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We consider the estimation of a density at a fixed point under a local differential privacy constraint, where the observations are anonymised before being available for statistical inference. We propose both a privatised version of a projection density estimator as well as a kernel density estimator and derive their minimax rates under a privacy constraint. There is a twofold deterioration of the minimax rates due to the anonymisation, which we show to be unavoidable by providing lower bounds. In both estimation procedures a tuning parameter has to be chosen. We suggest a variant of the classical Goldenshluger-Lepski method for choosing the bandwidth and the cut-off dimension, respectively, and analyse its performance. It provides adaptive minimax-optimal (up to log-factors) estimators. We discuss in detail how the lower and upper bound depend on the privacy constraints, which in turn is reflected by a modification of the adaptive method.

This paper analysed the economic efficiency of small-scale tomato farmers in the Greater Letaba municipality of South Africa’s Limpopo Province. Primary data were collected from 68 tomato farmers based on structured questionnaires and using convenience and purposive sampling procedures. The Cobb-Douglas production function was used to analyse the level of economic efficiency. The study utilised the output approach, where the output achieved by the farmers is compared to the maximum output attainable using the given inputs. The empirical results reveal that mean technical, allocative, and economic efficiency levels are at 0.95, 0.41 and 0.39, respectively. The study also found that land (farm size), seedlings, labour, pesticides and water have a positively significant relationship with the production of tomatoes in the study area. Therefore, it is recommended that the Department of Agriculture, Land Reform and Rural Development provide farmers with enough extension services by employing more extension personnel. Government programmes such as the Comprehensive Agricultural Support Programme should be intensified – through the pillar of training and capacity building – to reach the small-scale farmers in the municipality, whereby farmers should be provided with training on the recommended minimum and maximum application of inputs like pesticides, fertilisers, seedlings and water in tomato production.

Objective. Conduction-induced heart failure in patients with left bundle branch block (LBBB) can benefit from cardiac resynchronization therapy (CRT). However, some patients are non-responders to the therapy with one contributing factor being poor optimization of the atrioventricular (AV) pacing delay. In this study, we have investigated the pacing-induced changes in the seismocardiogram (SCG). Approach. 14 patients with heart failure, LBBB, and CRT were included. SCG was recorded with pacing turned on and off. Based on a mean SCG heartbeat from each patient, fiducial points were annotated, and cardiac timing intervals (CTI) and amplitudes were derived. These were compared between the CRT group and a group of healthy normal subjects (n = 14). Echocardiography was also used to derive CTI. Intervals derived from the SCG and echocardiogram were correlated. Main results. The isovolumetric contraction time (IVCT) derived from SCG was significantly shorter in the CRT group when the pacemaker was turned on (63.2–52.6 ms, p = 0.027). The first peak-to-peak amplitude in the systolic complex was significantly larger with the pacemaker turned on (p = 0.002), as well as the ∣max-min∣ amplitude in the systolic complex (p = 0.003). Isovolumetric relaxation time and left ventricular ejection time (LVET) were not significantly different between pacemaker settings. Compared to normal subjects, IVCT was significantly prolonged with the pacemaker turned off. All amplitudes were significantly larger in the healthy subject group. IVCT and LVET derived from SCG were significantly correlated to the echocardiogram. Significance. IVCT shortened and SCG amplitudes increased in response to CRT, indicating a more efficient ventricular contraction. This demonstrates the possibility to detect cardio-mechanic changes in response to treatment with the SCG. However, for the patients the systolic part of the SCG was abnormal and difficult to characterize, raising concerns about the correct interpretation of the SCG.

OBJECTIVES: Previous studies have observed an increase in low frequency diastolic heart sounds in patients with coronary artery disease (CAD). The aim was to develop and validate a diagnostic, computerized acoustic CAD-score based on heart sounds for the non-invasive detection of CAD.METHODS: Prospective study enrolling 463 patients referred for elective coronary angiography. Pre-procedure non-invasive recordings of heart sounds were obtained using a novel acoustic sensor. A CAD-score was defined as the power ratio between the 10-90 Hz frequency spectrum and the 90-300 Hz frequency spectrum of the mid-diastolic heart sound. Quantitative coronary angiography analysis was performed by a blinded core laboratory and patients grouped according to the results: obstructive CAD defined by the presence of at least one ≥ 50% stenosis, non-obstructive CAD as patients with a maximal stenosis in the 25-50% interval and non-CAD as no coronary lesions exceeding 25%. We excluded patients with potential confounders or incomplete data (n = 245). To avoid over-fitting the final cohort of 218 patients was randomly divided into to a training group for development (n = 127) and a validation group (n = 91).RESULTS: In both the training and the validation group the CAD-score was significantly increased in CAD patients compared to non-CAD patients (p < 0.0001). In the validation group the area under the receiver-operating curve was 77% (95% CI 63-91%). Sensitivity was 71% (95% CI 59-82%) and specificity 64% (95% CI 45-83%).CONCLUSION: The acoustic CAD-score is a new, inexpensive, non-invasive method to detect CAD, which may supplement clinical risk stratification and reduce the need for subsequent non-invasive and invasive testing.

In a circular deconvolution model we consider the fully data driven density estimation of a circular random variable where the density of the additive independent measurement error is unknown. We have at hand two independent iid samples, one of the contaminated version of the variable of interest, and the other of the additive noise. We show optimality,in an oracle and minimax sense, of a fully data-driven weighted sum of orthogonal series density estimators. Two shapes of random weights are considered, one motivated by a Bayesian approach and the other by a well known model selection method. We derive non-asymptotic upper bounds for the quadratic risk and the maximal quadratic risk over Sobolev-like ellipsoids of the fully data-driven estimator. We compute rates which can be obtained in different configurations for the smoothness of the density of interest and the error density. The rates (strictly) match the optimal oracle or minimax rates for a large variety of cases, and feature otherwise at most a deterioration by a logarithmic factor. We illustrate the performance of the fully data-driven weighted sum of orthogonal series estimators by a simulation study., 19 pages, 4 figures, 27 appendix pages

We study the non-parametric estimation of the value ${\theta}(f )$ of a linear functional evaluated at an unknown density function f with support on $R_+$ based on an i.i.d. sample with multiplicative measurement errors. The proposed estimation procedure combines the estimation of the Mellin transform of the density $f$ and a regularisation of the inverse of the Mellin transform by a spectral cut-off. In order to bound the mean squared error we distinguish several scenarios characterised through different decays of the upcoming Mellin transforms and the smoothnes of the linear functional. In fact, we identify scenarios, where a non-trivial choice of the upcoming tuning parameter is necessary and propose a data-driven choice based on a Goldenshluger-Lepski method. Additionally, we show minimax-optimality over Mellin-Sobolev spaces of the estimator., Comment: 25 pages

Decision-makers are increasingly required to assess the value created by complex physical systems over their entire life cycle. The commonly applied Life Cycle Costing approach fails to fully capture value, as it is primarily aimed at costs, takes a reductionist approach, and does not account for continuously changing industrial environments. To address these shortcomings, the Life Cycle Valuation methodology is proposed, designed as a hybrid of LCC and Life Cycle Assessment. LCV facilitates the assessment of costs and benefits from multiple complementary perspectives and can be tailored to specific decision contexts, as demonstrated by applying LCV during Asset Management decision-making.

In this thesis I focussed on a mutualistic symbiosis; the fungus-growing termites and their fungal symbiont Termitomyces. The origin of this symbiosis between fungus-growing termites (Macrotermitinae) and Basidiomycete fungi of the genus Termitomyces has been estimated at approximately 30 million years ago in the rainforests of central Africa. The symbiosis has a single origin with no known reversals of both partners to a non-symbiotic state. The single origin of fungiculture and the absence of any reversals of the fungus to a free-living state are puzzling as both parties have retained independent reproduction and dispersal, which could potentially de-stabilize the symbiosis by leaving room for the fungus to escape or for the termite to domesticate more than one symbiont. For this thesis I studied the biology of the closest relatives of Termitomyces in order to reconstruct the biology of its ancestor. Furthermore, I studied the phylogeny and biology of the genus Termitomyces in more detail to understand what made the genus so diverse and successful.In order to make inferences on the events leading to domestication of Termitomyces a well-supported phylogeny is needed. In chapter 2 I performed a phylogenomic analysis of 25 species of Termitomyces and 21 related non-domesticated species. To reconstruct the biology of the ancestor of Termitomyces I also studied the biology of the non-domesticated species. The phylogenomic analysis recovered the insect-faecal associated genus Arthromyces as the sister group of Termitomyces. I found that Arthromyces shares a suite of traits with Termitomyces, indicating that their common ancestor also possessed these traits. I hypothesize that this set of traits predisposed the ancestor of Termitomyces towards domestication.One of traits found in many species related to Termitomyces is the production of asexual spores (conidia). I hypothesized that these conidia play a role in local dispersal of the fungus and potentially in faster substrate colonisation. In chapter 3 I conducted a population study on the insect-faecal associated and conidia-producing fungus Blastosporella zonata. I sequenced two highly variable genetic markers of 21 collections of B. zonata mushrooms at three collection sites in cloud forests near Murillo, Colombia. I found signatures of clonality within a collection site but usually not between sites, which indicates that conidia are mainly used for local dispersal. There was genetic diversity as well, particularly between collection sites, indicating an important role for sexual spores (basidiospores) and resulting in unique genotypes of most collections. Unexpectedly. by reconstructing fungal haplotypes and subsequent phylogenetic analysis on these, I found evidence for the existence of two cryptic biological species in B. zonata.In the studies I conducted in chapter 2 I used five unidentified species of Tephrocybe. As the morphological descriptions as well as the DNA sequences of these species did not match any species already known, I chose to describe those species in chapter 4. The taxonomy for these five new species was challenging as morphological traits and biogeography did not align with the molecular phylogeny. Therefore, I discussed several alternative options to keep genera monophyletic. This ultimately led to the erection of four new genera: Australocybe, Nigrocarnea, Phaeotephrocybe and Praearthromyces.The genus Termitomyces contains about 40 described species and harbours a lot of morphological diversity. Probably many species of Termitomyces remain undescribed as there is a strong bias towards the description of species that regularly produce mushrooms. In chapter 5 I assembled a large dataset of over 1500 DNA sequences of Termitomyces and used species delimitation software to sort sequences into hypothetical species based on the barcoding gap within the dataset. Using this approach, I recovered 87 phylogenetic species and for these species I collected specimen metadata such as continent of origin and termite host genus. A phylogenetic reconstruction of the 87 species revealed five main symbiont groups which corresponded with the five largest fungus-growing termite genera. I identified several factors which could be involved in speciation: fruiting mode (underground or aboveground), geographic separation, symbiont transmission mode and suppression of mushroom formation by the host termite. Finally, I demonstrated that mushroom morphology does not correlate with phylogeny and therefore, without additional support of molecular data, is not useful for taxonomy in this genus.Uniparental vertical transmission of symbionts is an important factor in aligning reproductive interests of host and symbiont and thus stabilizing the symbiosis. In fungus-growing termites, horizontal transmission is the predominant transmission mode, but uniparental vertical transmission has evolved via the female reproductives of the genus Microtermes and via the male reproductives of the species M. bellicosus. In chapter 6 I studied populations of Microtermes and their symbionts in South Africa. I found high genetic diversity in symbiont populations and low host-specificity, which indicates that frequent horizontal symbiont exchange occurs as well as sexual reproduction, despite the absence of mushrooms of these species. I therefore argue that Microtermes species regularly acquire symbionts associated with species of other genera that do not suppress mushroom formation, such as species of the genus Ancistrotermes.New species of mushroom-forming fungi are often described based on morphological features of the mushroom. In the genus Termitomyces this has caused a heavy bias towards the description of species, which regularly produce mushrooms, leaving species that rarely or infrequently fruit undescribed. In chapter 7 I describe, for the first time in this genus, a species of Termitomyces with no reported naturally produced mushrooms, based on DNA sequence evidence, biological data and asexual features.In chapter 8, I discuss how investigating and understanding the biology of species closely related to fungal symbionts can assist in reconstructing the biology of the ancestor and thereby determine conditions that facilitated fungal domestication by an insect. Although my research focused on a specific group of fungus-growing insects, the fungus-growing termites, I discuss how my approach can be applied to different unrelated groups of fungus-growing insects as well. Finally I discuss how my work not only offers significant contributions to the knowledge on the origin and subsequent evolution of fungiculture in termites, but also contributed to general fundamental knowledge of mutualistic symbioses in general. &nbsp

The study aims to estimate empirically the relationship between economic growth and unemployment rate in Limpopo Province of South Africa. The analysis used quarterly data covering the period 2008-2018 which was obtained from Statistics South Africa. The study employed, difference model, dynamic model, and granger causality test for data analysis, in order to consider both, short term and long term possible relationship Based on the difference model estimation of the coefficient was done and the coefficient was found to be -0.22. From Granger causality test, causal relationship between these two variables doesn’t exist meaning that change in the growth rate of real GDP doesn’t cause change in the rate of unemployment and vice-versa. Inapplicable of the law indicates that a cyclical recovery will not be accompanied by reduction of unemployment. Furthermore, this might reflect the sizable structural and/or frictional component of unemployment in Limpopo Province. Lastly, the country's economic policies have not been suitable for fostering development that can reduce unemployment and this could be due to lack of appropriate composition of public sector and private sector.

Human values—life goals—guide our attitudes and actions. Brands such as Patagonia, TOMS, Warby Parker, Chobani, and Nike successfully position their marketing around human values such as safety, harmony, seeking pleasure, or social welfare. Evidently, consumers attach importance to brands whose values align with their own values. However, the alignment of values (value congruence) and the resulting effects on (re)purchasing behavior are scarcely discussed in the marketing literature. The effects of “traits” and “functional congruence” on purchasing behavior receive considerably more attention. Since human values are conceptually distinct from personality traits, the following question is posed: What is the role of value congruence in the process of consumer brand selection compared with that of trait and functional congruence. An online survey (N = 1182) is conducted to study the effects of these different types of congruence on re-purchase intentions for a range of product categories. Multiple regression and mediation analyses reveal that value congruence is a significant predictor of re-purchase intentions and that it is more important than trait congruence for the categories of services and durables, but not for consumables. This study offers insights into when brand marketing should be aligned with personality traits and human values, respectively.

In the inverse Gaussian sequence space model with additional noisy observations of the operator, we derive nonasymptotic minimax radii of testing for ellipsoid-type alternatives simultaneously for both the signal detection problem (testing against zero) and the goodness-of-fit testing problem (testing against a prescribed sequence) without any regularity assumption on the null hypothesis. The radii are the maximum of two terms, each of which only depends on one of the noise levels. Interestingly, the term involving the noise level of the operator explicitly depends on the null hypothesis and vanishes in the signal detection case. The minimax radii are established by first showing a lower bound for arbitrary null hypotheses and noise levels. For the upper bound we consider two testing procedures, a direct test based on estimating the energy in the image space and an indirect test. Under mild assumptions, we prove that the testing radius of the indirect test achieves the lower bound, which shows the minimax optimality of the radius and the test. We highlight the assumptions under which the direct test also performs optimally. Furthermore, we apply a classical Bonferroni method for making both the indirect and the direct test adaptive with respect to the regularity of the alternative. The radii of the adaptive tests are deteriorated by an additional log-factor, which we show to be unavoidable. The results are illustrated considering Sobolev spaces and mildly or severely ill-posed inverse problems.

We study non-parametric estimation of an unknown density with support in R (respectively R+). The proposed estimation procedure is based on the projection on finite dimensional subspaces spanned by the Hermite (respectively the Laguerre) functions. The focus of this paper is to introduce a data-driven aggregation approach in order to deal with the upcoming bias-variance trade-off. Our novel procedure integrates the usual model selection method as a limit case. We show the oracle- and the minimax-optimality of the data-driven aggregated density estimator and hence its adaptivity. We present results of a simulation study which allow to compare the finite sample performance of the data-driven estimators using model selection compared to the new aggregation.

Plants manipulate their rhizosphere community in a species and even a plant life stage-dependent manner. In essence plants select, promote and (de)activate directly the local bacterial and fungal community, and indirectly representatives of the next trophic level, protists and nematodes. By doing so, plants enlarge the pool of bioavailable nutrients and maximize local disease suppressiveness within the boundaries set by the nature of the local microbial community. MiSeq sequencing of specific variable regions of the 16S or 18S ribosomal DNA (rDNA) is widely used to map microbial shifts. As current RNA extraction procedures are time-consuming and expensive, the rRNA-based characterization of the active microbial community is taken along less frequently. Recently, we developed a relatively fast and affordable protocol for the simultaneous extraction of rDNA and rRNA from soil. Here, we investigated the long-term impact of three type of soil management, two conventional and an organic regime, on soil biota in fields naturally infested with the Columbian root-knot nematode Meloidogyne chitwoodi with pea (Pisum sativum) as the main crop. For all soil samples, large differences were observed between resident (rDNA) and active (rRNA) microbial communities. Among the four organismal group under investigation, the bacterial community was most affected by the main crop, and unweighted and weighted UniFrac analyses (explaining respectively 16.4% and 51.3% of the observed variation) pointed at a quantitative rather than a qualitative shift. LEfSe analyses were employed for each of the four organismal groups to taxonomically pinpoint the effects of soil management. Concentrating on the bacterial community in the pea rhizosphere, organic soil management resulted in a remarkable activation of members of the Burkholderiaceae, Enterobacteriaceae, and Pseudomonadaceae. Prolonged organic soil management was also accompanied by significantly higher densities of bacterivorous nematodes, whereas levels of M. chitwoodi had dropped drastically. Though present and active in the fields under investigation Orbiliaceae, a family harboring numerous nematophagous fungi, was not associated with the M. chitwoodi decline. A closer look revealed that a local accumulation and activation of Pseudomonas, a genus that includes a number of nematode-suppressive species, paralleled the lower M. chitwoodi densities. This study underlines the relevance of taking along both resident and active fractions of multiple organismal groups while mapping the impact of e.g. crops and soil management regimes.

In a circular convolution model, we aim to infer on the density of a circular random variable using observations contaminated by an additive measurement error. We highlight the interplay of the two problems: optimal testing and quadratic functional estimation. Under general regularity assumptions, we determine an upper bound for the minimax risk of estimation for the quadratic functional. The upper bound consists of two terms, one that mimics a classical bias-variance trade-off and a second that causes the typical elbow effect in quadratic functional estimation. Using a minimax optimal estimator of the quadratic functional as a test statistic, we derive an upper bound for the nonasymptotic minimax radius of testing for nonparametric alternatives. Interestingly, the term causing the elbow effect in the estimation case vanishes in the radius of testing. We provide a matching lower bound for the testing problem. By showing that any lower bound for the testing problem also yields a lower bound for the quadratic functional estimation problem, we obtain a lower bound for the risk of estimation. Lastly, we prove a matching lower bound for the term causing the elbow effect in the estimation problem. The results are illustrated considering Sobolev spaces and ordinary or super smooth error densities.

Asset Life Cycle Management aims to maximize the value realized from physical assets over their complete lifetime. Over the years, the operation and maintenance of the assets must continually be adapted to changes in goals and context. In an earlier publication, we proposed the Lifetime Impact Identification Analysis to identify such changes. This paper tests this method through an application at two different companies. The method proved to result in a shared and integral overview of long-term challenges and opportunities for the asset, based on experts discussing the asset's future from a technical, economic, compliance, commercial and organizational perspective.

In the capital-intensive industry, maintenance expenditures can add up to several times the initial investment. In order to be competitive in their business, owners and users of these capital goods have to take into account the total life cycle cost at investment (e.g. the lifespan of a capital is often more than several decades), the renewal decisions for their installations and the logistic management of the spare parts. Erroneous or unstructured initial spare parts assortment decision-making part of the logistic management can lead to undesired downtime and increases the risk of obsolete or unavailable components. Decision making is complicated by non-existent data in the early design phase and several information management problems. Based on a case study at Netherlands Railways (the largest maintainer of rolling stock in the Netherlands) and literature review a Decision Support Model to structure and improve the data gathering for more effective initial spare part assortment decision making is proposed.

Plants manipulate their rhizosphere community in a species and even a plant life stage-dependent manner. In essence plants select, promote and (de)activate directly the local bacterial and fungal community, and indirectly representatives of the next trophic level, protists and nematodes. By doing so, plants enlarge the pool of bioavailable nutrients and maximize local disease suppressiveness within the boundaries set by the nature of the local microbial community. MiSeq sequencing of specific variable regions of the 16S or 18S ribosomal DNA (rDNA) is widely used to map microbial shifts. As current RNA extraction procedures are time-consuming and expensive, the rRNA-based characterization of the active microbial community is taken along less frequently. Recently, we developed a relatively fast and affordable protocol for the simultaneous extraction of rDNA and rRNA from soil. Here, we investigated the long-term impact of three type of soil management, two conventional and an organic regime, on soil biota in fields naturally infested with the Columbian root-knot nematode Meloidogyne chitwoodi with pea (Pisum sativum) as the main crop. For all soil samples, large differences were observed between resident (rDNA) and active (rRNA) microbial communities. Among the four organismal group under investigation, the bacterial community was most affected by the main crop, and unweighted and weighted UniFrac analyses (explaining respectively 16.4% and 51.3% of the observed variation) pointed at a quantitative rather than a qualitative shift. LEfSe analyses were employed for each of the four organismal groups to taxonomically pinpoint the effects of soil management. Concentrating on the bacterial community in the pea rhizosphere, organic soil management resulted in a remarkable activation of members of the Burkholderiaceae, Enterobacteriaceae, and Pseudomonadaceae. Prolonged organic soil management was also accompanied by significantly higher densities of bacterivorous nematodes, whereas levels of M. chitwoodi had dropped drastically. Though present and active in the fields under investigation Orbiliaceae, a family harboring numerous nematophagous fungi, was not associated with the M. chitwoodi decline. A closer look revealed that a local accumulation and activation of Pseudomonas, a genus that includes a number of nematode-suppressive species, paralleled the lower M. chitwoodi densities. This study underlines the relevance of taking along both resident and active fractions of multiple organismal groups while mapping the impact of e.g. crops and soil management regimes.

This study aims to estimate sorghum supply elasticity in South Africa. The study used time series data spanning from 1998 to 2016, obtained from the abstracts of agricultural statistics. The Variance Error Correction Model was employed; the study used two dependent variables, these being area and yield response functions. The results have shown that the area response function was found to be a robust model as most of the variables were significant, responsive and elastic. Maize price, as a competing crop for sorghum, negatively influenced the area allocation; however, the remaining variableshad a positive impact on area allocation in the long-run. The yield response function was found not to be robust and hence not adopted. It was therefore concluded that the area response function is more robust than the yield response function, hence sorghum production has shown more response to areaallocation than yield. The findings further indicated that the error correction term for area and for the yield response function was –1.55 and –1.30, respectively. This indicated that the two models were able to revert to equilibrium. Based on the findings, the study recommends that amongst other methods to enhance sorghum output, producers could use improved varieties or hybrids, as this action would result in allocation of more land to sorghum production, following price change.

Conventional agricultural production systems, typified by large inputs of mineral fertilizers and pesticides, reduce soil biodiversity and may negatively affect ecosystem services such as carbon fixation, nutrient cycling and disease suppressiveness. Organic soil management is thought to contribute to a more diverse and stable soil food web, but data detailing this effect are sparse and fragmented. We set out to map both the resident (rDNA) and the active (rRNA) fractions of bacterial, fungal, protozoan and metazoan communities under various soil management regimes in two distinct soil types with barley as the main crop. Contrasts between resident and active communities explained 22%, 14%, 21% and 25% of the variance within the bacterial, fungal, protozoan, and metazoan communities. As the active fractions of organismal groups define the actual ecological functioning of soils, our findings underline the relevance of characterizing both resident and active pools. All four major organismal groups were affected by soil management (p

A reduction in duration of antibiotic therapy is crucial in minimizing the development of antimicrobial resistance, drug-related side effects and health care costs. The minimal effective duration of antimicrobial therapy for febrile urinary tract infections (fUTI) remains a topic of uncertainty, especially in male patients, those of older age or with comorbidities. Biomarkers have the potential to objectively identify the optimal moment for cessation of therapy. A secondary analysis of a randomized placebo-controlled trial among 35 primary care centers and 7 emergency departments of regional hospitals in the Netherlands. Women and men aged ≥18 years with a diagnosis of fUTI were randomly assigned to receive antibiotic treatment for 7 or 14 days. Patients indicated to receive antimicrobial treatment for more than 14 days were excluded from randomization. The biomarkers procalcitonin (PCT), mid-regional proadrenomedullin (MR-proADM), and C-reactive protein (CRP) were compared in their ability to predict clinical cure or failure through the 10–18 day post-treatment visit. Biomarker concentrations were measured in 249 patients, with a clinical cure rate of 94% in the 165 randomized and 88% in the 84 non-randomized patients. PCT, MR-proADM and CRP concentrations did not differ between patients with clinical cure and treatment failure, and did not predict treatment outcome, irrespective of 7 or 14 day treatment duration (ROCAUC 0.521; 0.515; 0.512, respectively). PCT concentrations at presentation were positively correlated with bacteraemia (τ = 0.33, p

Asset-intensive organizations rely on physical assets that are expensive, complex, and have a significant impact on organizational performance. The management of such assets is essential when seeking for reliable performance in a world of increasing uncertainties. The observation that asset-intensive organizations deal with increasingly complex and tightly coupled systems and often operate in highly demanding environments may indicate that they should adopt practices from high reliability organizations (HRO) to ensure and maintain reliable performance in the fourth industrial revolution. This chapter operationalizes the HRO concept in the field of physical asset management, measures to what extent the underlying principles are recognized, and explores the relationship between the HRO principles and asset performance using a descriptive survey. Results indicated that the HRO principles are recognized and may, therefore, serve as an instrument for reliable performance when adopting new technologies. A positive relation between asset performance and the five HRO principles was identified.

DSO’s are increasingly expected and motivated to improve the environmental impact of their assets. A key improvement opportunity can be found in the procurement process, as most characteristics that determine an asset’s environmental impact are committed at this stage. Many DSO’s look to LCA as the international industry standard for assessing and quantifying environmental impact. Full LCA however, is too complex and tedious to be pragmatically used in procurement processes, as these require simplicity and accessibility to provide equal opportunity for all participants. By only including only the most dominant input & output parameters of a “master” LCA, the process of performing an LCA on a specific asset can be greatly simplified. This simplified model is demonstrated and evaluated through a case study on the procurement of medium voltage switchgear. The streamlined LCA approach presented in this article maintains most of the relevance of full LCA, while greatly reducing the complexity and resources required for its use.

Industry shows an increasing interest in the circular economy. However, circularity for physical capital assets is still ill-defined and existing models are complex and information dependent hindering implementation. This paper addresses these gaps by operationalizing circular economy principles and developing a suitable business case framework embedding circularity. Following the Design Science Methodology, a framework was developed based on circularity and investment decision-making literature and requirements from a Dutch electricity grid operator. An initial evaluation showed that the framework supports selecting the preferred circular investment scenario and can be generalized to other industries.

Predictive maintenance (PdM) or Prognostics and Health Management (PHM) assists in better predicting the future state of physical assets and making timely and better-informed maintenance decisions. Many companies nowadays desire the implementation of such an advanced maintenance policy. However, the first step in any implementation of PdM is identifying the most suitable candidates (i.e. systems, components). This is to assess where PdM would provide the greatest benefit in performance and costs of downtime. Although multiple selection methods are available, these methods do not always lead to the most suitable candidates for PdM. The main reason is that these methods mainly focus on critical components without considering the clustering of maintenance, and the technical, economic, and organizational feasibility.This paper proposes a three-stage funnel-based selection method to enhance this process. The first step of the funnel helps to significantly reduce the number of suitable systems or components by a traditional filtering on failure frequency and impact on the firm. In the second and third step, a more in-depth analysis on the remaining candidates is conducted. These steps help to filter potential showstoppers and study the technical and economic feasibility of developing a specific PdM approach for the selected candidates. Finally, the proposed method is successfully demonstrated using two distinct cases: a vessel propulsion system and a canal lock.

Life Cycle Costing (LCC) is a powerful instrument in the Asset Management (AM) of physical systems. It is a valuable tool that can be used to support rational decision making and provide transparency and accountability about various operational, tactical and strategic AM decisions and their expected consequences. However, the application of LCC may not always be straightforward. In order to effectively use LCC in this context, it is important to further the understanding of the main challenges that practitioners face during the application of LCC in AM decision making. This publication summarizes the main descriptions and assumptions found in the literature on LCC into five postulates. These postulates are subsequently compared with empirical findings from four practical case studies within a Dutch utility provider on the use of LCC in AM decision making. Whereas some empirical findings agreed with the postulates, other findings were at least partially in disagreement, indicating inconsistencies between the theory of LCC and the practice of AM. The results suggest fundamental challenges in the use of LCC in AM, specifically regarding the objectives of using LCC and the scope of what is included in the assessment. Practitioners can use this article to avoid the pitfalls of using LCC in the context of AM and researchers can use the findings to guide further work on developing methods and tools that support AM.

Seismocardiography (SCG) have long been proposed as the mechanical counterpart of electrocardiography. However, while the different components of the electrocardiography have been accounted for the SCG seems more complex. Methods that included echocardiography have been used to study the relation between the heart activity and the SCG components. However SCG seems to be a composition of many waves that propagates across the chest, and then is acquired at a single site.We hypothesis that by investigating chest vibrations synchronously from multiple sites we can investigate how waves propagates across the chest. By comparing the waves propagation velocity, direction, epicenters, and timing, we hope to better explain how the SCG from a single site is composed of these waves.A system with up to 16 3-axis highly sensitive accelerometers where developed. This multichannel seismocardiogram (mSCG) could synchronize the measurements of 16 SCG along with a 3-lead ECG. To investigate the chest waves two sensor formations have been proposed. The transverse formation reveals a distinct wave that travels downward from the xiphoid process. The second formation is in a 4 by 4 grid on the chest, which show that the local maximum of the SCG different waves have different centers.We have shown through this method that information about the composition of a single site SCG can be better explained by measuring on multiple sites and by investigating the features of the individual waves. Further studies with this method will be conducted, that will involve echocardiography, to assist in describing the SCG signal composition of chest surface waves.

Consumers currently place increasing importance on the values that companies represent. Modern values such as transparency, benevolence to society, sustainability and fairness are becoming more relevant, as noted by many major consulting firms among which Nielsen (2013), the World Federation of Advertisers (2013) and the Boston Consultancy Group (2013). Modern values are grounded in social, political and economic developments, and represent the new values of this era. As a consequence, a company’s value to consumers no longer has to lie solely in its products and services. This new, broader scope of value may include the entire business process and organizational culture, ranging from the management’s integrity to values being found in the company’s contributions to society. Although the role of values in human behaviour has been extensively discussed in the psychology literature since the beginning of the 1900s (e.g. Feather, 1995; Hofstede, 1980; Olson & Maio, 2003; Rokeach, 1973; Schwartz, 2012), limited attention has been dedicated to values in marketing literature. This was the conclusion of a systematic literature review that we conducted on this subject (Voorn, van der Veen, van Rompay, Hegner & Pruyn, 2016). As a follow up, we organized an online survey (n = 1109) to empirically investigate the role of values in the brand selection process. In this paper, we report on the relationship between values and brand purchase intentions through the concept of value congruence and in relation to several product categories representing services, durables and consumables. Overall, the results confirm the relevance of value congruence as a predictor of brand purchase, in particular in services and durables. Our study shows that companies can benefit from incorporating values into their marketing strategies, especially those values that are congruent with (higher-order) personal goals, rather than more (instrumental) category-specific values. This offers new marketing perspectives, especially for brands. Brands are – by definition – more than just one product or service, which means they can serve as an umbrella for the incorporation and propagation of new values. However, an important question remains for the brand manager: the extent to which values have an advantage over brand personality traits and functional attributes, since investing in values is not only about communication – it means that an organization needs to embody them in the very fibre of its being otherwise it may be perceived as ‘green washing’, which can undermine brand trust.

Asset Life Cycle Management is a strategic approach to managing physical assets over their complete life cycle. However, the literature and the recent ISO 55,000 standard do not offer guidance as to how to develop such an approach. This paper investigates the main capabilities for Asset Life Cycle Management by means of a four year Action Research project implementing Asset Life Cycle Plans. Five main capabilities emerged: 1. strategic information use; 2. alignment of operations and strategy; 3. alignment of different disciplines; 4. a dual time perspective and 5. exerting influence over the assets throughout the entirety of the organization.

Physical assets are essential in manufacturing, and typically have lifetimes of several decades. Over such a period, many changes in goals and context may occur. Such changes should be dealt with to prevent a premature end to the asset’s useful life. However, current maintenance methodologies focus mainly on the short term and on operation in a stable context. Based on a case study of Asset Management practices and the literature, we developed the Lifetime Impact Identification Analysis (LIIA) method. The method uses Technical, Economic, Compliance and Commercial perspectives to identify long-term challenges and opportunities for the asset.

The lifespan to which assets can be efficiently maintained, upgraded or disposed, heavily depends on the characteristics designed into the asset in the design phase. RAMS analysis is a well-established approach often used to reach this target. This approach is however not adequate for handling the complexity of changes and demands placed on nowadays assets. This can lead to reduced performance and unnecessary risk taking. There is a need for a more integral RAMS(SHEEP) perspective including Supportability, Health, Environment, Economics and Politics. Additionally there is often only focused on the asset itself and not on processes supporting the maintenance of an asset. Therefore this chapter does not only give a historic overview on RAMS evolvement, but also aims at answering how the supporting processes can be designed from an integral RAMSSHEEP perspective. We illustrate this by analysing the functional requirements for the Toilet System (TS) of the Sprinter Light Train (SLT).

Effective management of physical assets should deliver maximum business value. Therefore, Asset Management standards such as PAS 55 and ISO 55000 ask for a life cycle approach. However, most existing methods focus only on the short term of the asset's life or the estimation of its remaining life. These methods do not consider alignment to changing corporate objectives in a variable context, nor do they adopt a multidisciplinary perspective. This chapter argues that, to create maximum value, Asset Management should be a multidisciplinary and strategic practice that considers the complete life cycle of the asset: Asset Life Cycle Management. A practical twelve-step approach is presented to develop an Asset Life Cycle Plan (ALCP) in which expert sessions are used to identify the main lifetime impacts that influence the creation of business value from the use of the asset. The steps are illustrated with an example from practice. The chapter concludes that the ALCP supports asset managers in making long-term strategic decisions in a timely and effective manner.

Unexpected failures of physical assets are often stated as the primary operational risk to asset-intensive organizations. Managing these failures is of critical importance to maintenance operations. This study demonstrates the operationalization and application of organizational resilience in a railway pit stop system, in order to identify changes for improvement in managing unexpected failures, using the soft systems methodology. A new ability (ability to inform) has been identified from the field as an important driver for resilient performance. Further research is needed to defend this position.

Advanced maintenance techniques (AMTs) are practices that can help practitioners to make better-informed maintenance decisions, such as ensuring just-in-time maintenance, corporate business planning or lifetime extension of physical assets. These techniques take the current, but preferably also the future, state of assets into account. Although there is much literature on the development of specific techniques, reports on their adoption and use show that only few companies have successfully applied these methods. Guidance is needed on their selection and application. In this chapter, a typology of companies–based on an ongoing multiple-case study–that apply these maintenance techniques is proposed. This typology identifies typical difficulties practitioners experience in applying AMTs. Finally, a four-step procedure is offered with the aim of helping practitioners to overcome the discussed difficulties in the application of AMTs.

Failure mode and effect analysis (FMEA) is an important method for designing and prioritising preventive maintenance activities and is often used as the basis for preventive maintenance planning. Although FMEA was studied extensively, most of the published work so far covers FMEA concept design. Little detailed comparison to industrial practice regarding the application of FMEA can be found in the literature, which is the contribution of this study. This paper summarises the main descriptions and assumptions found in the literature on FMEA into six postulates, and compares the postulates to industrial practice. This was done in a multiple case study conducted at six companies in the process industry. Some postulates were supported by empirical evidence, whereas for others, limited or no support could be found. The results suggest a fundamental problem in the FMEA procedure, namely, the reliance upon expert judgement in general and the reliance upon design engineering expertise for keeping the FMEA up-to-date in particular. Also, a number of operational and information management problems that companies suffer from when conducting an FMEA were identified. Practitioners can use this paper to assess their potential for implementing FMEA and to learn from the insight into the identified pitfalls. Researchers can use the findings to guide further work on improving and developing the FMEA procedures.

Asset Life Cycle Management concerns the effective management of physical assets over their complete lifetimes, typically lasting decades. Therefore, a large number and a wide variety of challenges and opportunities may need to be dealt with proactively, using limited resources. Therefore, we developed a tool to prioritize these lifetime impacts. The test of the tool showed that by assessing the lifetime impacts on their likelihood, impact and required management effort, a list of the highest priority lifetime impacts can be generated as an input for further discussion, which allows the efficient use of limited resources in Asset Life Cycle Management.

Although consumers currently give increasing importance to the values brands represent, limited attention appears to be paid to this subject throughout marketing literature. Therefore, our research addresses the role of values in the brand selection process. Based on a literature study, a conceptual model is presented that incorporates values into the self-congruence theory (Sirgy, 1982) that describes the process by which consumers determine a match between their desires and what brands offer them. A first quantitative survey shows evidence for the relevance of value-congruence for some but not all product categories. We offer several explanations why this might be the case.

We consider the estimation of a structural function which models a nonparametric relationship between a response and an endogenous regressor given an instrument in presence of dependence in the data generating process. Assuming an independent and identically distributed (iid.) sample it has been shown in Johannes and Schwarz [2011] that a least squares estimator based on dimension reduction and thresholding can attain minimax-optimal rates of convergence up to a constant. As this estimation procedure requires an optimal choice of a dimension parameter with regard amongst others to certain characteristics of the unknown structural function we investigate its fully data-driven choice based on a combination of model selection and Lepski’s method inspired by Goldenshluger and Lepski [2011]. For the resulting fully data-driven thresholded least squares estimator a non-asymptotic oracle risk bound is derived by considering either an iid. sample or by dismissing the independence assumption. In both cases the derived risk bounds coincide up to a constant assuming sufficiently weak dependence characterised by a fast decay of the mixing coefficients. Employing the risk bounds the minimax optimality up to constant of the estimator is established over a variety of classes of structural functions.

We consider the problem of estimating the slope parameter in functional linear instrumental regression, where in the presence of an instrument W, i.e., an exogenous random function, a scalar response Y is modeled in dependence of an endogenous random function X. Assuming second order stationarity jointly for X and W a nonparametric estimator of the functional slope parameter and its derivatives is proposed based on an n-sample of (Y,X,W). In this paper the minimax optimal rate of convergence of the estimator is derived assuming that the slope parameter belongs to the well-known Sobolev space of periodic functions. We discuss the cases that the cross-covariance operator associated to the random functions X and W is finitely, infinitely or in some general form smoothing., Comment: arXiv admin note: text overlap with arXiv:0901.4266

We consider non-parametric estimation problems in the presence of dependent data, notably non-parametric regression with random design and non-parametric density estimation. The proposed estimation procedure is based on a dimension reduction. The minimax optimal rate of convergence of the estimator is derived assuming a sufficiently weak dependence characterized by fast decreasing mixing coefficients. We illustrate these results by considering classical smoothness assumptions. However, the proposed estimator requires an optimal choice of a dimension parameter depending on certain characteristics of the function of interest, which are not known in practice. The main issue addressed in our work is an adaptive choice of this dimension parameter combining model selection and Lepski's method. It is inspired by the recent work of Goldenshluger and Lepski (2011). We show that this data-driven estimator can attain the lower risk bound up to a constant provided a fast decay of the mixing coefficients., Comment: 39 pages, 4 figures

This WSI Study gives an overview of the position of solo self-employed in Germany and the Netherlands by examining - in their institutional context - labour market characteristics and social security provisions. The Study has been prepared within the project 'Self-employed without personnel: between freedom and insecurity', funded by the Hans-Böckler-Stiftung. It draws on results from desk research, analyses of existing statistical data, survey research and interviews with self-employed without personnel. The research was conducted by a research team from Utrecht University School of Economics (U.S.E.) in collaboration with the Institute of Economic and Social Research (WSI).

We consider non-parametric estimation problems in the presence of dependent data, notably non-parametric regression with random design and non-parametric density estimation. The proposed estimation procedure is based on a dimension reduction. The minimax optimal rate of convergence of the estimator is derived assuming a sufficiently weak dependence characterized by fast decreasing mixing coefficients. We illustrate these results by considering classical smoothness assumptions. However, the proposed estimator requires an optimal choice of a dimension parameter depending on certain characteristics of the function of interest, which are not known in practice. The main issue addressed in our work is an adaptive choice of this dimension parameter combining model selection and Lepski’s method. It is inspired by the recent work of Goldenshluger and Lepski [2011]. We show that this data-driven estimator can attain the lower risk bound up to a constant provided a fast decay of the mixing coefficients.

Failure Mode and Effects Analysis (FMEA) is commonly used for designing maintenance routines by analysing potential failures, predicting their effect and facilitating preventive action. It is used to make decisions on operational and capital expenditure. The literature has reported that despite its popularity, the FMEA method lacks transparency, repeatability and the ability to continuously improve maintenance routines. In this paper an enhancement to the FMEA method is proposed, which enables the probability of asset failure to be expressed as a function of explanatory variables, such as age, operating conditions or process measurements. The probability of failure and an estimate of the total costs can be used to determine maintenance routines. The procedure facilitates continuous improvement as the dataset builds up. The proposed method is illustrated through two datasets on failures. The first was based on an operating company exploiting a major gas field in the Netherlands. The second was retrieved from the public record and covers degradation occurrences of nuclear power plants in the United States.

In this article the use of asset information standards for collaboration in the process industry is reviewed based on a survey of the literature and two case studies. The investigation shows that the process industry appears to have had only limited success in introducing such standards so far, despite significant efforts. Since information hand-over between asset life cycle phases is important, lack of information standardization suggests that collaboration costs are higher than necessary. Reported causes can be grouped into standard related causes (slow development of standards, stability, complexity, cost, quality/ontological problems), organization related causes (lack of direct financial incentives, organizational readiness, resistance to change) and business environment related causes (legal aspects, level of adoption, limited governmental enforcement and a lack of dominant actors in the process industry). It is also shown that initial local configuration of a standard may lead to successful acceptance of the standards, but may hinder later external use. The contribution of this article is insight into the use of asset information standards and the causes for lack of pervasiveness. This is necessary for improving the use of standards in collaboration in the process industry. The article concludes by suggesting future research directions. (C) 2010 Elsevier B.V. All rights reserved.