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This editorial discusses the expanding role of paper as a platform on which to build new point-of-care assays, particularly those intended for use in resource-limited settings. Successful diagnostics for use in these environments require a low-cost platform (possibly paper) as well as new assay strategies, reagents and materials for achieving selectivity and sensitivity. Paper provides a common platform for bringing these components together and serves as a low-cost medium for prototyping new point-of-care assays. [ABSTRACT FROM AUTHOR]

Manufacturing companies are striving hard to remain competitive hence, they rely on a number of resources to meet customers’ expectations, among which cutting tools are included. This paper addresses the problems faced in the management of cutting tools activities. Production managers have highlighted the lack of procedures containing metrics and targets that would show them whether their company is able to perform an efficient management, and if it is capable of supporting the deployment process. In this context, this paper presents a novel lean environmental benchmarking (LEB) method for performing a diagnosis of practices and performances to support the implementation of a cutting tool management strategy and/or the effective management of these assets. Strategic, technical and logistical aspects are addressed, particularly, with regard to management focused on lean manufacturing and environmental aspects. Field studies were performed in nine Brazilian companies in the metal-mechanical sector to validate the LEB method proposed. The LEB method helped the participant organisations to clarify the various activities that involved the management of their cutting tools, while the field studies indicated that all nine organisations had a great concern regarding the preservations of the environment, and also an effective utilisation of resources spent for machining components. [ABSTRACT FROM AUTHOR]

Despite considerable advances over the last two decades in sensing instrumentation and information technology infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. This is in spite of numerous studies reporting that the expected savings from widespread deployment of condition-based maintenance (CBM) technology would be in the tens of billions of dollars in many industrial sectors as well as in governmental agencies. It turns out that a prerequisite to widespread deployment of CBM technology and practice in industry is cost efficient and effective diagnostics and prognostics. This paper presents a novel method for employing hidden Markov models (HMMs) for carrying out both diagnostic as well as prognostic activities for metal cutting tools. The methods employ HMMs for modelling sensor signals emanating from the machine (or features thereof), and in turn, identify the health state of the cutting tool as well as facilitate estimation of remaining useful life. This paper also investigates some of the underlying issues of proper HMM design and training for the express purpose of effective diagnostics and prognostics. The proposed methods were validated on a physical test-bed, a vertical drilling machine. Experimental results are very promising. [ABSTRACT FROM AUTHOR]

In the last few years, the study of the surface color of historical buildings has progressed as part of conservation and restoration efforts to comply with the aesthetics of historical and pre-existing materials. The main critical element related to this type of study is represented by the succession of various interventions of restoration and maintenance over time that has altered the original aesthetics of monuments by using materials and colors selected according to criteria, trends, and needs of succeeding historical eras. In this paper, the investigation of plasters and colored finishing layers applied on two historical buildings located in Rome and dating to the early 1900s is reported. Results were obtained through a specific diagnostic investigation plan focused on microscopic, morphological, and chemical characterizations of plasters and colored layers detected on the monument surfaces. All post-intervention phases and dates have been identified by comparing the results obtained with historical data. Important information was uncovered that helps in tracing the original aesthetic conception of the buildings and in planning restoration approaches which can be compatible both aesthetically and materially with the original work. [ABSTRACT FROM AUTHOR]

Children with gifted intellects often earn lower scores on measures of processing speed than their nongifted counterparts. However, neuropsychological research indicates such a profile of scores is likely not due to a true innate neurocognitive processing speed deficit but is rather a statistical artifact resulting from the interference of common cognitive and behavioral idiosyncrasies inherent to giftedness, such as perfectionism and an aversion to completing paper and pencil tasks. The first aim of this study is to identify and explain the relative underperformance of children with gifted intellects on measures of processing speed. The second aim is to develop a neuropsychologically informed framework for clinically assessing processing speed in children with gifted intellects using alternative measures from the WISC–V. Clinical implications are discussed as they relate to the relative disabling effects of common gifted-related idiosyncrasies on the expression of their otherwise intact neuropsychological capabilities. [ABSTRACT FROM AUTHOR]

Introduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics. Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic. Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed. [ABSTRACT FROM AUTHOR]

Modern manufacturing systems are often installed with sensor networks which generate high-dimensional data at high velocity. These data streams offer valuable information about the industrial system's real-time performance. If a shift occurs in the manufacturing process, fault diagnosis based on the data streams becomes a fundamental task as it identifies the affected data streams and provides insights into the root cause. Existing fault diagnostic methods either ignore the correlation between different streams or fail to determine the shift directions. In this paper, we propose a directional fault classification procedure that incorporates the between-stream correlations. We suggest a three-state hidden Markov model that captures the correlation structure and enables inference about the shift direction. We show that our procedure is optimal in the sense that it minimizes the expected number of false discoveries while controlling the proportion of missed signals at a desired level. We also propose a deconvolution-expectation-maximization (DEM) algorithm for estimating the model parameters and establish the asymptotic optimality for the data-driven version of our procedure. Numerical comparisons with an existing approach and an application to a semiconductor production study show that the proposed procedure works well in practice. [ABSTRACT FROM AUTHOR]

A new paradigm of emergency R&D has transformed global health. Beginning with the 2014–2016 Ebola virus disease epidemic in West Africa, experimental product development has been propelled to the frontlines of outbreak response, radically compressing timelines and unsettling regulatory standards, biosecurity strategies and humanitarian protocols. This paper examines these emerging epistemic practices and ethical norms as they played out in the creation of rapid diagnostic tests for Ebola, Zika and COVID-19. In each of these viral public health crises, new platforms for quick detection have been the principal load-bearing pillar of outbreak response, and the effort to speed up their development illuminates the fragile set of accommodations between public health needs and commercial interests that obtain under conditions of emergency. The World Health Organization's role in stimulating and coordinating the development of these tools provides our analytical through-line, and reveals, we argue, the limitations of an accelerationist model of global health innovation organized around the concept of 'market failure'. The evolution of this paradigm of 'emergency R&D' into a permanent feature of pandemic preparedness will further narrow our imagination of how global health goods should be construed and provided. [ABSTRACT FROM AUTHOR]

Breast cancer (BCa) is the most common cancer type diagnosed in women and 5th most common cause of deaths among all cancer deaths despite the fact that screening program is at place. This is why novel diagnostics approaches are needed in order to decrease number of BCa cases and disease mortality. In this review paper, we aim to cover some basic aspects regarding cellular metabolism and signalling in BCa behind altered glycosylation. We also discuss novel exciting discoveries regarding glycan-based analysis, which can provide useful information for better understanding of the disease. The final part deals with clinical usefulness of glycan-based biomarkers and the clinical performance of such biomarkers is compared to already approved BCa biomarkers and diagnostic tools based on imaging. Recent discoveries suggest that glycan-based biomarkers offer high accuracy for possible BCa diagnostics in blood, but also for better monitoring and management of BCa patients. The review article was written using Web of Science search engine to include articles published between 2019 and 2021. [ABSTRACT FROM AUTHOR]

This review is directed at physicians and laboratory personnel in private practice and clinics who treat and diagnose Lyme borreliosis (LB) in patients as part of their daily work. A major objective of this paper is to bring together background information on Borrelia (B.) burgdorferi sensu lato (s.l.) and basic clinical knowledge of LB, which is one of the most frequently reported vector-borne diseases in the Northern Hemisphere. The goal is to provide practical guidance for clinicians and for laboratory physicians, and scientists for a better understanding of current achievements and ongoing obstacles in the laboratory diagnosis of LB, an infectious disease that still remains one of the diagnostic chameleons of modern clinical medicine. Moreover, in bringing together current scientific information from guidelines, reviews, and original papers, this review provides recommendations for selecting the appropriate tests in relation to the patient’s stage of disease to achieve effective, stage-related application of current direct and indirect laboratory methods for the detection of B. burgdorferi s.l. Additionally, the review aims to discuss the current state of the art concerning the diagnostic potential and limitations of the assays and test methods currently in use to optimize LB patient management and provide insight into the possible future prospects of this rapidly changing area of laboratory medicine. [ABSTRACT FROM AUTHOR]

Introduction: Globally, both communicable and non-communicable diseases pose a serious threat to populations in developed as well as developing countries. Access to reliable diagnostic testing along with qualified health practitioners is severely limited in low resource and very remote areas and following natural catastrophes. Areas covered: This paper provides an overview of the challenges involved and suggests strategies to address them. The emergence of more robust, user-friendly, cost-effective and ‘sample-to-result’ point-of-care (POC) tools, along with the proliferation of mobile technologies, may provide a practical approach in addressing some of the challenges. Expert commentary: The successful implementation of POC testing requires the availability of versatile diagnostic technologies, improved platforms and back-up infrastructure, successful leveraging of human resources through training and, finally, engagement/coordination of associated stakeholders, including public health agencies, diagnostics companies, healthcare practitioners and local rural authorities. [ABSTRACT FROM PUBLISHER]

Industrial components and systems undergo degradation in process operations. Prognostics and health management (PHM) is a process to assess and predict health conditions of components and can be applied to condition-based monitoring and maintenance. PHM is commonly utilized to analyze the health condition of a single lifecycle until failure. When maintenance occurs, degradation can be removed, and the PHM model can be restarted with new parameters related to the expected postmaintenance conditions. Maintenance actions, mostly imperfect repairs, may not entirely reset the condition to as good as new, and further degradation may occur at a higher rate. Maintenance repairs should be considered in prognostic models to predict component health more accurately. Furthermore, processes typically have more than one component that degrade and influence process measurements. The dependence of process measurements to multiple fault modes and related degradation can make individual component health monitoring complex. Commonly, faults and their related effects on process parameters must be isolated. In these cases, the diagnostics and prognostics framework should handle unsynchronized failure and maintenance reinitialization of different components for multiple fault processes. This research paper presents the Maintenance-Dependent Monitoring and Prognostics Model (MDMPM) to detect anomalies, decouple faults for different components, and predict future health conditions to calculate remaining useful life (RUL). The model is demonstrated with semisimulated nuclear power plant (NPP) data, with simultaneous condenser pump degradation and condenser tube fouling. The MDMPM shows a reliable prediction of RULs of NPP maintenance-dependent processes with interacting component degradation modes. [ABSTRACT FROM AUTHOR]

Introduction: Sensorineural hearing impairment (SNHI), a common childhood disorder with heterogeneous genetic causes, can lead to delayed language development and psychosocial problems. Next-generation sequencing (NGS) offers high-throughput screening and high-sensitivity detection of genetic etiologies of SNHI, enabling clinicians to make informed medical decisions, provide tailored treatments, and improve prognostic outcomes. Areas covered: This review covers the diverse etiologies of HHI and the utility of different NGS modalities (targeted sequencing and whole exome/genome sequencing), and includes HHI-related studies on newborn screening, genetic counseling, prognostic prediction, and personalized treatment. Challenges such as the trade-off between cost and diagnostic yield, detection of structural variants, and exploration of the non-coding genome are also highlighted. Expert opinion: In the current landscape of NGS-based diagnostics for HHI, there are both challenges (e.g. detection of structural variants and non-coding genome variants) and opportunities (e.g. the emergence of medical artificial intelligence tools). The authors advocate the use of technological advances such as long-read sequencing for structural variant detection, multi-omics analysis for non-coding variant exploration, and medical artificial intelligence for pathogenicity assessment and outcome prediction. By integrating these innovations into clinical practice, precision medicine in the diagnosis and management of HHI can be further improved. [ABSTRACT FROM AUTHOR]

A new Phase II control chart, which is based on the change-point model and combined with the exponentially weighted moving average (EWMA) mechanism, is proposed to monitor general linear profiles. The new control chart can be used to monitor general linear profiles when the true in-control parameters are unknown, and only a few historical data are available. In addition, when the chart triggers an out-of-control signal, not only can it estimate the location of the change point, it can also identify which of the parameters have changed as well as the change directions. Using Monte-Carlo simulations, the proposed chart is shown to be effective and has good diagnostic performance. Furthermore, the simulation results show that the proposed chart has better performance than the existing charts in most of the out-of-control scenarios considered. An example is used to illustrate how the proposed chart can be implemented in practical applications. [ABSTRACT FROM AUTHOR]

Operators' work order descriptions in computerized maintenance management systems (CMMS) represent an untapped opportunity to benchmark a facility's maintenance and operation performance. However, it is challenging to carry out analytics on these large and amorphous databases. This paper puts forward a text-mining method to extract information about failure patterns in building systems and components from CMMS databases. The method is executed in three steps. Step 1 is pre-processing to convert work order descriptions into a mathematical form that lends itself to a quantitative lexical analysis. Step 2 is clustering to focus on interesting sections of a CMMS database that contain work orders about failures in building systems and components - rather than less interesting routine maintenance and inspection activities. Step 3 is association rule-mining to identify the coexistence tendencies among the terms of cluster of interest (e.g. coexistence of the terms 'radiator' and 'leak'). This text-mining method is demonstrated by using two data sets. One data set was from a central heating and cooling plant with four boilers and five chillers; the other data set was from a cluster of 44 buildings. The results provide insights into per equipment breakdown of failure events, top system and component-level failure modes, and their occurrence frequencies. [ABSTRACT FROM AUTHOR]

A method is proposed in this paper to assess the local influence of minor perturbations for the Sharpe model when the normal distribution is replaced by normal/independent (NI) distributions. The family of NI distributions is an attractive class of symmetric heavy-tailed densities that includes as special cases the normal, t-Student, slash, and the contaminated normal distributions. Since the returns of the market are not observable, the statistical analysis is carried out in the context of an errors-in-variables model. An influence analysis for detecting influential observations (atypical returns) is developed to investigate the sensitivity of the maximum likelihood estimators. Diagnostic measures are obtained based on the conditional expectation of the complete-data log-likelihood function. The results are illustrated by using a set of shares of companies traded in the Chilean stock market. [ABSTRACT FROM AUTHOR]

Introduction: Prostate cancer (PCa) is a life-threatening disease affecting millions of men. The current best PCa biomarker (level of prostate-specific antigen in serum) lacks specificity for PCa diagnostics and this is why novel PCa biomarkers in addition to the conventional ones based on biomolecules such as DNA, RNA and proteins need to be identified. Areas covered: This review details the potential of glycans-based biomarkers to become diagnostic, prognostic, predictive and therapeutic PCa biomarkers with a brief description of the innovative approaches applied to glycan analysis to date. Finally, the review covers the possibility to use exosomes as a rich source of glycans for future innovative and advanced diagnostics of PCa. The review covers updates in the field since 2016. Expert commentary: The summary provided in this review paper suggests that glycan-based biomarkers can offer high-assay accuracy not only for diagnostic purposes but also for monitoring/surveillance of the PCa disease. [ABSTRACT FROM AUTHOR]

The rapidly increasing range and expense of new diagnostics, compels consideration of a different, more proactive approach to health economic evaluation of diagnostic technologies. Early cycle economic evaluation is a decision analytic approach to evaluate technologies in development so as to increase the return on investment as well as patient and societal impact. This paper describes examples of 'early cycle economic evaluations' as applied to diagnostic technologies and highlights challenges in its real-time application. It shows that especially in the field of diagnostics, with rapid technological developments and a changing regulatory climate, early cycle economic evaluation can have a guiding role to improve the efficiency of the diagnostics innovation process. In the next five years the attention will move beyond the methodological and analytic challenges of early cycle economic evaluation towards the challenge of effectively applying it to improve diagnostic research and development and patient value. Future work in this area should therefore be 'strong on principles and soft on metrics', that is, the metrics that resonate most clearly with the various decision makers in this field. [ABSTRACT FROM AUTHOR]

Biotechnology embraces various physical and chemical phenomena toward advancement of health diagnostics. Toward such advancement, detection of toxins plays an important role. Toxins produce severe health impacts on consumption with high mortality associated in acute cases. The most prominent route of infection and intoxication is through food matrices. Therefore, rapid detection of toxins at low concentrations is the need of modern diagnostics. Lateral flow immunoassays are one of the emergent and popularly used rapid detection technology developed for detecting various kinds of analytes. This review thus focuses on recent advancements in lateral flow immunoassays for detecting different toxins in agricultural food. Appropriate emphasis was given on how the labels, recognition elements, or detection strategy has laid an impact on improvement in immunochromatographic assays for toxins. The paper also discusses the gradual change in sensitivities and specificities of assays in accordance with the method of food processing used. The review concludes with the major challenges faced by this technology and provides an outlook and insight of ideas to improve it in the future. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new semiparametric heteroscedastic regression model allowing for positive and negative skewness and bimodal shapes using the B-spline basis for nonlinear effects. The proposed distribution is based on the generalized additive models for location, scale and shape framework in order to model any or all parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. We motivate the new model by means of Monte Carlo simulations, thus ignoring the skewness and bimodality of the random errors in semiparametric regression models, which may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. An iterative estimation process and some diagnostic methods are investigated. Applications to two real data sets are presented and the method is compared to the usual regression methods. [ABSTRACT FROM AUTHOR]

This paper describes small area estimation (SAE) of proportions under a spatial dependent generalized linear mixed model using aggregated level data. The SAE is also applied to produce reliable district level estimates and mapping of incidence of indebtedness in the State of Uttar Pradesh in India using debt and investment survey data collected by National Sample Survey Office (NSSO) and the secondary data from the Census. The results show a significant improvement in precision of model-based estimates generated by SAE as compared to direct estimates. The estimates generated by incorporating spatial information are more efficient than the one generated by ignoring this information. [ABSTRACT FROM AUTHOR]

Thanks to the capabilities of distributed fibre optic sensors (DFOS), a significant increase in their applications within civil and geotechnical engineering has been observed during the last few years. In contrast to conventional spot gauges, the main advantage of this technology is direct damage detection, as the information is gained over the entire measurement path. This diagnostic solution is especially suitable for safety-critical infrastructure, including bridges. However, to utilise the benefits of the DFOS approach, appropriate interrogators, sensors, and installation methods must be adopted. The article briefly discusses possible solutions available on the market, indicating their specific advantages and limitations. Then, several practical case studies related to bridge monitoring in Poland are presented. The review shows various possible applications, from long measurement sections over entire spans to small-size structural components. Some are truly new and pioneer solutions, solving different technical problems in bridge engineering. Presented applications supported by in situ data proved the possible high-quality performance of DFOS-based systems for measurements of concrete, steel and composite bridges in actual operating conditions. [ABSTRACT FROM AUTHOR]

In this paper we consider Sharpe's single-index model or Sharpe's model, by assuming that the returns obtained follow a multivariate t elliptical distribution. Also, given that the returns of the market are not observable, the statistical analysis was made in the context of an errors-in-variables model. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators the local influence method [10] was implemented. The results are illustrated by using a set of shares of companies belonging to the Chilean Stock Market. The main conclusion is that the t model with small degrees of freedom is able to incorporate possible outliers and influential returns in the data. [ABSTRACT FROM AUTHOR]

Generally, the primary structures of buildings perform well throughout their lifetime. However, there is still considerable expenditure on both building maintenance and the consequential damage caused by failures associated with building envelopes. This paper presents the main causal factors of building envelope defects and the forensic process used to investigate these defects. It then explores a number of case studies where forensic investigation, along with an in-depth understanding of the building envelope, its materials and the interaction of the building envelope with the supporting structure, was key in determining the cause of the underlying defects. [ABSTRACT FROM AUTHOR]

The class of nonlinear reproductive dispersion mixed models (NRDMMs) is an extension of nonlinear reproductive dispersion models and generalized linear mixed models. This paper discusses the influence analysis of the model based on Laplace approximation. The equivalence of case-deletion models and mean-shift outlier models in NRDMMs is investigated, and some diagnostic measures are proposed via the case-deletion method. We also investigate the assessment of local influence of various perturbation schemes. The proposed method is illustrated with an example. [ABSTRACT FROM AUTHOR]

Student mathematics performance and the need for work-ready graduates to be mathematics-competent is a core issue for many universities. While both student and teacher are responsible for learning outcomes, there is a need to explicitly acknowledge the weak mathematics foundation of many university students. A systematic literature review was undertaken of identified innovations and/or interventions that may lead to improvement in student outcomes for university mathematics-based units of study. The review revealed the importance of understanding the foundations of student performance in higher education mathematics learning, especially in first year. Pre-university mathematics skills were identified as significant in student retention and mathematics success at university, and a specific focus on student pre-university mathematics skill level was found to be more effective in providing help, rather than simply focusing on a particular at-risk group. Diagnostics tools were found to be important in identifying (1) student background and (2) appropriate intervention. The studies highlighted the importance of appropriate and validated interventions in mathematics teaching and learning, and the need to improve the learning model for mathematics-based subjects, communication and technology innovations. [ABSTRACT FROM AUTHOR]

Context:Novel biomarkers are essential for developing rapid diagnostics and therapeutic interventions Objective:This review aimed to highlight biomarker characterisation and assessment of unique bacterial pili. Methods:A PubMed search for bacterial pili, diagnostics, vaccine and therapeutics was performed, with emphasis on the well characterised pili. Results:In total, 46 papers were identified and reviewed. Conclusion:Extensive analyses of pili enabled by advanced nanotechnology and whole genome sequencing provide evidence that they are strong biomarker candidates.Mycobacterium tuberculosiscurli pili are emphasised as important epitopes for the development of much needed point-of-care diagnostics and therapeutics. [ABSTRACT FROM PUBLISHER]

Ridge penalized least-squares estimators has been suggested as an alternative to the minimum penalized sum of squares estimates in the presence of collinearity among the explanatory variables in semiparametric regression models (SPRMs). This paper studies the local influence of minor perturbations on the ridge estimates in the SPRM. The diagnostics under the perturbation of ridge penalized sum of squares, response variable, explanatory variables and ridge parameter are considered. Some local influence diagnostics are given. A Monte Carlo simulation study and a real example are used to illustrate the proposed perturbations. [ABSTRACT FROM AUTHOR]

Introduction:Polymerase chain reaction (PCR) has emerged as a promising technology for the rapid and reliable detection and identification of medical mycoses. Recent technological advancements – including microarray, multiplex PCR with magnetic resonance, and beacon probes – have mitigated the technical difficulties of performing nucleic amplification in fungi, thereby improving the sensitivity and specificity of PCR-based assays. In this paper, we examine current applications of PCR in the diagnosis of human fungal infections and look ahead to emerging techniques that may play a larger role in molecular diagnostics in the future. Areas covered:This review includes a brief overview of the advantages and disadvantages of PCR using various clinical specimens, manual versus automated DNA extraction procedures, panfungal versus specific targets, and spectrum of pathogens detected. This is followed by a brief synopsis of species-specific PCR approaches and a more in-depth look at the obstacles to widespread implementation. Expert commentary:The review concludes with a short perspective for the next five years, including the hurdles to standardization and validation, as well as the role of PCR coupled with electrospray-ionization mass spectrometry (PCR/ESI-MS) or nuclear magnetic resonance for the diagnosis of medical mycoses. [ABSTRACT FROM PUBLISHER]

The striking prevalence of HIV, TB and malaria, as well as outbreaks of emerging infectious diseases, such as influenza A (H7N9), Ebola and MERS, poses great challenges for patient care in resource-limited settings (RLS). However, advanced diagnostic technologies cannot be implemented in RLS largely due to economic constraints. Simple and inexpensive point-of-care (POC) diagnostics, which rely less on environmental context and operator training, have thus been extensively studied to achieve early diagnosis and treatment monitoring in non-laboratory settings. Despite great input from material science, biomedical engineering and nanotechnology for developing POC diagnostics, significant technical challenges are yet to be overcome. Summarized here are the technical challenges associated with POC diagnostics from a RLS perspective and the latest advances in addressing these challenges are reviewed. [ABSTRACT FROM PUBLISHER]

Breaking Bread: A Dementia Awareness Café was a theatre installation held on the forecourt of a busy city café in regional Victoria. The aim of the work was to reduce the fear and stigma associated with dementia by engaging an audience in an imaginative, participatory artwork, which challenged prejudice inherent within a medical term used to diagnose dementia. Included in the installation was the Loss and Nourishment Book, a collection of edited interviews with people experiencing the effects of dementia. The installation was successful in attracting a large and diverse audience, and in fostering conversation about dementia in a non-threatening manner. [ABSTRACT FROM AUTHOR]

Partially linear models are extensions of linear models that include a nonparametric function of some covariate allowing an adequate and more flexible handling of explanatory variables than in linear models. The difference-based estimation in partially linear models is an approach designed to estimate parametric component by using the ordinary least squares estimator after removing the nonparametric component from the model by differencing. However, it is known that least squares estimates do not provide useful information for the majority of data when the error distribution is not normal, particularly when the errors are heavy-tailed and when outliers are present in the dataset. This paper aims to find an outlier-resistant fit that represents the information in the majority of the data by robustly estimating the parametric and the nonparametric components of the partially linear model. Simulations and a real data example are used to illustrate the feasibility of the proposed methodology and to compare it with the classical difference-based estimator when outliers exist. [ABSTRACT FROM AUTHOR]

In recent years, exciting developments in disease modifying treatments for Alzheimer's disease (AD) have made accurate and timely diagnosis of this disease a priority. Blood biomarkers (BBMs) for amyloid pathology using improved immunoassay and mass spectrometry techniques have been an area of intense research for the last 10 years and are coming to the fore, as a real prospect to be used in the clinical diagnostics of the disease. The following review will update and discuss blood biomarkers that will be most useful in diagnosing AD and the context necessary for their implementation. It is clear we now have BBMs, and technology to measure them, that are capable of detecting amyloid pathology in AD. The challenge is to validate them across platforms and populations to incorporate them into clinical practice. It is important that implementation comes with education, we need to give clinicians the tools for appropriate use and interpretation. It is feasible that BBMs will be used to screen populations, initially for clinical trial entry but also therapeutic intervention in the foreseeable future. We now need to focus BBM research on other pathologies to ensure we accelerate the development of therapeutics for all neurodegenerative diseases [ABSTRACT FROM AUTHOR]

A vibration sensor as a part of the IMES GmbH diagnostic systems provides enhanced diagnostic capabilities for marine engines. Although direct measurements of the fuel injection and valve train system parameters could give complete diagnostic information about their technical conditions, such measurements usually are forbidden at navigation. Application of the vibration sensor provides reasonable alternative to direct measurements as it enables detection of the injector's needle rise and landing timing, as well as the timing of the start of a fuel delivery and fuel cut-off by the high-pressure fuel injection pump. The heated heavy fuel oil circulation opening and closing timing together with the intake and exhaust valves closing and, for some cases, opening timing could also be measured by vibration sensor. This article shows examples of diagnostics of the fuel injection and the valve-train system various malfunctions using vibration diagrams and a mathematical simulation of the engine operating process. [ABSTRACT FROM AUTHOR]

This paper presents a diagnostic technique using wavelets, kurtosis and skewness for detecting crack location and severity in beams. The first three mode shapes of a damaged beam are used for detecting crack locations in beams with varying boundary conditions. Damage due to an edge-crack is modelled by a conventional macroscopic torsional spring element. Multiple families of wavelets with varying scales and vanishing moments are used so as to distinguish between their ability to detect crack locations by using the mode shapes of the damaged beam. Crack detection is followed by determination of the crack size by using statistical measures of the mode shapes such as kurtosis and skewness. These two measures are highly sensitive to small changes in mode shapes resulting from crack initiation or crack propagation, and have been used in other fields of damage detection. Several simulations are presented to showcase the application of the proposed technique, and to highlight the differences between multiple wavelets and the parameters associated with the wavelets. The proposed diagnostic technique is also tested for robustness by adding noise to the mode shapes. The simulation results and preliminary experimental results indicate that the statistical measures of kurtosis and skewness can be used in conjunction with wavelets in order to provide a viable crack diagnostic technique for beam structures. [ABSTRACT FROM AUTHOR]

In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Brake systems in trucks are crucial for ensuring the safety of vehicles and passengers on the roadways. Most trucks in the USA are equipped with S-cam drum brake systems and they are sensitive to maintenance. Brake deficiencies such as leaks and out-of-adjustment of the pushrod are a major cause of accidents involving trucks. Leaks in the air brake systems drastically affect braking performance by decreasing the maximum attainable braking pressure and also increasing the time required to attain the same, thereby resulting in longer stopping distances. Out-of-adjustment of the pushrod leads to loss of braking torque even if no leaks are present in the air brake system. In this paper, we present a mathematical model for an air brake system in the presence of leaks, with a view towards developing a diagnostic system for the air brake system based on the models. Additionally, we present a scheme that estimates the severity of leak in terms of the mass flow rate of air leaking from the air brake system to the atmosphere. This scheme can be implemented using a simple look-up table. We also present a steady-state pushrod stroke estimation scheme, based on brake chamber pressure measurements in the absence of any leaks in the air brake system. [ABSTRACT FROM AUTHOR]

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In this paper we consider the Capital Asset Pricing Model under Elliptical (symmetric) Distributions. This class of distributions, which contains the normal distribution, t, contaminated normal and power exponential, among others, offers a more flexible framework for modelling asset prices or returns. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators, the local influence method was implemented. The results are illustrated by using a set of shares from companies who trade in the Chilean Stock Market. Our main conclusion is that symmetric distributions having heavier tails than those of the normal distribution, especially the t distribution with small degrees of freedom, show a better fit and allow the reduction of the influence of atypical returns in the maximum likelihood estimators. [ABSTRACT FROM AUTHOR]

Endoluminal microscopy and spectroscopy could significantly improve the efficiency of clinical endoscopic examination by allowing in-situ detection, staging and grading of potentially cancerous lesions. Indeed, high-end optical microscopy techniques such as confocal, coherence-gated and single-/multi-photon microscopy today deliver optical histology information and spectrally/spatially resolved measurements of tissue reflectance allow grading and staging of precancerous/cancerous lesions. Owing to the brisk development of MEMS technologies, miniaturization requirements satisfying the dimension requirements for endoscope integration have been met within the last decade and the present paper will report on the current and future development of MEMS-based endoscopes for optical diagnosis. [ABSTRACT FROM AUTHOR]

Bivariate claim data come from a population that consists of insureds who may claim either one, both or none of the two types of benefits covered by a policy. In the present paper, we develop a statistical procedure to fit bivariate distributions of claims in presence of covariates. This allows for a more accurate study of insureds' choice and size in the frequency and severity of the two types of claims. A generalised logistic model is employed to examine the frequency probabilities, whilst the three parameter Burr distribution is suggested to model the underlying severity distributions. The bivariate copula model is exploited in such a way that it allows us to adjust for a range of frequency dependence structures; a method for assessing the adequacy of the fitted severity model is outlined. A health claims dataset illustrates the methods; we describe the use of orthogonal polynomials for characterising the relationship between age and the frequency and severity models. [ABSTRACT FROM AUTHOR]

We describe in this paper a new approach for plant monitoring and diagnostics using type-2 fuzzy logic and fractal theory. The concept of the fractal dimension is used to measure the complexity of the time series of relevant variables for the process. A set of type-2 fuzzy rules is used to represent the knowledge for monitoring the process. In the type-2 fuzzy rules, the fractal dimension is used as a linguistic variable to help in recognizing specific patterns in the measured data. The fuzzy-fractal approach has been applied before in problems of financial time series prediction and for other types of problems, but now it is proposed to the monitoring of plants using type-2 fuzzy logic. We also compare the results of the type-2 fuzzy logic approach with the results of using only a traditional type-1 fuzzy logic approach. Experimental results show a significant improvement in the monitoring ability with the type-2 fuzzy logic approach. [ABSTRACT FROM AUTHOR]

We describe in this paper a new hybrid fuzzy-fractal approach for plant monitoring. We use the concept of the fractal dimension to measure the complexity of a time series of observed data from the plant. We also use fuzzy logic to represent expert knowledge on monitoring the process in the plant. In the hybrid fuzzy-fractal approach a set of fuzzy if-then rules are used to classify different conditions of the plant. The fractal dimension is used as input linguistic variable in the fuzzy system to improve the accuracy in the classification. An implementation of the proposed approach is shown to describe in more detail the method. [ABSTRACT FROM AUTHOR]

The present editorial introduces selected research for a special issue covering the 15th Annual Meeting of the European Society for Traumatic Stress Studies (ESTSS) 2017, 'Child Maltreatment across the Lifespan'. Unfortunately, childhood maltreatment is highly prevalent around the world. Exposure to child maltreatment is associated with a considerable risk of developing a wide range of psychopathologies. Thus, preventing child maltreatment and offering early treatment to victims is an important and promising area in reducing the risk of psychopathology. The 10 articles included in the special issue focus on different important areas related to the topic of child maltreatment. These include a biological and systemic understanding of maltreatment, a globalization perspective, and contributions on diagnosis and treatment, with implications for clinical practice. Furthermore, the special issue includes an update on ICD-11 and implications associated with the implementation of ICD-11. Future research recommendations are provided to underline the enduring need to promote and undertake research into all aspects of child maltreatment. [ABSTRACT FROM AUTHOR]

At present, most commercial kits for nucleic acid isolation are silica-based. In this regard, new strategies are under development with the aim of optimizing nucleic acid purification silica-based protocols. This review aims to summarize the evolution of such strategies and highlight the current necessities to be solved by new purification methods based on silica materials. In addition, it will perform a survey of the purified DNA/RNA quality and quantity for a subsequent technique. This information is of vital importance to consider for the success of molecular diagnostics. The text was designed to guide scientists and engineers in choosing suitable DNA/RNA purification techniques for research or bioprocessing applications. [ABSTRACT FROM AUTHOR]

Liassodites obsti gen. et sp. nov., an Early Jurassic member of Trachypachidae, is described from Western Pomerania (Germany). An epoxy resin transfer method is applied for the preparation of three-dimensionally preserved fossil. An annotated list of Palaeozoic and Mesozoic trachypachid genera was elaborated. For each trachypachid fossil member and erroneously placed genus, a short diagnosis or available arguments on systematic position are proposed. The relations of the subfamilies Eodromeinae and Coptoclaviscinae (Coptoclavidae) are discussed. The description of Liassodites obsti gen. et sp. nov. makes it possible to suppose that the currently recognised hiatus between families Carabidae and Trachypachidae is not great enough to regard them as two separate families. The genus Coptoclavisca should be regarded as a member of Trachypachidae or Carabidae (including Trachypachinae). The new genus Apermunda gen. nov. is proposed for Petrodromeus minor. Novunda nom. nov. is proposed for Unda and the following binominal names are changed: Novunda angulata comb. nov., N. cursoria comb. nov., N. microplata comb. nov., N. pachycephala comb. nov. and N. chifengensis comb. nov. A synonymy of Evertus and Fortiseode is established, with change of species name: Evertus pervalimand comb. nov. The taxa Beipiaocarabus, Leptopodocoleus (including Leptopodocoleidae), Praedodromeus, Procalosoma, Xinbinia, 'Eodromeus' viriosus, 'Unda' chonggezhuangensis and 'Unda' pandurata, 'Karadromeus' xiangfanggouensis are regarded as incertae sedis. [ABSTRACT FROM AUTHOR]

As data acquisition and processing technologies continue to advance rapidly, new challenges emerge for statistical process monitoring. One such challenge, especially in the era of big data analytics, is monitoring multivariate processes involving a mixture of continuous, categorical, and discrete quality variables. The existing multivariate control charts focus mostly on monitoring correlated variables of the same type. We propose a new Phase II control chart that is based on a modified Holm's step-down multiple testing procedure (Holm 1979) which achieves two important goals at the same time: (1) it simultaneously monitors correlated variables of different types, while keeping the probability of false alarm under desirable level, and (2) when the process is determined to be out of control, it further provides, without any additional efforts, diagnostics to pinpoint which parameters are out of control. The proposed chart is shown to outperform the existing charts particularly in its ability to provide more accurate diagnostics. [ABSTRACT FROM AUTHOR]

The study considers the features of controlling the degree of structural metallic material degradation at the stage of scattered damage accumulation before the appearance of macrocracks using an approach based on the analysis of structural acoustic noise. Various methods of measuring the parameters of structural noise have been analysed from the point of view of their information content and the stability of the used calculation algorithms. Refined calculation algorithms for determining the spectral-energy parameters of structural noise are proposed, and the results of their experimental verification are presented. Comparative results of the stability of these algorithms are given. On the example of API X70 steel samples, cut from the main gas pipeline after long-term operation, and the sample of 08Cr18Ni10Ti steel, tested for low-cycle fatigue, the effectiveness of the proposed algorithm application for assessing the degree of critical material technical objects damage and the possibility of constructing a method for diagnosing products from these materials is shown. [ABSTRACT FROM AUTHOR]