less conservative than synchronization criterion obtained by using existing methods. Moreover, without using the existing finite-time stability theorem, finite-time synchronization of the MNNs with delays is also investigated. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.

In this article, a robust statistical analysis of particulate matter (PM2.5) concentration measurements is carried out. Here, the region chosen for the study was the urban park La Carolina, which is one of the most important in Quito, Ecuador, and is located in the financial center of the city. This park is surrounded by avenues with high traffic, in which shopping centers, businesses, entertainment venues, and homes, among other things, can be found. Therefore, it is important to study air pollution in the region where this urban park is located, in order to contribute to the improvement of the quality of life in the area. The preliminary study presented in this article was focused on the robust estimation of both the central tendency and the dispersion of the PM2.5 concentration measurements carried out in the park and some surrounding streets. To this end, the following estimators were used: (i) for robust location estimation: &alpha, trimmed mean, trimean, and median estimators, and (ii) for robust scale estimation: median absolute deviation, semi interquartile range, biweight midvariance, and estimators based on a subrange. In addition, nonparametric confidence intervals were established, and air pollution levels due to PM2.5 concentrations were classified according to categories established by the Quito Air Quality Index. According to these categories, the results of the analysis showed that neither the streets that border the park nor the park itself are at the Alert level. Finally, it can be said that La Carolina Park is fulfilling its function as an air pollution filter.

The maximum sensitivity function as the conventional robustness index is often used to test the robustness and cannot be used to tune the controller parameters directly. To reduce analytical difficulties in dealing with the maximum sensitivity function and improve the control performance of the proportional-integral-derivative controller, the relative delay margin as a good alternative is proposed to offer a simple robust analysis for the proportional-integral-derivative controller and the first-order plus dead-time systems. The relationship between the parameters of the proportional-integral-derivative controller and the new pair, e.g., the phase margin and the corresponding gain crossover frequency, is derived. Based on this work, the stability regions of the proportional-integral-derivative controller parameters, the proportional gain and the integral gain with a given derivative gain, are obtained in a simple way. The tuning of the proportional-integral-derivative controller with constraints on the relative delay margin is simplified into an optimal disturbance rejection problem and the tuning procedure is summarized. For convenience, the recommended parameters are also offered. Simulation results demonstrate that the proposed methodology has better tracking and disturbance rejection performance than other comparative design methodologies of the proportional-integral/proportional-integral-derivative controller. For example, the integrated absolute errors of the proposed proportional-integral-derivative controller for the tracking performance and disturbance rejection performance are less than 91.3% and 91.7% of the integrated absolute errors of other comparative controllers in Example 3, respectively. The proposed methodology shows great potential in industrial applications. Besides, the proposed method can be applied to the design of the proportional-integral-derivative controller with filtered derivative which is recommended for practical applications to weaken the adverse influence of the high-frequency measurement noise.

228 p. This thesis studies the nonlinear robust control problems for continuous time parameter-dependent nonlinear systems with polynomial vector fields. Several novel approaches have been proposed to overcome the non-convex nature and numerical difficulty in ro-bust nonlinear system design. Several design problems are addressed, including stability analysis and synthesis, estimation of region of attraction, H^ synthesis, output feedback design, multi-objective reliable control and H^ filter design. DOCTOR OF PHILOSOPHY (EEE)

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.

Automated image analysis tools for Ki67 breast cancer digital pathology images would have significant value if integrated into diagnostic pathology workflows. Such tools would reduce the workload of pathologists, while improving efficiency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An “overstaining” threshold is implemented to adjustforbackgroundoverstaining,andanautomatednucleiradiusestimatorisdesigned to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67− nuclei. The method accurately quantified the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classified 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classifiers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantification accuracy.

Biped robots are expected to have various activities in human society, and many biped robots have been developed. As the example, limit cycle walkers, can realize efficient walking using their own dynamics. However, they have a problem of weak robustness on uneven terrains. On the other hand, a technique for improving robustness by using output zeroing control with relative degree 3 has been proposed. In this paper, we derive the reference trajectory for the output function and holonomic constraints used when applying output zeroing control with relative degree 3 by direct collocation method which is a method of a trajectory optimization. We show that they walk by using the derived reference output function and holonomic constraints and improve the robustness on uneven terrain.

Introduction: Mometasone furoate (MF), an inhaled corticosteroid (ICS), is approved for the treatment of asthma. Previous studies suggest that MF Twisthaler® doses of 800 and 200 µg and MF Breezhaler® doses of 320 and 80 µg would elicit similar lung function effects, respectively. These MF doses are also used in an indacaterol/MF combination in development for asthma. Since sensitivity to ICS is variable, individual patients’ ICS sensitivity (as measured by FEV1 decline on ICS weaning) was used to build a robust analysis model for the study data. Methods: This was a randomized, double-blind, double-dummy, parallel-group, non-inferiority study of 739 adolescents and adults with asthma. MF was delivered with Breezhaler® at daily doses of 80 and 320 µg and corresponding Twisthaler® doses of 200 and 800 µg. The primary endpoint was trough FEV1 on Day 29 (non-inferiority margin: -90 mL). Models with and without ICS sensitivity at baseline were used to analyze the data. Results: In the model with ICS sensitivity the least squares (LS) mean difference (Δ) in trough FEV1 between MF 80 µg Breezhaler® and MF 200 µg Twisthaler® was 27 mL (95% CI –34, 89); for MF 320 µg Breezhaler® and MF 800 µg Twisthaler® Δ was 0 mL (95% CI –60, 61). In the model without ICS sensitivity Δ between MF 80 μg Breezhaler® and MF 200 μg Twisthaler® patients was 68 mL (0, 137) and 25 mL (-43, 92) between MF 320 μg Breezhaler® and MF 800 μg Twisthaler®. Model diagnostics showed that using ICS sensitivity as a covariate improved the model. Conclusion: MF 80 and 320 μg, delivered via Breezhaler®, is non-inferior to MF at 200 and 800 μg, delivered via Twisthaler®. Including ICS sensitivity in the model improves model robustness.

Sheet metal parts are widely used in automotive, aerospace, ship and consumer goods industries. The final dimensions of a sheet metal assembly result from the parts deformation, which in turn is affected by many variations in material, thickness and single parts dimensions. The tolerance analysis on sheet metal assemblies improve the knowledge about the process. Advanced simulations enable the optimization of product features, GD&T scheme and assembly process. Moreover, Variational Models of both the product and the assembly system enable to assess the sources of 3D error propagation from the different contributors. However, the simulation results are very affected by the modelling approach of critical components, such as the Fixture Systems. The present paper firstly introduces a strategy to model the Fixture System and the assembly process for compliant parts. Then, a robust analysis of the variations in the model with respect to the modelling factors is performed by a Design of Experiments. A case study on an automotive fender is discussed. The results demonstrate that the modelling strategy of the clamping operation have the main effects, while the modelling of locators scheme, spot joints and FEM meshing are less important.

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior. Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with 'ovarian cancer' including 'proteomics', 'proteogenomic', 'reverse-phase protein array', 'mass spectrometry', and 'adaptive response', were used to search PubMed. Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

Purpose The purpose of this paper is to establish the evidence for, the why and how recent austerity policy atmosphere associated with the UK government affected environmental protection decisions within planning in Scotland. Design/methodology/approach A qualitative analysis based on perspectives gathered via questionnaire survey targeted at stakeholders involved in planning in Scotland was undertaken. The questionnaire responses were analysed thematically, supplemented by using statistical tests of significance and variance to show how responses differed across participants. Findings The evidence showed that austerity policy atmosphere resulted in a pervasive neoliberal imperative of resuscitating the economy; whilst producing subtle and adverse effects on environmental decisions. This was best understood within a neo-Gramscian perspective of hegemony, borrowed from the field of political economy of states. Research limitations/implications The gathered views were constrained within unknown biases that the participants may have had; and because the case study approach was not equipped to generalise the results beyond the study, more research testing cause-effect between the austerity and selected environmental parameters is needed, from various contexts. Practical implications Decision-making frameworks should explicitly acknowledge the unique pressures during austerity periods; and contemplate resilient decision-making frameworks that can withstand the hegemonic tendencies which prioritise economic goals above environmental ones. Originality/value Whilst the area of austerity’s impacts on the environment remains poorly evidenced, empirically, this seminal paper uses robust analysis to establish how the austerity policy atmosphere affects environmental decisions. This is insight into what may be happening in other similar situations outside Scotland, raising concern as to whether and how we should approach the challenge of hegemonic ideas.

Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method&rsquo, s detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error.