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One of the main causes of contaminated groundwater in emerging nations is improper trash disposal in urban areas, which affects the level of groundwater contamination caused by contaminants of municipal solid waste (MSW) origin within the three local government headquarters in Akwa Ibom State, southeastern Nigeria. The main thrust of this research survey is to assess the level of groundwater contaminations and their consequences. The research used statistical data generated from the Electrical Resistivity Survey (ERS) in combination with hydrogeochemical investigations. Analysis of variance of resistivity between Uyo, Ikot Ekpene and Oron was carried out. The test result indicated significant difference in contamination among the three cities. This was followed by a t-test between each pair of dump and control sites in the three cities. The test results showed significant difference between each control and dumpsite. The results showed that leachate layer conductivity is always higher than that of the layer above it. All water samples from boreholes close to the dumpsites were identified by hydrogeochemical analysis to exhibit pH (3.70–4.15) lower than the permissible limit of the WHO; few water samples exhibit increased electrical conductivity (EC), cadmium and total dissolved solids (TDS). Similarly, the bacteriological analyses indicated a high level of microbial load due to the waste dump. Formations found in boreholes close to the dumpsite have litho-correlations which depict intercalations of comparatively impermeable and porous materials. The findings reveal that leachate (contaminate) travels slowly downward, allowing for physical, chemical and biological processes to filter out impurities before they get to the aquifer. It is recommended that no new water supply wells should be placed in areas of abnormally low resistivity and physicochemical and bacteriological parameters, until the reasons for these values are properly assessed. [ABSTRACT FROM AUTHOR]

Factors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9[Formula: see text] higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

Factors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9[Formula: see text] higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

The subject of this article is the process of detection and estimation frequency boundaries of spectrum holes under conditions of high spectrum occupancy when using receivers with narrow instantaneous bandwidths. The work increases the probability of spectrum holes correct detection in conditions of high occupancy of the radio frequency spectrum and variable noise levels by developing a method to distinguish signal and noise samples based on the analysis of the histogram of spectral sample modes. The tasks to be solved are: development of a method for separation signal and noise samples in the frequency domain; development of a methodology to find the minimum mode of a multimodal probability distribution; determination of frequency boundaries of spectrum holes; formulation of recommendations for the practical implementation of developed method. The methods used are: methods of probability theory and mathematical statistics, methods of statistical modeling. The essence of the proposed method is to distinguish the set of energy spectrum samples using a threshold, obtained for the value of the histogram mode, which corresponds to noise, and to determine the frequency boundaries of spectrum holes. The following results were obtained: an expression for calculating the threshold value for separation signal and noise samples in the frequency domain using the value, which corresponds to the noise mode of the frequency samples of the probability density function. It was found that the noise mode has the smallest value among other modes, since noise samples have a smaller value compared to the signal ones. A technique for estimating the value of the noise mode has been developed, which consists of a histogram of energy spectrum frequency samples and finding the partition interval that corresponds to the value of the minimal mode. An approach was proposed to determine the frequency boundaries of noise samples in the presence of one signal in the analyzed band. Conclusions. The developed method allows detecting spectrum holes with a probability of at least 0.9 at signal-to-noise ratio values of at least 5 dB for the spectrum with a rectangular shape envelope and 12 dB for other envelopes under occupancy of up to 80%.

【Objective】 Weir is a classical apparatus measuring water flow rate in open channels. Its accuracy and the formula used to calculate flow rate depend on many factors. In this paper, we proposed a new method to calculate flow rate with measurement errors of the Weir taken into account. 【Method】 Considering the limitation of short series in multiple regression for calibrating the basic weir flow using the simplified lumped flow coefficient, the statistical effect of the accuracy of weir-measured flow in short series causal mapping relationship is found from the calibration results of different measured weir flow accuracy under the experimental statistical characteristics. The consistent response mechanism under the effect of multi-dimensional variable weir flow characteristic variable fuzzy probability correlation distribution identification and measured weir flow accuracy is established. 【Result】 The multivariate short series discharge calculation involving the front and rear weir width ratio a/c, the side weir length b and the expansion ratio L/W is a multidimensional variable fuzzy probability correlation distribution response different from the traditional linear section causal mapping model; From the perspective of algorithm mechanism, it is revealed that under the same weir crest head H, the contribution of front and rear weir width ratio a/c, side weir length b and widening ratio L/W to the characterization of the index measured discharge [Q'cr] is of equal significance. 【Conclusion】 The application calibration and verification of different short series measured weir flow accuracy samples in the existing literature results show that the calculation results of the method are in good agreement with the experimental statistical results, and the research results can provide reference for the design review of similar projects.

This article discusses an instrumental variable approach for analyzing censored data that includes many instruments that are weakly associated with the endogenous variable. We study the effect of imprisonment on time to employment using an administrative data on all individuals sentenced for felony in Michigan in the years 2003–2006. Despite the large body of research on the effect of prison on employment, this is still a controversial topic, especially since some of the studies could have been affected by unmeasured confounding. We take advantage of a natural experiment based on the random assignment of judges to felony cases and construct a vector of instruments based on judges’ ID that can avoid the confounding bias. However, some of the constructed instruments are weakly associated with the sentence type, that is, the endogenous variable, which can potentially lead to misleading results. Using a dimension reduction technique, we propose a novel semiparametric estimation procedure in a survival context that is robust to the presence of many weak instruments. Specifically, we construct a test statistic based on the structural failure time model and provide inference by inverting the testing procedure. Under some assumptions, the optimal choice of the test statistic has also been derived. Analyses show a significant negative impact of imprisonment on time to employment which is consistent with some of the previous results. Our simulation studies highlight the importance of accounting for weak instruments in the analyses in terms of both bias and inflated type-I error rates.

Global Navigation Satellite System (GNSS) signal quality, type of receiver equipment, and external environment can cause GNSS observations to be anomalous, and these anomalies are sometimes reflected in GNSS pseudorange observations rather than phase observations. To better detect blunders in pseudorange observations, this paper proposes three pseudorange blunder detection methods under the same frequency and different code types (case1), and the same code type and different frequencies (case2), of pseudorange observations, which are the Code Observation Difference Method (CODM), the Inter-satellite Code Observation Difference Method (ICODM), and the Inter-epoch and Inter-satellite Code Observation Difference Method (IICODM). The corresponding thresholds for the constructed test statistics of the three detection methods were derived based on the Bessel formula. Performance analysis of the three detection methods was performed under case1 based on C2 and P2 code observation data of Global Positioning System (GPS) at 137 Multi-GNSS Experiment (MGEX) stations, and case2 based on BDS B1I and B3I frequency observation data of BeiDou Navigation Satellite System (BDS) at 232 MGEX stations, on 29 July 2022. The results show that the statistical information value of the three methods in case1 was significantly smaller than that in case2. In the first case, the maximum values of test statistics, RMSE and threshold mean values were 0.526, 0.752 and 2.243 m, respectively, while the corresponding values in case2 were 7.066, 4.490 and 13.480 m respectively. The reason for this is that the data quality of global GPS is higher than that of BDS and the differential observation equation eliminates or weakens more errors with the same frequency and different types of code pseudorange observations. Under the same conditions, compared with ICODM and IICODM, CODM has high computational efficiency and a simple mathematical model. It is recommended to use CODM first for pseudorange blunder detection in the GNSS observation domain. According to the RMSE of 3 times as the limit, it is recommended that the threshold be set to 5 m under case1 for GPS and 15 m under case2 for BDS, which is half the existing reference value. Finally, the blunder detection methods proposed can improve positioning performance through actual data verification. [ABSTRACT FROM AUTHOR]

Nowadays the main approach of organizing the radio frequency spectrum monitoring is fully automatic continuous analysis of a given frequency range. The overwhelming majority of software products of the world leaders specializing in the development of radio monitoring instruments have an open architecture, which allows users to implement their own signal processing modules. Therefore, improving the software and algorithms of existing spectrum analyzers will increase the efficiency of detecting occupied frequency regions and forming receiving channels. In this paper, it is proposed to use a dynamic energy detector in the frequency domain to detect frequency channels. The dynamism of the detector consists in the sequential calculating of the detection threshold depending on the value of the test statistics. The coefficient of variation of frequency samples is used as a test statistic. The essence of the proposed method is calculating the coefficient of variation using the samples of the power spectral density of the received signal and comparing it with the threshold for test statistic, calculating threshold in frequency domain for dividing samples into signal and noise. Then the procedure is repeated until all signal samples are detected. For operation of the algorithm, it is necessary to set only the parameters of the Welch periodogram, the vector of the threshold values for the test statistics, and the probability of a false alarm. The software implementation of the proposed approach will allow detecting and determining the frequency boundaries of signals in a wide dynamic range, with unknown values of the bandwidth occupancy and noise power. The increase in the speed of the developed algorithm in comparison with similar ones depends on the level of occupancy of the radio frequency spectrum, the dynamic range of signals and the length of the fast Fourier transform and can be tens of times.

Polymerase chain reaction (PCR) and antigen tests have been used extensively for screening during the severe acute respiratory syndrome coronavirus 2 pandemics. However, the real‐world sensitivity and specificity of the two testing procedures in the field have not yet been estimated without assuming that the PCR constitutes a gold standard test. We use latent class models to estimate the in situ performance of both tests using data from the Danish national registries. We find that the specificity of both tests is very high (>99.7%), while the sensitivities are 95.7% (95% confidence interval [CI]: 92.8%–98.4%) and 53.8% (95% CI: 49.8%–57.9%) for the PCR and antigen tests, respectively. These findings have implications for the use of confirmatory PCR tests following a positive antigen test result: we estimate that serial testing is counterproductive at higher prevalence levels. [ABSTRACT FROM AUTHOR]

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The hematological module of the Athlete Biological Passport (ABP) aims to reveal blood doping indirectly by looking at selected biomarkers of doping over time. For Anti-Doping Organizations (ADOs), the ABP is a vital tool in the fight against doping in sports through improved target testing and analysis, investigations, deterrence, and as indirect evidence for use of prohibited methods or substances. The physiological characteristics of sport disciplines is an important risk factor in the overall risk assessment and when implementing the hematological module. Sharing of experiences with implementing the hematological ABP between ADOs is key to further strengthen and extend its use. In this study, we present 10 years of experience with the hematological ABP program from the perspectives of a National ADO with special attention to sport disciplines' physiological characteristics as a potential risk factor for blood doping. Not surprisingly, most samples were collected in sport disciplines where the aerobic capacity is vital for performance. The study highlights strengths in Anti-Doping Norway's testing program but also areas that could be improved. For example, it was shown that samples were collected both in and out of season in a subset of the data material that included three popular sports in Norway (Cross-Country Skiing, Nordic Combined, and Biathlon), however, from the total data material it was clear that athletes were more likely to be tested out of competition and on certain days of the week and times of the day. The use of doping control officers with a flexible time schedule and testing outside an athlete's 60 min time-slot could help with a more even distribution during the week and day, and thus reduce the predictability of testing. In addition to promoting a discussion on testing strategies, the study can be used as a starting point for other ADOs on how to examine their own testing program.

In this paper, we develop the asymptotic distribution of the covariance structure of an asymmetric multivariate exponential power distribution (AMEPD) by extending the existing Hotelling t2 distribution to its generalized form. The obtained results 'generalised Hotelling t2 test statistic', accommodates for the existing Mahalanobis distance between two multivariate data sets. The hazard rate and the characteristics function of the desired test statistics are established. The study further establishes the test of hypothesis and simultaneous confidence interval for the population mean vectors of p th attributes from AMEPD by using the Neymar-Pearson lemma and Pivotal quantity approaches. The obtained results, which are dependent on shape parameter flexibility and skewness parameter, generalize the test of hypothesis and simultaneous confidence intervals of various distributions which are members of the exponential power family. Simulated and real life data were used to demonstrate the usefulness of the obtained results. [ABSTRACT FROM AUTHOR]

We report the results of studies of improved tests for non-linearity based on time series-induced network statistics and surrogate data. We compare results from the network-based statistics with the earlier tests available in the literature and demonstrate the superiority of these tests over the previous tests for several systems. The method we propose is based on constructing a network from a time series and using easily computable parameters of the resulting network such as the average path length, graph density and clustering coefficient as test statistics for the surrogate data test. These statistics are tested for their ability to distinguish between nonlinear processes and linear noise processes, using surrogate data tests on time series obtained from the Rössler system, the Lorenz system, the Henon map, the logistic map and an actual experimental time series of wind speed data, and compared with popularly used time series associated statistics. The network-based statistics are found to distinguish between the nonlinear time series and surrogates derived from the data to a higher degree than the commonly used time series-based statistics, even in the presence of measurement noise and dynamical noise. These statistics may thus prove to be of value in distinguishing between time series derived from nonlinear processes and time series obtained from linearly correlated stochastic processes even in the presence of measurement noise and dynamical noise. The results also show that the efficiency of the network parameters is not exacerbated by the presence of outliers in the given time series. [ABSTRACT FROM AUTHOR]

Despite vaccination programs and antivirals, influenza remains a prominent cause of morbidity and mortality. The Xpert Xpress Flu/respiratory syncytial virus (RSV) test is a leading influenza point‐of‐care test, but its evaluation has been limited to nasopharyngeal samples. In addition, the clinical impacts of Xpress Flu/RSV have not yet been quantified. We evaluated the performance of Xpress Flu/RSV at three locations in a UK Hospital Trust against an existing laboratory assay. Multiple upper respiratory tract sample types were included. In addition, we calculated time saved by Xpert, and the associations between Xpert use and rates of early patient isolation and antiviral prescription as recorded at the time of the laboratory result being telephoned out. A total of 642 patients were included in the diagnostic performance analysis. There were 177 laboratory‐confirmed cases of influenza A, 7 influenza B and 86 RSV. For influenza A, sensitivity and specificity were 96.6% (95% confidence interval [CI]: 92.8%–98.8%) and 98.1% (CI: 96.4%–99.1%), respectively. This was sustained across all locations and sample types. The negative predictive value was 98.7% (CI: 97.2%–99.4%). The median amount of time saved was 27.1 h. Xpert use was associated with sixfold higher rates of isolation and threefold higher rates of antiviral prescribing by the time the laboratory result was available. Sensitivity for RSV was lower at 86.0% (95% CI: 76.9%–92.6%). Xpert Xpress Flu/RSV reliably detects influenza A infection and has significant clinical impacts. Cartridge optimization is required to enable accurate multiplexing, including from a range of sample types. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) signal quality, type of receiver equipment, and external environment can cause GNSS observations to be anomalous, and these anomalies are sometimes reflected in GNSS pseudorange observations rather than phase observations. To better detect blunders in pseudorange observations, this paper proposes three pseudorange blunder detection methods under the same frequency and different code types (case1), and the same code type and different frequencies (case2), of pseudorange observations, which are the Code Observation Difference Method (CODM), the Inter-satellite Code Observation Difference Method (ICODM), and the Inter-epoch and Inter-satellite Code Observation Difference Method (IICODM). The corresponding thresholds for the constructed test statistics of the three detection methods were derived based on the Bessel formula. Performance analysis of the three detection methods was performed under case1 based on C2 and P2 code observation data of Global Positioning System (GPS) at 137 Multi-GNSS Experiment (MGEX) stations, and case2 based on BDS B1I and B3I frequency observation data of BeiDou Navigation Satellite System (BDS) at 232 MGEX stations, on 29 July 2022. The results show that the statistical information value of the three methods in case1 was significantly smaller than that in case2. In the first case, the maximum values of test statistics, RMSE and threshold mean values were 0.526, 0.752 and 2.243 m, respectively, while the corresponding values in case2 were 7.066, 4.490 and 13.480 m respectively. The reason for this is that the data quality of global GPS is higher than that of BDS and the differential observation equation eliminates or weakens more errors with the same frequency and different types of code pseudorange observations. Under the same conditions, compared with ICODM and IICODM, CODM has high computational efficiency and a simple mathematical model. It is recommended to use CODM first for pseudorange blunder detection in the GNSS observation domain. According to the RMSE of 3 times as the limit, it is recommended that the threshold be set to 5 m under case1 for GPS and 15 m under case2 for BDS, which is half the existing reference value. Finally, the blunder detection methods proposed can improve positioning performance through actual data verification.

Measuring signals reflecting local processes of complex industrial aggregates bear important information about the long-term functional stability of such dynamic objects. However, to detect such information in signals with a priori unknown probability models of nonstationarity is a problem. It's possible solution is the creation of information technologies for the parametrization and normalization of random spectral changes in signals with significant limitations on the observation time. Such technologies will reduce the risks of decision making during monitoring and diagnosing of functional conditions of industrial, transportation, and process facilities. The identification of regularities in random models of nonstationarity is the acquisition of fundamentally new additional information on the functional properties of a dynamic object that facilitates the solution of many problem identification problems of objects and the optimal synthesis of information computerized systems under conditions of a priori uncertainty.

The aim of the research is to develop an online environment where teachers can access test and item statistics and a comprehensible report regarding the tests they have used, to ensure the storage of their reports in their own online environment as well as to have an idea on the utility of the subject matter environment. The research is planned as a design and development research. Research data was collected in two stages. The data which was needed to compare were obtained simulatively through the TAP program. Also, dta was collected from a group of 15 teachers to collect evidence for the usability of the application. Test and item statistics were calculated for the collected data and compared with the results of the programs making similar calculations. Descriptive analysis was used in the interpretation of the usability questionnaire and of teacher opinions. As a result of the research, the item and test statistics calculated for all data sets created by sampling different situations were found to be similar to those of reliable equivalent package programs. With the opinions received from the teachers, it has been concluded that the environment is useful and practical. While one of the important differences of the online environment formed by this study is its keeping a record of the past; another is its accessibility through the internet without the need for an additional program and thirdly, its usability on a mobile platform. [ABSTRACT FROM AUTHOR]

The 2019 novel‐coronavirus (COVID‐19) has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID‐19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID‐19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea's rate of 1.8% (seven times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran. Highlights: Due to the very recent outbreak of COVID‐19, our knowledge is expanding as new data is presented.Pandemic's outbreak behavior and the the effectiveness of the corresponding policies by the countries analyzed in this study.A new procedure based on regression approach to calculate the case fatality rate during pandemics.A reference of patterns and behaviors to help future pandemics' analysis. [ABSTRACT FROM AUTHOR]

The results of numerical studies of the effect of roundoff errors on the distribution of test statistics of statistical hypotheses are presented. The effect of the roundoff on the distribution of statistics of different goodness-of-fit and homogeneity tests is investigated. It is shown that, when the roundoff errors in the analyzed samples are comparable with the standard deviation of the measurement errors, the distribution of test statistics may vary significantly. Under such conditions the application of a test in the systems of data processing using the classical results may lead to false conclusions. The recommendations for resolving this issue are proposed. [ABSTRACT FROM AUTHOR]

Purpose: Human papilloma virus (HPV) as the most common viral infection of the anogenital tract is highly associated with intraepithelial neoplasia and cancer of the cervix and other anogential regions. To date, 15 high-risk (HR-) HPV and 3 probably/possibly HR-HPVs have been found to be associated with cervical cancer. Therefore, a screening especially for HR-HPV by appropriate tests is important for detection of precancerous lesions to prevent cancer. The purpose of this study was to analyze prospectively the concordance of the EUROArray HPV genotyping assay (Euroimmun; EUROArray) and the HPV 3.5 LCD-Array Kit (Chipron; LCD-Array).Methods: Liquid-based, clinician-collected cervical cytology samples (n = 163) from women undergoing cervical inspection at the dysplasia consultation in the colposcopy clinic at the Medical Center-University of Freiburg, Germany were analyzed. Genotype-specific agreement was assessed by Cohen's kappa statistic and McNemar's P value of significance between proportions.Results: Seventeen of the HR-HPV genotypes included in both assays were detected in 42.3% and 38% of samples by EUROArray and by LCD-Array, respectively; i.e. an agreement of 92.0% and a kappa value of 0.83 could be proven between the EUROArray and the LCD-Array. In 50 of 72 samples, identical HR-HPV genotypes were analyzed (81.9%, κ = 0.47) and genotyping for HPV 16 and/or 18 was highly concordant in both tests (relative agreement 96.3%, κ = 0.88). Detection of any HR-HPV was not significantly different after comparison of EUROArray with LCD-Array.Conclusion: Both of the tests showed comparable results for the detection of HPV in cervical specimens and permit these assays to be suitable for routine diagnostics. [ABSTRACT FROM AUTHOR]

Humans and other animals effortlessly identify natural sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable sound recognition and the formation of perceptual categories are largely unknown. Here, using multichannel neural recordings in the auditory midbrain of unanesthetized female rabbits, we first demonstrate that neural ensemble activity in the auditory midbrain displays highly structured correlations that vary with distinct natural sound stimuli. These stimulus-driven correlations can be used to accurately identify individual sounds using single-response trials, even when the sounds do not differ in their spectral content. Combining neural recordings and an auditory model, we then show how correlations between frequency-organized auditory channels can contribute to discrimination of not just individual sounds but sound categories. For both the model and neural data, spectral and temporal correlations achieved similar categorization performance and appear to contribute equally. Moreover, both the neural and model classifiers achieve their best task performance when they accumulate evidence over a time frame of approximately 1–2 seconds, mirroring human perceptual trends. These results together suggest that time-frequency correlations in sounds may be reflected in the correlations between auditory midbrain ensembles and that these correlations may play an important role in the identification and categorization of natural sounds. [ABSTRACT FROM AUTHOR]

The Best Possible Self (BPS) exercise promotes a positive view of oneself in the best possible future, after working hard towards it. Since the first work that attempted to examine the benefits of this intervention in 2001, studies on the BPS have grown exponentially and, currently, this is one of the most widely used Positive Psychology Interventions. However, little is yet known about its overall effectiveness in increasing wellbeing outcomes. Thus, the aim of this meta-analysis is to shed light on this question. A systematic literature search was conducted, and 29 studies (in 26 articles) met the inclusion criteria of empirically testing the intervention and comparing it to a control condition. In addition, BPS was compared to gratitude interventions in some of the included studies. A total of 2,909 participants were involved in the analyses. The outcome measures were wellbeing, optimism, depressive symptoms, and positive and negative affect. Results showed that the BPS is an effective intervention to improve wellbeing (d+ = .325), optimism (d+ = .334) and positive affect (d+ = .511) comparing to controls. Small effect sizes were obtained for negative affect and depressive symptoms. Moderator analyses did not show statistically significant results for wellbeing, except for a trend towards significance in the age of the participants (years) and the magnitude of the intervention (total minutes of practice). In addition, the BPS was found to be more beneficial for positive and negative affect than gratitude interventions (d+ = .326 and d+ = .485, respectively). These results indicate that the BPS can be considered a valuable Positive Psychology Intervention to improve clients’ wellbeing, and it seems that it might be more effective for older participants and with shorter practices (measured as total minutes of practice). [ABSTRACT FROM AUTHOR]

Sex-biased demographic events (“sex-bias”) involve unequal numbers of females and males. These events are typically inferred from the relative amount of X-chromosomal to autosomal genetic variation and have led to conflicting conclusions about human demographic history. Though population size changes alter the relative amount of X-chromosomal to autosomal genetic diversity even in the absence of sex-bias, this has generally not been accounted for in sex-bias estimators to date. Here, we present a novel method to identify sex-bias from genetic sequence data that models population size changes and estimates the female fraction of the effective population size during each time epoch. Compared to recent sex-bias inference methods, our approach can detect sex-bias that changes on a single population branch without requiring data from an outgroup or knowledge of divergence events. When applied to simulated data, conventional sex-bias estimators are biased by population size changes, especially recent growth or bottlenecks, while our estimator is unbiased. We next apply our method to high-coverage exome data from the 1000 Genomes Project and estimate a male bias in Yorubans (47% female) and Europeans (43%), possibly due to stronger background selection on the X chromosome than on the autosomes. Finally, we apply our method to the 1000 Genomes Project Phase 3 high-coverage Complete Genomics whole-genome data and estimate a female bias in Yorubans (63% female), Europeans (84%), Punjabis (82%), as well as Peruvians (56%), and a male bias in the Southern Han Chinese (45%). Our method additionally identifies a male-biased migration out of Africa based on data from Europeans (20% female). Our results demonstrate that modeling population size change is necessary to estimate sex-bias parameters accurately. Our approach gives insight into signatures of sex-bias in sexual species, and the demographic models it produces can serve as more accurate null models for tests of selection. [ABSTRACT FROM AUTHOR]

Classical null hypothesis significance tests are not appropriate in corpus linguistics, because the randomness assumption underlying these testing procedures is not fulfilled. Nevertheless, there are numerous scenarios where it would be beneficial to have some kind of test in order to judge the relevance of a result (e.g. a difference between two corpora) by answering the question whether the attribute of interest is pronounced enough to warrant the conclusion that it is substantial and not due to chance. In this paper, I outline such a test. [ABSTRACT FROM AUTHOR]

How might nonverbal synchrony naturally evolve in a social virtual reality environment? And how can avatar embodiment affect how participants coordinate nonverbally with each other? In the following pre-registered between-subjects experiment, we tracked the movements of pairs of users during a collaborative or competitive task in immersive virtual reality. Each conversational partner controlled either a customized avatar body or an abstract cube that responded to their movements. We compared the movements of the actual user pairs between the two conditions, and to an artificial “pseudosynchrony” dataset composed of the movements of randomly combined participant pairs who did not actually interact. We found stronger positive and negative correlations between real pairs compared to pseudosynchronous pairs, providing evidence for naturally occurring nonverbal synchrony between pairs in virtual reality. We discuss this in the context of the relationships between avatar appearance, task success, social closeness and social presence. [ABSTRACT FROM AUTHOR]

In this study, we assumed that correct functional movements for weightlifting can be learned with the help of a music-based biofeedback system. We compared musical feedback with verbal feedback from experienced trainers using two independent groups. The focus was on one specific movement called deadlift. Physical parameters under considerations were the spine (i.e. loss of midline stability resulting in flexion) and the forward displacement of the barbell during the repetitions relative to the mid-foot. We recruited 31 recreational weight lifters (21-42 years of age). Results revealed that both feedback types are effective in improving the movements for deadlift. No significant differences were found across the two feedback types, neither in terms of movement, nor in terms of clarity and motivation. The results suggest that the proposed feedback system is a valid tool for technology-aided training and self-training practices. [ABSTRACT FROM AUTHOR]

Background and aim: Workplace Social Capital has been suggested as a useful concept when addressing organizational and social factors of the work environment. The overall aim of the present study is to establish and evaluate the construct validity of a measure of Workplace Social Capital based on the operationalization suggested in the third version of the Copenhagen Psychosocial questionnaire. Methods: The present study is based on data collected as part of a validation and development project for the use of the Swedish version of COPSOQ at workplaces and includes responses from 1316 human service workers answering a workplace survey. Six items from scales for organizational justice, vertical trust and horizontal trust in COPSOQ III were included in the analyses. Rasch Analysis was used for scale validation. Results: The analyses showed that the psychometric properties of the suggested COPSOQ scale for Workplace Social Capital were satisfactory after accommodation for local dependency. Each individual item worked as intended, the scale was unidimensional and functioned invariantly for women and men, and for younger and older employees. The scale was furthermore found to be valid for use for distinguishing groups, not individuals. Conclusion: We have established that the scale for Workplace Social Capital measured by COPSOQ III is valid for distinguishing groups, e.g. work teams. The scale exhibits good construct validity as it satisfies the measurement criteria defined by the Rasch model. [ABSTRACT FROM AUTHOR]

Several lifetime distributions have played an important role to fit survival data. However, for some of these models, the computation of maximum likelihood estimators is quite difficult due to presence of flat regions in the search space, among other factors. Several well-known derivative-based optimization tools are unsuitable for obtaining such estimates. To circumvent this problem, we introduce the AdequacyModel computational library version 2.0.0 for the R statistical environment with two major contributions: a general optimization technique based on the Particle Swarm Optimization (PSO) method (with a minor modification of the original algorithm) and a set of statistical measures for assessment of the adequacy of the fitted model. This library is very useful for researchers in probability and statistics and has been cited in various papers in these areas. It serves as the basis for the Newdistns library (version 2.1) published in an impact journal in the area of computational statistics, see . It is also the basis of the Wrapped library (version 2.0), see . A third package making use of the AdequacyModel library can be found in . In addition, the proposed library has proved to be very useful for maximizing log-likelihood functions with complex search regions. The library provides a greater control of the optimization process by introducing a stop criterion based on a minimum number of iterations and the variance of a given proportion of optimal values. We emphasize that the new library can be used not only in statistics but in physics and mathematics as proved in several examples throughout the paper. [ABSTRACT FROM AUTHOR]

Spatial scan statistics are widely used for cluster detection analysis in geographical disease surveillance. While this method has been developed for various types of data such as binary, count, and continuous data, spatial scan statistics for matched case-control data, which often arise in spatial epidemiology, have not been considered. We propose spatial scan statistics for matched case-control data. The proposed test statistics consider the correlations between matched pairs. We evaluate the statistical power and cluster detection accuracy of the proposed methods through simulations compared to the Bernoulli-based method. We illustrate the proposed methods using a real data example. The simulation study clearly revealed that the proposed methods had higher power and higher accuracy for detecting spatial clusters for matched case-control data than the Bernoulli-based spatial scan statistic. The cluster detection result of the real data example also appeared to reflect a higher power of the proposed methods. The proposed methods are very useful for spatial cluster detection for matched case-control data. [ABSTRACT FROM AUTHOR]

Individual plant cells have a genetic circuit, the circadian clock, that times key processes to the day-night cycle. These clocks are aligned to the day-night cycle by multiple environmental signals that vary across the plant. How does the plant integrate clock rhythms, both within and between organs, to ensure coordinated timing? To address this question, we examined the clock at the sub-tissue level across Arabidopsis thaliana seedlings under multiple environmental conditions and genetic backgrounds. Our results show that the clock runs at different speeds (periods) in each organ, which causes the clock to peak at different times across the plant in both constant environmental conditions and light-dark (LD) cycles. Closer examination reveals that spatial waves of clock gene expression propagate both within and between organs. Using a combination of modeling and experiment, we reveal that these spatial waves are the result of the period differences between organs and local coupling, rather than long-distance signaling. With further experiments we show that the endogenous period differences, and thus the spatial waves, can be generated by the organ specificity of inputs into the clock. We demonstrate this by modulating periods using light and metabolic signals, as well as with genetic perturbations. Our results reveal that plant clocks can be set locally by organ-specific inputs but coordinated globally via spatial waves of clock gene expression. [ABSTRACT FROM AUTHOR]

There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study. [ABSTRACT FROM AUTHOR]

This paper provides an empirical analysis of the distributional properties and statistical regularities of virtual, intra-virtual and traditional currency exchange rates. To perform the analysis, the most relevant virtual, intra-virtual and foreign currency exchange rates between October 2015 and December 2018 are examined. The analysis shows that, in spite of their differing mode of formation, daily log-returns of all currency types share tent-shaped empirical densities, one of the characteristics of a Laplace distribution at semi-log scale. This peculiar property has also been examined thoroughly in other fields of economic literature. Moreover, the empirical results show that virtual and traditional currencies hold the same functional form, even after the 2018 hype. However, in spite of these similarities virtual and intra-virtual currencies display fatter tails and steeper towering peaks than regular foreign currencies which underscores the rather speculative nature of this asset class. [ABSTRACT FROM AUTHOR]

Gene-environment (GxE) interaction is one potential explanation for the missing heritability problem. A popular approach to genome-wide environment interaction studies (GWEIS) is based on regression models involving interactions between genetic variants and environment variables. Unfortunately, GWEIS encounters systematically inflated (or deflated) test statistics more frequently than a marginal association study. The problematic behavior may occur due to poor specification of the null model (i.e. the model without genetic effect) in GWEIS. Improved null model specification may resolve the problem, but the investigation requires many time-consuming analyses of genome-wide scans, e.g. by trying out several transformations of the phenotype. It is therefore helpful if we can predict such problematic behavior beforehand. We present a simple closed-form formula to assess problematic behavior of GWEIS under the null hypothesis of no genetic effects. It requires only phenotype, environment variables, and covariates, enabling quick identification of systematic test statistic inflation or deflation. Applied to real data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), our formula identified problematic studies from among hundreds GWEIS considering each metabolite as the environment variable in GxE interaction. Our formula is useful to quickly identify problematic GWEIS without requiring a genome-wide scan. [ABSTRACT FROM AUTHOR]

Introduction: The closed testing principle provides strong control of the type I error probabilities of tests of a set of hypotheses that are closed under intersection such that a given hypothesis H can only be tested and rejected at level α if all intersection hypotheses containing that hypothesis are also tested and rejected at level α. For the higher order hypotheses, multivariate tests (> 1df) are generally employed. However, such tests are directed to an omnibus alternative hypothesis of a difference in any direction for any component that may be less meaningful than a test directed against a restricted alternative hypothesis of interest. Methods: Herein we describe applications of this principle using an α-level test of a surrogate hypothesis such that the type I error probability is preserved if such that rejection of implies rejection of H. Applications include the analysis of multiple event times in a Wei-Lachin test against a one-directional alternative, a test of the treatment group difference in the means of K repeated measures using a 1 df test of the difference in the longitudinal LSMEANS, and analyses within subgroups when a test of treatment by subgroup interaction is significant. In such cases the successive higher order surrogate tests can be aimed at detecting parameter values that fall within a more desirable restricted subspace of the global alternative hypothesis parameter space. Conclusion: Closed testing using α-level tests of surrogate hypotheses will protect the type I error probability and detect specific alternatives of interest, as opposed to the global alternative hypothesis of any difference in any direction. [ABSTRACT FROM AUTHOR]

Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count test directly targets distributional difference between groups, therefore it is sensitive to nonlinear effects. Second, we relax the independence assumption and adapt Efron’s procedure to adjust for the dependence between features. The performance of the proposed procedure, in terms of statistical power and false discovery rate, is illustrated by simulated data. We apply the new method to three genomic datasets to identify genes associated with colon, cervical and prostate cancers. [ABSTRACT FROM AUTHOR]

In this note we present a robust bootstrap test with good Type I error control for testing the general hypothesis H0: ρ = ρ0. In order to carry out this test we use what is termed a surrogate bootstrap distribution. The test was inspired by the studentized permutation for testing H0: ρ = 0, which was proven to be exact in certain scenarios and asymptotically correct overall. We show that the bootstrap based test is robust to a variety of distributional scenarios in terms of proper Type I error control. [ABSTRACT FROM AUTHOR]

Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods. [ABSTRACT FROM AUTHOR]

Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n = 1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n = 1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package “FunSPU”. [ABSTRACT FROM AUTHOR]

Persistent neural activity that encodes online mental representations plays a central role in working memory (WM). However, there has been debate regarding the number of items that can be concurrently represented in this active neural state, which is often called the “focus of attention.” Some models propose a strict single-item limit, such that just 1 item can be neurally active at once while other items are relegated to an activity-silent state. Although past studies have decoded multiple items stored in WM, these studies cannot rule out a switching account in which only a single item is actively represented at a time. Here, we directly tested whether multiple representations can be held concurrently in an active state. We tracked spatial representations in WM using alpha-band (8–12 Hz) activity, which encodes spatial positions held in WM. Human observers remembered 1 or 2 positions over a short delay while we recorded electroencephalography (EEG) data. Using a spatial encoding model, we reconstructed active stimulus-specific representations (channel-tuning functions [CTFs]) from the scalp distribution of alpha-band power. Consistent with past work, we found that the selectivity of spatial CTFs was lower when 2 items were stored than when 1 item was stored. Critically, data-driven simulations revealed that the selectivity of spatial representations in the two-item condition could not be explained by models that propose that only a single item can exist in an active state at once. Thus, our findings demonstrate that multiple items can be concurrently represented in an active neural state. [ABSTRACT FROM AUTHOR]

This study analyzes the core energy consumption among countries’ specific variables by Environmental Kuznets Curve hypothesis (EKC), for a panel data of 29 (14 developed and 15 developing) countries during the period of 1977–2014. By assessing Generalized Method of Moments (GMM) regressions with first generation tests such as common root, individual Augmented Dickey-Fuller (ADF), and individual root-Fisher-PP which have been computed individually, the results confirm the EKC hypothesis in the case of emissions of solid, liquid, gases, manufacturing industries and also construction. Hence, we computed the cointegration test by Pedroni Kao from Engle-Granger based and Fisher. Since the variables are co-integrated, a panel vector error correction model is estimated in GDP per capita, emission from manufacturing industries, arms import, commercial service export, and coal rent, in order to perform Pairwise Granger Causality test and indicate Vector Error Correction (VEC), with co-integration restrictions. Moreover, the statistical finding from VEC short-run unidirectional causality from GDP per capita growth to manufacturing industries and coal rent, as well as the causal link with manufacturing industries and commercial service export. Additionally, there occurred no causal link among economic growth, arm import and coal rent. [ABSTRACT FROM AUTHOR]

The omnibus test is commonly applied to evaluate the overall disparity between group means in ANOVA. Alternatively, linear contrasts are more informative in detecting specific pattern of mean differences that cannot be obtained via the omnibus test. This article concerns power and sample size calculations for contrast analysis with heterogeneous variances and budget concerns. Optimal allocation procedures for the Welch-Satterthwaite tests of standardized and unstandardized contrasts are presented to minimize the total sample size with the designated ratios, to meet a desirable power level for the least cost, and to attain the maximum power performance under a fixed cost. Currently available methods rely exclusively on simple allocation formula and direct rounding rule. The proposed allocation strategies combine the computing techniques of nonlinear optimization search and iterative screening process. Numerical assessments of a randomized control trial for the overcoming depression on the Internet are conducted to demonstrate and confirm that the approximate procedures do not guarantee optimal solution. The suggested approaches extend and outperform the existing findings in methodological soundness and overall performance. The corresponding computer algorithms are developed to implement the recommended power and sample size calculations for optimal contrast analysis. [ABSTRACT FROM AUTHOR]

The accumulation of knowledge required to produce economic value is a process that often relates to nations economic growth. Some decades ago many authors, in the absence of other available indicators, used to rely on certain measures of human capital such as years of schooling, enrollment rates, or literacy. In this paper, we show that the predictive power of years of education as a proxy for human capital started to dwindle in 1990 when the schooling of nations began to be homogenized. We developed a structural equation model that estimates a metric of human capital that is less sensitive than average years of education and remains as a significant predictor of economic growth when tested with both cross-section data and panel data. [ABSTRACT FROM AUTHOR]

This paper develops the ability of the normal inverse Gaussian distribution (NIG) to fit the returns of bitcoin (BTC). As the first cryptocurrency created, the behavior of this new asset is characterized by great volatility. The lack of a proper definition or classification under existing theory exacerbates this property in such a way that explosive periods followed by a rapid decline have been observed along the series, meaning bubble episodes. By detecting the periods in which a bubble rises and collapses, it is possible to study the statistical properties of such segments. In particular, adjusting a theoretical distribution may help to determine better strategies to hedge against these episodes. The NIG is an appropriate candidate not only because of its heavy-tailed property but also because it has been proven to be closed under convolution, a characteristic that can be implemented to measure multivariate value at risk. Using data on the price of BTC with respect to seven of the main global currencies, the NIG was able to fit every time segment despite the bubble behavior. In the out-of-sample tests, the NIG was proven to have an adjustment similar to that of a generalized hyperbolic (GH) distribution. This result could serve as a starting point for future studies regarding the statistical properties of cryptocurrencies as well as their multivariate distributions. [ABSTRACT FROM AUTHOR]