Statistical evaluation of measured biomechanical properties of human brain aneurysm samples.

Background and purpose:

Human brain aneurysms may often prove fatal if not re­cognized in time and treated accordingly. The understanding of development and rupture of aneurysms can significantly be improved by the application of numerical modelling, which in turn, requires the knowledge of mechanical properties of vessel wall. This study aims to identify assumed differences with respect to age, sex, spatial orientation, and rupture by utilizing detailed statistical analysis of uniaxial tensile measurements of human brain aneurysm samples, performed by the authors in a previous project.

At surgery of 42 patients, aneu­rysm fundi were cut distally to the clip. In each case, depending on size, varying number of stripes (altogether 88) were prepared and uniaxial stress-strain measurements were performed. Quantities related to the capacity, energy absorption or stiffness were determined and statistically analysed.

The number of specimens in the aneurysm sample was sufficient to establish statistical differences with respect to sex and rupture (p<0.05). No significant differences were detected in orientation, though higher values of stresses and deformations were ob­tained in the circumferential direction com­pared to the meridional direction. 

Significant differences bet­ween sexes with respect to ultimate deformations were demonstrated according to expectation, and the hypothesis on equality of energy capacity could be supported. Similarity of curves with respect to specimen orientation was also observed and ruptured aneurysm sacs tended to be smaller in size. It seems that differences and trends described in this paper are realistic and need to be applied in numerical modelling.

Detecting unexpected growths in health technologies expenditures: the case of MIPRES in Colombia.

We developed an algorithm to explore unexpected growth in the usage and costs of health technologies. We exploit data from the expenditures on technologies funded by the Colombian government under the compulsory insurance system, where all prescriptions for technologies not included in an explicit list must be registered in a centralized information system, covering the period from 2017 to 2022. The algorithm consists of two steps: an outlier detection method based on the density of the expenditures for selecting a first set of technologies to consider (39 technologies out of 106,957), and two anomaly detection models for time series to determine which insurance companies, health providers, and regions have the most notorious increases. We have found that most medicines associated with atypical behavior and significant monetary growth could be linked to the use of recently introduced drugs in the market. These drugs have valid patents and very specific clinical indications, often involving high-cost pharmacological treatments. The most relevant case is the Burosumab, approved in 2018 to treat a rare genetic disorder affecting skeletal growth. Secondly, there is clear evidence of anomalous increasing trend evolutions in the identified enteral nutritional support supplements or Food for Special Medical Purposes. The health system did not purchase these products before July 2021, but in 2022 they represented more than 500,000 USD per month.

On the accuracy of official Chinese crop production data: Evidence from biophysical indexes of net primary production.

With rapid economic growth and urbanization, self-sufficiency in crop production has become central to China's agriculture policy. Accurate crop production statistics are essential for research, monitoring, and planning. Although researchers agree that China's statistical authority has considerably modernized over time, China's economic statistics have still been viewed as unreliable and often overstated to meet growth targets at different administrative levels. Recent increases in crop production reported by national statistics have also come under increasing scrutiny. This paper investigates crop production data quality from a planetary boundary perspective-comparing net primary production (NPP) harvested obtained from national statistics with satellite-driven NPP estimates that are supported by detailed observation of land cover, combined with observations on physical factors that limit plant growth. This approach provides a powerful means to check the plausibility of China's grain production statistics at different administrative levels that can generate insights about their discrepancies and can contribute to improved crop production measurements. We find some evidence of potential misreporting problems from the lower administration level where the risk of manipulation of statistics is higher. We also find problems from provincial-level major grain producers. These values can also affect the national totals. Although the numbers are affected by large uncertainties, we find that improving the spatial resolution of key agricultural parameters can greatly improve the reliability of the indicator that in turn can help improve data quality. More reliable production data will be vital for relevant research and provide better insights into food security problems, the carbon cycle, and sustainable development.

[Cohort approach to the study of elderly mortality in modern Russia.]

In recent decades, there has been an increase in the number of elderly people in Russia, and the importance of getting a correct estimate of the number of elderly is more relevant than ever. Earlier, it was shown that the annual official estimates of the population in older ages in Russia are overstated. We examined this problem in a cohort context, comparing the mortality rates of cohorts born in 1900-1920 at the age of over 80, calculated by two methods, and made a comparison with countries with reliable population statistics. To adjust mortality rates, the population estimates in older ages were obtained using the method of extinct cohorts. We have shown why the calculation of mortality rates of the elderly population based on the method of extinct cohorts is better suited for the population of Russia over 80 years old than based on the official population estimates. As a result, it turned out that the underestimation of mortality rates of the elderly increases with age, especially in men.

[The application of statistical programs and methods of data mathematical processing in obstetrics and gynecology].

In recent decades, evidence-based medicine acquired special importance in medicine. Therefore, proper presentation of data obtained in scientific research is extremely important. The statistical data processing, being an integral part of this process, often causes difficulties for researchers and its incorrect application results in distortion of results obtained. The purpose of the study is to comparatively analyze programs and methods of statistical data processing applied in dissertations on obstetrics and gynecology in 2011-2021, to examine trends in choosing them depending on specificity of research issue and to identify shortcomings erred by authors in choosing or describing data processing methods. The sampling for analysis included 258 abstracts of candidate's dissertations in the specialty "obstetrics and gynecology", defended in 2011-2021. The analysis covered the programs and methods of mathematical data processing. Over the past decade, significant complication of statistical processing of results of clinical trials in obstetrics and gynecology occurred in part of methods applied. The application of binary logistic regression and discriminant analysis increased most significantly over the past decade. Such sophisticated methods of statistical data processing as factor analysis, decision trees, ordinal logistic regression and neural networks began to be used too. The trend of gradual replacement of parametric methods (Student's t-test, one-way analysis of variance) by such corresponding non-parametric methods as Mann-Whitney test, Kruskal-Wallis test. The Microsoft Excel and Statistica were used most often for data processing. In recent years, the software SPSS Statistics is actively applied. However, problems in describing statistical methods used in dissertations continue to be present. In significant part of dissertations information about statistical program applied, methods of assessing of quantitative data distribution and criteria of significance of obtained results is absent. The proper application of statistical programs, methods of information processing, adequate interpretation of results as well as provision of complete information about methodological support are the key points to carry out modern research resulting in trusted attitude to scientific work and its results.

Formaldehyde, aliphatic aldehydes (C2 -C11 ), furfural, and benzaldehyde in the residential indoor air of children and adolescents during the German Environmental Survey 2014-2017 (GerES V).

Indoor air concentrations of formaldehyde, furfural, benzaldehyde, and 11 aliphatic aldehydes (C2 -C11 ) were measured in residences of 639 participants in the German Environmental Survey for Children and Adolescents 2014-2017 (GerES V). Sampling was conducted using passive samplers over periods of approximately seven days for each participant. The most abundant compounds were formaldehyde and hexanal with median concentrations of 24.9 µg m-3 and 10.9 µg m-3 , respectively. Formaldehyde concentrations exceeded the Guide Value I recommended by the German Committee on Indoor Guide Values (Ausschuss für Innenraumrichtwerte - AIR) (0.10 mg m-3 ) for 0.3% of the participating residences. The sum of aliphatic n-aldehydes between C4 (butanal) and C11 (undecanal) exceeded their Guide Value (0.10 mg m-3 ) for 2.0% of the residences. The geometric mean concentrations of most aldehydes were lower than in the earlier GerES IV (2003-2006) study. Formaldehyde and hexanal concentrations, however, were comparable in both studies and showed no significant difference. Indoor aldehyde concentrations did not exhibit significant correlations with factors collected in questionnaires, such as the age of the participants, their socio-economic status, the location of the residence (former East/West Germany), migration background, tobacco exposure, and the type of furniture used. The validity of the passive sampler measurements was verified against active sampling techniques in a test chamber experiment.

An analysis of complaints about hospital care in the Republic of Ireland.

BACKGROUND: Patients and family members make complaints about their hospital care in order to express their dissatisfaction with the care received and prompt quality improvement. Increasingly, it is being understood that these complaints could serve as important data on how to improve care if analysed using a standardized tool. The use of the Healthcare Complaints Analysis Tool (HCAT) for this purpose has emerged internationally for quality and safety improvement. Previous work has identified hot spots (areas in care where harm occurs frequently) and blind spots (areas in care that are difficult for staff members to observe) from complaints analysis. This study aimed to (i) apply the HCAT to a sample of complaints about hospital care in the Republic of Ireland (RoI) to identify hot spots and blind spots in care and (ii) compare the findings of this analysis to a previously published study on hospital complaints in the UK. METHODS: A sample of complaints was taken from 16 hospitals in the RoI in Quarter 4 of 2019 (n = 641). These complaints were coded using the HCAT to classify complaints by domain, category, severity, stage of care and harm. Chi-squared tests were used to identify hot spots, and logistic regression was used to identify blind spots. The findings of this study were compared to a previously published UK study that used HCAT to identify hot spots and blind spots. RESULTS: Hot spots were identified in Irish hospital complaints while patients were receiving care on the ward, during initial examination and diagnosis, and while they were undergoing operations or procedures. This aligned with hot spots identified in the UK study. Blind spots were found for systemic problems, where patients experience multiple issues across their care. CONCLUSIONS: Hot spots and blind spots for patient harm can be identified in hospital care using the HCAT analysis. These in turn could be used to inform improvement interventions, and direct stakeholders to areas that require urgent attention. This study also highlights the promise of the HCAT for use across different healthcare systems, with similar results emerging from the RoI and the UK.

[Retrospective evaluation of electronically recorded patient questionnaires of a university outpatient pain clinic with the painDETECT® questionnaire]. / Retrospektive Auswertung elektronisch erhobener Patientenfragebögen einer universitären Schmerzambulanz mit dem painDETECT®-Fragebogen.

BACKGROUND AND OBJECTIVE: Psychometric tests can provide important information for diagnostics and progression in chronic pain patients. Between 2008 and 2018, the electronic system painDETECT® was used in the outpatient pain clinic of the Hannover Medical School (MHH). The aim of this retrospective study was to evaluate the pain symptomatology data recorded using painDETECT® and the treatment procedures used in the patient cohort examined over a period of 15 months. MATERIAL AND METHODS: A statistical analysis of baseline and follow-up data was performed. The analysis comprised pain-related parameters recorded by use of the painDETECT® system as well as outpatient records. RESULTS: Baseline data of 459 patients (66% women) could be evaluated. The most common clinical pictures were spinal pain, headache, facial pain, and somatoform disorders, mostly with many years of previous treatment. Approximately 40% showed evidence of neuropathic pain components or central sensitization. With a mean pain intensity of VAS 6 (0-10), a predominantly high degree of chronicity was present. Approximately one third showed a high degree of pain-related functional impairment. Slightly more than half showed evidence of clinically relevant depression. Approximately 80% showed clinically relevant sleep disturbances. Follow-up data were available for 145 patients (31.6%). The proportion of patients receiving a nonpharmacological form of treatment increased by 44.1% (physical therapy) and by 24.1% (psychotherapeutic procedures) during the observation period. The use of co-analgesics increased by approximately 30% over the course. CONCLUSION: In the outpatient setting, an extension of treatment can be successful for high-grade chronic pain patients. Close structural networking with the clinics for rehabilitation medicine and for psychosomatics and psychotherapy at the MHH can be a favorable prerequisite for this.

Olive Oil Traceability Studies Using Inorganic and Isotopic Signatures: A Review.

The olive oil industry is subject to significant fraudulent practices that can lead to serious economic implications and even affect consumer health. Therefore, many analytical strategies have been developed for olive oil's geographic authentication, including multi-elemental and isotopic analyses. In the first part of this review, the range of multi-elemental concentrations recorded in olive oil from the main olive oil-producing countries is discussed. The compiled data from the literature indicates that the concentrations of elements are in comparable ranges overall. They can be classified into three categories, with (1) Rb and Pb well below 1 µg kg-1; (2) elements such as As, B, Mn, Ni, and Sr ranging on average between 10 and 100 µg kg-1; and (3) elements including Cr, Fe, and Ca ranging between 100 to 10,000 µg kg-1. Various sample preparations, detection techniques, and statistical data treatments were reviewed and discussed. Results obtained through the selected analytical approaches have demonstrated a strong correlation between the multi-elemental composition of the oil and that of the soil in which the plant grew. The review next focused on the limits of olive oil authentication using the multi-elemental composition method. Finally, different methods based on isotopic signatures were compiled and critically assessed. Stable isotopes of light elements have provided acceptable segregation of oils from different origins for years already. More recently, the determination of stable isotopes of strontium has proven to be a reliable tool in determining the geographical origin of food products. The ratio 87Sr/86Sr is stable over time and directly related to soil geology; it merits further study and is likely to become part of the standard tool kit for olive oil origin determination, along with a combination of different isotopic approaches and multi-elemental composition.

Statistical assessment of seasonal variations in water quality for different regions in Lake Van (Türkiye).

On earth, surface water bodies interact and change with the natural ecosystems. These surface waters and water quality may be adversely affected due to different factors. To analyze the effects, parameters indicating water pollution and quality and the possible causes of these parameters should be examined. In addition, environmental pollution issues should be controlled by taking measures. The most important surface water body in the province of Van, located in the east of Türkiye, is the biggest soda Lake Van. The population density around the lake, human polluting factors, unconscious beach use, inadequate wastewater treatment, agriculture and livestock activities, small-scale industrial areas, and chemicals used create a pollution effect. In the study, data were obtained during year of 2018 from six important sampling points around Lake Van and from the middle of the lake. Twenty-seven water quality parameters were analyzed separately and together. These variables' yearly values were evaluated with Turkish Surface Water Quality Regulation (TSWQR, 2015). As a result, these points were determined to have class I in terms of water parameters according to the seasonal data. The basic descriptive statistics were compared with the regulation, and max, mean, and min values were examined. Data analyzed were done with probability-normality, trend analysis, correlation, and regression methods. The results of this study are that general parameters were normal and the quality of the six points continued to be similar. Na+, Cl-, salinity, and TDS were highly correlated, while DO and F were high matrix value parameters. EC, TDS, and SS regression equations provided high correlation parameters.

COMPARISON OF CAROTID STENOSIS GRADING BY CT ANGIOGRAPHY AND DOPPLER ULTRASONOGRAPHY: HOW THE STATISTICAL METHODS APPLIED INFLUENCE THE RESULTS.

In this study, we compared the measurement of carotid stenosis by computed tomography angiography (CTA) based on the narrowest diameter versus cross sectional area (CSA) with the measurement by color Doppler ultrasonography (CDUS) as a reference standard, and analyzed how the application of different statistical methods affected the result. On 113 carotid arteries with ≥50% stenosis, we quantified the level of correlation among the three measurements, sensitivity, specificity, and differences in the estimated stenosis level. Correlation between both CTA measurements was good with Pearson's ρ between 0.87 and 0.91 (p<0.001). Correlation between CDUS and CTA measurements was only modest with Pearson's ρ between 0.2 (p=0.075) and 0.4 (p=0,007) for CDUS CTA (CSA), and between 0.23 (p=0.062) and 0.39 (p=0.008) for CDUS CTA (diameter). Differences in stenosis between CTA (CSA) and CDUS were centered around 0%, and between CTA (diameter) and CDUS around 20%. Sensitivity and specificity for CTA (CSA) method were 81% and 77%, and for CTA (diameter) 23% and 100%, respectively. A good correlation between CSA and diameter measurement just means that these are two related features of stenosis, it does not mean good agreement. CTA (CSA) method better detected surgical stenoses, whereas CTA (diameter) systematically underestimated stenosis level. The study of differences between the measurements indicated agreement better than the calculation of correlation coefficients.

Cut points and contexts.

In research, policy, and practice, continuous variables are often categorized. Statisticians have generally advised against categorization for many reasons, such as loss of information and precision as well as distortion of estimated statistics. Here, a different kind of problem with categorization is considered: the idea that, for a given continuous variable, there is a unique set of cut points that is the objectively correct or best categorization. It is shown that this is unlikely to be the case because categorized variables typically exist in webs of statistical relationships with other variables. The choice of cut points for a categorized variable can influence the values of many statistics relating that variable to others. This essay explores the substantive trade-offs that can arise between different possible cut points to categorize a continuous variable, making it difficult to say that any particular categorization is objectively best. Limitations of different approaches to selecting cut points are discussed. Contextual trade-offs may often be an argument against categorization. At the very least, such trade-offs mean that research inferences, or decisions about policy or practice, that involve categorized variables should be framed and acted upon with flexibility and humility. LAY SUMMARY: In research, policy, and practice, continuous variables are often turned into categorical variables with cut points that define the boundaries between categories. This involves choices about how many categories to create and what cut-point values to use. This commentary shows that different choices about which cut points to use can lead to different sets of trade-offs across multiple statistical relationships between the categorized variable and other variables. These trade-offs mean that no single categorization is objectively best or correct. This context is critical when one is deciding whether and how to categorize a continuous variable.

Statistical data presentation: a primer for rheumatology researchers.

Statistical presentation of data is key to understanding patterns and drawing inferences about biomedical phenomena. In this article, we provide an overview of basic statistical considerations for data analysis. Assessment of whether tested parameters are distributed normally is important to decide whether to employ parametric or non-parametric data analyses. The nature of variables (continuous or discrete) also determines analysis strategies. Normally distributed data can be presented using means with standard deviations (SD), whereas non-parametric measures such as medians (with range or interquartile range) should be used for non-normal distributions. While the SD provides a measure of data dispersion, the standard error provides estimates of the 95% confidence interval i.e. the actual mean in the population. Univariable analyses should be directed to denote effect sizes, as well as test a priori hypothesis (i.e. null hypothesis significance testing). Univariable analyses should be followed up by suitable adjusted multivariable analyses such as linear or logistic regression. Linear correlation statistics can help assess whether two variables change hand in hand. Concordance rather than correlation should be used to compare outcome measures of disease states. Prior sample size calculation to ensure adequate study power is recommended for studies which have analogues in the literature with SDs. Statistical considerations for systematic reviews should include appropriate use of meta-analysis, assessment of heterogeneity, publication bias assessment when there are more than ten studies, and quality assessment of studies. Since statistical errors are responsible for a significant proportion of retractions, appropriate statistical analysis is mandatory during study planning and data analysis.

Use of docetaxel in low- and high-burden metastatic hormone-sensitive prostate cancer: A systematic review and assessment of subgroup analyses.

BACKGROUND: Use of docetaxel in low- and high-burden metastatic hormone-sensitive prostate cancer presents considerable controversy. There is literature suggesting lack of benefit for low-volume of metastases. OBJECTIVE: The study aims to develop a systematic review and methodological assessment of subset analysis about use of docetaxel in metastatic hormone-sensitive prostate cancer regarding volume of metastatic disease. METHODS: A systematic review in the Pubmed® database was conducted up to 25 September 2020. A reference tracking was also developed. Randomised clinical trials with subgroup analysis according volume of metastatic disease for overall survival were selected. Two methodologies were used. One of them considered statistical interaction of subsets (p(i) < 0.1), pre-specification, biological plausibility and consistency among subset results of similar randomised clinical trials. The second methodology was a two-part validated tool: preliminary questions to discard subset analysis without minimal relevance and a checklist The checklist provides recommendations for applicability of subgroup analysis in clinical practice. RESULTS: A total of 31 results were found in systematic reviews in the Pubmed® database. One result was identified in the reference tracking. Of the total of 32 results, four randomised clinical trials were included in the study. About first methodology, statistical interaction among subgroups was obtained in one randomised clinical trial. Subgroup analysis was pre-specified in two randomised clinical trials. Biological plausibility was reasonable. No external consistency among results of subgroup analyses in randomised clinical trials was observed. Preliminary questions of second methodology rejected applicability of subgroup analysis in three randomised clinical trials. A 'null' recommendation for applicability of subset results was obtained in the remaining randomised clinical trial. CONCLUSIONS: Patients with low- and high-burden metastatic hormone-sensitive prostate cancer would benefit from docetaxel therapy. No consistent differences for overall survival were observed in subgroup analyses regarding volume of metastatic disease.

Effect of Acupuncture for Postprandial Distress Syndrome: A Randomized Clinical Trial.

BACKGROUND: Postprandial distress syndrome (PDS) is the most common subtype of functional dyspepsia. Acupuncture is commonly used to treat PDS, but its effect is uncertain because of the poor quality of prior studies. OBJECTIVE: To assess the efficacy of acupuncture versus sham acupuncture in patients with PDS. DESIGN: Multicenter, 2-group, randomized clinical trial. (ISRCTN registry number: ISRCTN12511434). SETTING: 5 tertiary hospitals in China. PARTICIPANTS: Chinese patients aged 18 to 65 years meeting Rome IV criteria for PDS. INTERVENTION: 12 sessions of acupuncture or sham acupuncture over 4 weeks. MEASUREMENTS: The 2 primary outcomes were the response rate based on overall treatment effect and the elimination rate of all 3 cardinal symptoms: postprandial fullness, upper abdominal bloating, and early satiation after 4 weeks of treatment. Participants were followed until week 16. RESULTS: Among the 278 randomly assigned participants, 228 (82%) completed outcome measurements at week 16. The estimated response rate from generalized linear mixed models at week 4 was 83.0% in the acupuncture group versus 51.6% in the sham acupuncture group (difference, 31.4 percentage points [95% CI, 20.3 to 42.5 percentage points]; P < 0.001). The estimated elimination rate of all 3 cardinal symptoms was 27.8% in the acupuncture group versus 17.3% in the sham acupuncture group (difference, 10.5 percentage points [CI, 0.08 to 20.9 percentage points]; P = 0.034). The efficacy of acupuncture was maintained during the 12-week posttreatment follow-up. There were no serious adverse events. LIMITATION: Lack of objective outcomes and daily measurement, high dropout rate, and inability to blind acupuncturists. CONCLUSION: Among patients with PDS, acupuncture resulted in increased response rate and elimination rate of all 3 cardinal symptoms compared with sham acupuncture, with sustained efficacy over 12 weeks in patients who received thrice-weekly acupuncture for 4 weeks. PRIMARY FUNDING SOURCE: Beijing Municipal Science and Technology Commission.

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy.

OBJECTIVE: Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction. DESIGN: A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland. SETTING: Fifteen primary health care centers of the PHCCA. SUBJECTS: All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses. MAIN OUTCOME MEASURES: Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier. RESULTS: The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses. CONCLUSION: In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.KeypointsLittle is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.

Estimation of a Minimum Allowable Structural Strength Based on Uncertainty in Material Test Data.

Three types of uncertainties exist in the estimation of the minimum fracture strength of a full-scale component or structure size. The first, to be called the "model selection uncertainty," is in selecting a statistical distribution that best fits the laboratory test data. The second, to be called the "laboratory-scale strength uncertainty," is in estimating model parameters of a specific distribution from which the minimum failure strength of a material at a certain confidence level is estimated using the laboratory test data. To extrapolate the laboratory-scale strength prediction to that of a full-scale component, a third uncertainty exists that can be called the "full-scale strength uncertainty." In this paper, we develop a three-step approach to estimating the minimum strength of a full-scale component using two metrics: One metric is based on six goodness-of-fit and parameter-estimation-method criteria, and the second metric is based on the uncertainty quantification of the so-called A-basis design allowable (99 % coverage at 95 % level of confidence) of the full-scale component. The three steps of our approach are: (1) Find the "best" model for the sample data from a list of five candidates, namely, normal, two-parameter Weibull, three-parameter Weibull, two-parameter lognormal, and three-parameter lognormal. (2) For each model, estimate (2a) the parameters of that model with uncertainty using the sample data, and (2b) the minimum strength at the laboratory scale at 95 % level of confidence. (3) Introduce the concept of "coverage" and estimate the fullscale allowable minimum strength of the component at 95 % level of confidence for two types of coverages commonly used in the aerospace industry, namely, 99 % (A-basis for critical parts) and 90 % (B-basis for less critical parts). This uncertainty-based approach is novel in all three steps: In step-1 we use a composite goodness-of-fit metric to rank and select the "best" distribution, in step-2 we introduce uncertainty quantification in estimating the parameters of each distribution, and in step-3 we introduce the concept of an uncertainty metric based on the estimates of the upper and lower tolerance limits of the so-called A-basis design allowable minimum strength. To illustrate the applicability of this uncertainty-based approach to a diverse group of data, we present results of our analysis for six sets of laboratory failure strength data from four engineering materials. A discussion of the significance and limitations of this approach and some concluding remarks are included.

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion.

This article presents the research and results of field tests and simulations regarding an autonomous/robotic railway vehicle, designed to collect multiple information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning. The maintenance of complex railways, or subway networks with long operating times is a difficult process and intensive resources consuming. The proposed solution delivers human operators in the fault management service and operations from the time-consuming task of railway inspection and measurements, by integrating several sensors and collecting most relevant information on railway, associated automation equipment and infrastructure on a single intelligent platform. The robotic cart integrates autonomy, remote sensing, artificial intelligence, and ability to detect even infrastructural anomalies. Moreover, via a future process of complex statistical filtering of data, it is foreseen that the solution might be configured to offer second-order information about infrastructure changes, such as land sliding, water flooding, or similar modifications. Results of simulations and field tests show the ability of the platform to integrate several fault management operations in a single process, useful in increasing railway capacity and resilience.

Analysis of long-term statistical data of cobalt flows in the EU.

Long-term statistical data was explored, acquired, processed, and analysed in order to assess the historical domestic production and international trade of a number of cobalt-containing commodities in the EU. Different data sources were examined for data, such as the British Geological Survey (BGS), the US Geological Survey (USGS), and the Eurostat and UN Comtrade (UNC) databases, considering all EU-member states before and after they joined the EU. For the international trade, hidden flows related to data gaps such as data reported in monetary value or recorded as "special category" were identified and included in the analysis. In addition, data from the Finnish customs database (ULJAS) was used to complement flows reported by Eurostat and UNC. From UNC, data was obtained considering the member states as reporters or as partners of the trade, due to internal differences of the database. Based on the acquired data the domestic production and international trade of the commodities were reconstructed for the timeframes 1938-2018 and 1988-2018, respectively. Next to the analysis of the trend of the production and trade of the different commodities, the importance of including hidden flows was revealed, where hidden flows represented more than 50% of the flow of a year in some cases. In addition, it was identified that even from reliable data sources, strong differences (more than 100% in some cases) can be found in the reported data, which is crucial to consider when utilizing the data in research.

Is it time to stop using statistical significance?

The important first step in the critical appraisal of a randomised trial is not an evaluation of the statistical analyses. The most important aspect to consider when reviewing a study of a new drug is the appropriateness and quality of the trial design and methods The next most important aspect is the effect size of different treatments and its clinical significance. Rather than reporting statistical significance, studies should report the difference between treatments and its precision Over-reliance on statistical significance and p values may lead to incorrect conclusions. Trial reports about drugs should therefore avoid the term statistical significance and quote p values with caution.