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• Temporal correlations exist in the GRACE TWS time series, resulting in the presence of colored noise. • To show the impact of these correlations, we apply autoregressive noise model and variance component estimation model. • Neglecting these correlations leads to underestimation of trend uncertainty, misinterpreting its significance. Temporal correlations are prevalent in most geophysical time series data, leading to what is known as colored noise, which become more apparent in the frequency domain rather than time domain. Neglecting this type of noise in modeling can result in underestimated standard errors for the parameters of the model used to analyze the time series. As a result, statistical tests misinterpret the significance of these parameters, such as the overall trend rate, because the "signal to noise" ratio becomes incorrectly larger than it should be. In this study, we investigate the temporal correlations in the time series data from the Gravity Recovery and Climate Experiment (GRACE) mission. GRACE and its successor mission GRACE Follow-On (GRACE-FO) have been successfully employed to monitor global total water storage anomalies over two decades since its initial launch in 2002. We primarily utilize monthly GRACE mascon (mass concentration) solutions provided by NASA Goddard Space Flight Center (GSFC), examining both regional and global scales. The power spectrum density of the residuals, calculated after applying standard harmonic regression to each mascon time series on Earth's land, reveals an average spectral index of κ = −1. This indicates the predominance of flicker noise, one of the most common forms of colored noise in geodetic time series. Notably, the noise becomes more Brownian (κ < −1) in regions with intense hydrological events, while it tends to be more white noise (κ → 0) in areas with less water circulation compared to other land regions. This observation suggests that unmodelled hydrological events, such as droughts, floods, and ones associated with ice-melting, contribute to the residuals as colored noise in the time series. To account for this noise in our time series modeling, we primarily consider autoregressive (AR) moving average (MA) process, commonly known as ARMA, in addition to the standard variance component estimation for noise amplitudes. We focus on individual mascon blocks in the Türkiye Region and mascon solutions for 24 world's major river basins. Our findings demonstrate that an ARMA(1,1) model is sufficient for describing the noise in these regions. The results underscore the importance of considering colored noise, as it leads to approximately three times larger standard errors for the overall trend rate. This substantially impacts the significance of the trend rates. [ABSTRACT FROM AUTHOR]

• Distractions caused by fellow drivers (DFDs) have received little attention. • Associations between DFDs and other latent factors were evaluated using SEM. • DFDs are positively associated with stress among recipient drivers. • Stress is positively associated with anxiety-based performance deficits. • DFDs and stress are positively associated with crash propensity. Introduction: Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity. Method: Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses. Results: The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity. Practical applications: The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs. [ABSTRACT FROM AUTHOR]

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management. [ABSTRACT FROM AUTHOR]

This paper analyses the determinants of Portuguese Banking efficiency using a sample of 45 banks from 2005 to 2019. Simar and Wilson's two-stage approach [1] has been applied. Data envelopment analysis (DEA) was used in the first stage to estimate efficiency scores. In the second stage, a truncated regression was estimated with a double bootstrap to test the significance of the relationship between bank efficiency and its determinants. The results obtained indicate that, in general, Portuguese banking shows good levels of pure technical efficiency but poor results in scale efficiency, thus deteriorating the levels of total technical efficiency. After a decrease in total technical efficiency between 2005 and 2011, from 2012 onwards, it shows a positive trend. Our results also show that larger banks, better-capitalised, more profitable, with less credit risk and less diversified sources of income, are the most efficient. These results have implications for public policies adopted by the government and regulated by the competent authorities for the sector studied. Namely, creating large, well-capitalized banks should be promoted, preventing excess credit risk. [ABSTRACT FROM AUTHOR]

The detrending-moving-average-based multivariate linear regression has been recently introduced as a new tool to probe into multiscale dependent behavior between multivariate data. It well solves the dependence among the nonstationary variates while classic multivariate linear regression dose not. The real-world applications demonstrate its versatility and robustness. However, the statistical significance of this new model needs to be further improved mathematically not just empirically. Hence, the main motivation of this paper is to provide a solid theoretical basis for the latest regression framework. Specifically, four propositions and three theorems are deduced theoretically to support the statistical significance test of detrending-moving-average-based multivariate linear regression model. These propositions and theorems revolve around hypothesis testing of each partial regression coefficients and regression model. They can examine the statistical significance at every given scales. Moreover, we also resort to synthetic time series to verify the performances of the proposed statistical test procedure. By using those statistical tests, we investigate the multiscale interdependence between the crude oil price and the exchange rates among oil-importing and oil-exporting countries. Some interesting findings show that the dependent behavior between the oil price and exchange rates varies at various scales, which cannot be achieved from classical multivariate regression model. • Statistical properties are derived mathematically for the model of DMA-based MLR. • Artificial experiments are also conducted around the statistical test of new model. • The scale-dependent co-movement behavior is investigated between crude oil price and exchange rate by the proposed methods. • The co-movement behavior is compared before and during the oil bust. [ABSTRACT FROM AUTHOR]

• Innovative phenotype-genotype association analysis for categorical genomic data having non-ignorable missing values. • Statistical learning method of random forest is used for variable selection in the analysis. • Weighted logistic ridge regression with EM algorithm is used for missing data imputation in the analysis. • Linear statistical hypothesis testing is used for determining the missingness mechanism in the analysis. • An application to analyzing real data from Australia breast cancer genome-wide association study (GWAS). Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an effective and efficient procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingness mechanism in genomic data. It is developed by integrating the statistical learning methods of random forest for variable selection, joint weighted logistic ridge regression with EM algorithm for missing data imputation, and linear statistical hypothesis testing for determining the missingness mechanism. Two simulated genomic datasets are used to undertake the phenotype-genotype association analysis by the proposed procedure, with the performance validated. The proposed procedure is then applied to analyze a real data set from breast cancer GWAS. [ABSTRACT FROM AUTHOR]

The article discusses the issue of false positives in the era of big data and high-frequency health measurement technologies ,published in the periodical, October 2023.

Reproducibility of research is poor; this may be because many articles report statistically significant findings that are false positives. Two potential solutions are to lower the P -value for statistical significance testing from 0.05 to 0.005 and to report the minimum false-positive risk (minFPR). This study determined these metrics for randomised controlled trials (RCTs) in general anaesthesiology journals. We identified superiority RCTs published between January 1, 2019 and March 15, 2021 from seven leading anaesthesia journals. P -values for primary outcomes were collected, and minFPRs for these outcomes were calculated using a formula assuming a 50% prior probability of an intervention being effective (minFPR 50). The primary outcomes were the percentage of RCTs maintaining statistical significance at P <0.005 and minFPR 50. We included 318 RCTs. P -values below 0.05 were reported in 205/318 (64%) of RCTs. Of these 205 RCTs, 119/205 (58%) maintained statistical significance at the P <0.005 threshold. The mean (standard deviation) minFPR 50 was 22% (20). At P =0.005, the minFPR 50 was approximately 5%. These proposed metrics aimed at mitigating reproducibility concerns would call a significant portion of the anaesthesiology literature into question. We found a minFPR of 22% and determined that 42% of primary outcomes would not maintain statistical significance if the P -value threshold changed from 0.05 to 0.005. These findings could partially explain the lack of reproducibility of research findings. [ABSTRACT FROM AUTHOR]

Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant Convolutional Neural Network (G-CNN) was used as an encoder of the state-of-the-art self-supervised methods SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2), enabling it to effectively learn the representation of globally oriented feature maps. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labeled data. Our findings demonstrate that our semi-supervised approach outperforms existing state-of-the-art methods across several metrics, including cluster quality, convergence rate, accuracy, precision, recall, and the F1-score. Moreover, statistical significance testing via a t-test revealed that our method surpasses the performance of a fully supervised G-CNN. This study emphasizes the importance of semi-supervised learning in radio galaxy classification, where labeled data are still scarce, but the prospects for discovery are immense. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) predictions are uncertain; but how uncertain? Statistical inference estimates the probabilities of uncertainty from a sample drawn from a population. Assessing the statistical significance of accuracies reported by DL remains largely unexplored. A framework to do so would usefully support a range of applications, and in particular group classifications from neuroimages where, for operational reasons, sample sizes are necessary limited and thus often do not generalise well. We applied a random-effects inference based on a label permutation test to calculate the statistical significance of K-fold cross-validation (CV) from statistical power and Type-I error rates. Our hypothesis is that in low sample size scenarios, the use of resubstitution with upper bound correction (RUB) as a validation would mitigate the debate on the generalisation ability of DL models. The derived framework enables testing such generalisation ability of DL models as feature extraction methods. A combination of autoencoders and support vector machines as feature extraction and classification models is evaluated in a case-control analysis of Alzheimer's disease with well-established outcomes. We found that RUB slightly outperforms K-fold CV as a validation method, especially estimating statistical power in the most heterogeneous samples. Therefore, we suggest RUB as potent and valid method for DL with neuroimages in terms of bias, variance and computational demand. • A non-parametric framework is used to assess statistical significance. • The complexity of a neural network affects its generalisation ability adversely. • The CV estimate is statistically consistent only when the classifier is stable. • Cross-validation in limited sample databases is not advisable. • In neuroimaging, using RUB offers more advantages than cross-validation. [ABSTRACT FROM AUTHOR]

The increasing use of digital technologies has allowed organizations to improve their business processes, enhancing the productivity and efficiency of their operations. The implementation of Robotic Process Automation (RPA), a technology that allows replicating human actions in existing systems, given its benefits, can play an important role in achieving this goal, often referred as the digital transformation of organizations. However, the benefits of RPA implementation may not be perceived equally by managers, those responsible or that required its implementation, and lower-level employees. The work described in this paper aims not only to assess the perception that both groups have of the impacts of technology, but also to understand whether the use of robots by the employees of a shared services company positively influences their perception of the technology and whether it affects developments in additional robots in the future. A questionnaire, underpinned in a multidimensional evaluation model of the technology and given to all the company's employees, is used as the main research instrument, while results are analysed and interpreted using descriptive statistical techniques and hypothesis testing. We argue that this study may be an important contribution to the company for a better understanding of its employees' perception of RPA, allowing the tailoring of its future communication strategies to facilitate the acceptance of the technology and, thus, enhance the success and effectiveness of its implementation. Furthermore, the multidimensional evaluation model developed enhances the existing literature on the topic of RPA impacts, more specifically in terms of evaluating possible differences in perceptions between managers and other employees. [ABSTRACT FROM AUTHOR]

• The DERs models have an impact on all studied variables. • The deterministic and probabilistic approaches show differences in their results. • The probabilistic approach and inferential statistics complement the analysis. • Co-simulation reduces calculation time and helps with results analysis. Distributed energy resources (DER) can have significant effects on distribution networks. Their growing integration makes it necessary to conduct characterisation studies on the effects of several operating scenarios. Such studies apply deterministic or probabilistic techniques to the modelling and simulation of networks and DER. Nevertheless, the lack of studies that integrate three DER within the same research to quantify the potential effects that each one singly and all jointly could have on the networks is perceptible. In addition, the analysis of the most significant number of parameters studied for correctly interpreting the results and using inferential statistics can be helpful. Moreover, we examined various modelling and simulation approaches to evaluate the differences and similarities among multiple scenarios. Accordingly, this paper presents and details deterministic and probabilistic analysis approaches to characterise the hourly impact of photovoltaic systems, storage units, and electric vehicle charging stations in a 13-node IEEE test feeder. This elucidates DER integration in networks and could guide future studies. Estimating the individual or collective effects of a DER consists of quantification and hourly comparison of RMS values of voltages and currents, voltage unbalance, chargeability of transformers, and power losses. We collected the data for analysis using a co-simulation PowerFactory-Python for eight operating scenarios. The hourly stochastic comparison between scenarios consisted of calculating the difference in sampling means and applying hypothesis testing of inferential statistics. This comparative analysis verifies that the applied approach (deterministic or probabilistic), specific hour of analysis and simultaneous number of DER in a simulated scenario influence the results. [ABSTRACT FROM AUTHOR]

We discuss a newly published study examining how phrases are used in clinical trials to describe results when the estimated P-value is close to (slightly above or slightly below) 0.05, which has been arbitrarily designated by convention as the boundary for 'statistical significance'. Terms such as 'marginally significant', 'trending towards significant', and 'nominally significant' are well represented in biomedical literature, but are not actually scientifically meaningful. Acknowledging that 'statistical significance' remains a major determinant of publication, we propose that scientific journals de-emphasise the use of P-values for null hypothesis significance testing, a purpose for which they were never intended, and avoid the use of these ambiguous and confusing terms in scientific articles. Instead, investigators could simply report their findings: effect sizes, P-values, and confidence intervals (or their Bayesian equivalents), and leave it to the discerning reader to infer the clinical applicability and importance. Our goal should be to move away from describing studies (or trials) as positive or negative based on an arbitrary P-value threshold, and rather to judge whether the scientific evidence provided is informative or uninformative. [ABSTRACT FROM AUTHOR]

With the rapid development of sensors and communication technologies, it has become easy to collect large-scale and long-term crowd movement positioning data, which brings new opportunities for studying crowd movement patterns. This paper proposes a novel Statistical and Density-Based Clustering algorithm (SDBC) to identify implicit significant spatial aggregation patterns in geographical flow data. Unlike existing flow clustering algorithms, this method identifies the hot spots of origin-destination (OD) flows based on local spatial statistics and density-growing clustering. It also evaluates the significance of the identified geographic flow clusters, effectively reducing the identification of spurious clusters generated by chance in data. In our method, the spatial neighborhood of each flow is first obtained based on spatial proximity, temporal similarity, and directional similarity. Then, the number of flows in the spatial neighborhood of each flow is calculated and used as the density measure. Based on this, high-density flows are automatically detected using local spatial aggregation statistics, and a hierarchical density-based clustering strategy is developed to merge adjacent high-density flows to generate candidate flow clusters. Finally, we perform permutation tests to infer the statistical significance of each flow cluster and eliminate the candidate clusters generated by chance. Experiments on synthetic data and real-world taxi trajectory data were conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify the statistically significant flow clusters of different shapes and densities and performs better than the available state-of-the-art flow clustering algorithms. • Local spatial autocorrelation analysis is extended for geographical flow data to adaptively determine the paprameters. • Statisical constraints are used and the significance of each flow cluster is evaluated with Monte Carlo permutation test. • The proposed method can find natural clusters in geographical flow data, avoiding the false clusters generated by chance. [ABSTRACT FROM AUTHOR]

• Made a small-area analysis of greenspace exposure and heat-mortality associations. • Examined which type and location of greenspace matter most in heat health risk reduction. • Unlike grassland, forests yielded significant health benefits under heat. • Greenspace, especially forests, within a 1 km radius of residents matter most. • More apparent effects were observed for greenspace nearby than area-level exposure. While vegetation type, population density and proximity to greenspaces have been linked to human health, what type and location of greenspace matter most have remained unclear. In this context, there are question marks over investment-style metrics. This paper aims at establishing what vegetation type may matter most in modifying heat-mortality associations, and what the optimal buffer distances of total and specific types of greenspace exposure associated with reduced heat-related mortality risks are. We conducted small-area analyses using daily mortality data for 286 Territory Planning Units (TPUs) across Hong Kong and 1 × 1 km gridded air temperature data for the summer months (2005–2018). Using a case time series design, we examined effect modifications of total and specific types of greenspaces, as well as population-weighted exposure at varying buffer distances (200–4000 m). We tested the significance of effect modifications by comparing relative risks (RRs) between the lowest and highest quartiles of each greenspace exposure metric; and explored the strength of effect modifications by calculating the ratio of RRs. Forests, unlike grasslands, showed significant effect modifications on heat-mortality associations, with RRs rising from 0.98 (95 %CI: 0.92,1.05) to 1.06 (1.03, 1.10) for the highest to lowest quartiles (p -value = 0.037) The optimal distances associated with the most apparent effects were around 1 km for population-weighted exposure, with the ratio of RRs being 1.424 (1.038,1.954) for NDVI, 1.191 (1.004,1.413) for total greenspace, and 1.227 (1.024,1.470) for forests. A marked difference was observed in terms of the paired area-level and optimal distance-based exposure to total greenspace and forests under extreme heat (p -values < 0.05). Our findings suggest that greenspace, particularly nearby forests, may significantly mitigate heat-related mortality risks. [ABSTRACT FROM AUTHOR]

• Spatial plant diversity indices help monitor diversity trends. • We proposed a framework for analysing competing diversity indices. • We tested the dissimilarity coefficient accounting for size inequality. • We identified higher test sensitivity for the dissimilarity coefficient. • The dissimilarity coefficient has a solid statistical foundation. • The dissimilarity coefficient principle can form spatial test functions. Worldwide biodiversity loss is perceived as a major threat and is likely to be enforced by ongoing climate change. Continued monitoring of biodiversity can assist in compensating for decreasing biodiversity by goal-oriented conservation management. Plant size diversity, also termed size inequality and size hierarchy, has often been neglected in studies of biodiversity, but plays a crucial role in many ecosystems, e.g. in forests. Several competing diversity indices and other characteristics of spatial plant size inequality have been proposed but to date no protocols or guidelines exist for evaluating their relative merits. In our study, we proposed a broad framework for such an analysis. In order to put this framework to a test, we revisited the dissimilarity coefficient, a somewhat ignored but promising spatial size inequality index published at the end of the 1990s, identified its nearest index competitors and analysed their performances in different mathematical-statistical contexts. We learned that the dissimilarity coefficient is more sensitive in statistical significance tests relating to three very different summary characteristics than its closest competitor, the size differentiation index. In addition the dissimilarity coefficient has a more solid foundation than the differentiation index, since it is based on the well-known coefficient of variation. The dissimilarity coefficient can be recommended in a wide range of plant diversity applications. The principle of the dissimilarity coefficient can also be used to define an effective mark correlation function. We recommend our evaluation framework for use in similar analyses of competing diversity characteristics. [ABSTRACT FROM AUTHOR]

Healthy street trees provide important ecosystem services to cities, but are under increasing stress from urbanisation and climate change, including drought. Traditional field observations are limited in their ability to provide city-wide and regular monitoring of the drought response of street trees, which is essential for their effective management. To overcome this, we propose a novel workflow using freely available remote sensing imagery from Sentinel-2 to identify temporal and species-specific drought response patterns of street trees. We evaluate the workflow on a sample of 2514 mature street trees of the seven most common street tree species in Leipzig, Germany. For each tree, we generate time series of eight vegetation indices from 2017 to 2022, namely enhanced vegetation index (EVI), normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), each using the 10 m and 20 m resolution near-infrared band (EVI-10, EVI-20, NBR-10, NBR-20, NDVI-10, NDVI-20), as well as red edge normalised difference vegetation index (RENDVI) and red-green vegetation index (RGVI). They form the basis for a correlation analysis with the meteorological drought indicator standardised precipitation evapotranspiration index (SPEI) and for annual growing season integrals, which we subtract from those of the base year 2017. We use boxplots and statistical hypothesis testing to examine differences between and within tree species and years. The results show positive relationships between the eight vegetation indices and the SPEI, with the NBR-20 having the highest correlation coefficients. We also find significant differences in the drought response of several tree species, with Quercus robur being the most drought responsive and Platanus x acerifolia being the least. While most tree species have significantly smaller growing season integrals in the drought years 2018 and 2020 than in the non-drought years 2017 and 2021, the effects are not as pronounced in the drought years 2019 and 2022. Uncertainties arise, for example, from spectral signal variations caused by adjacent land use. Nevertheless, the proposed workflow holds promise for incorporation into holistic urban green management solutions and mixed-method approaches for further research into the causes and consequences of drought-induced damage to street trees. • Remote sensing-based indicators are used to monitor street tree health. • We assess vegetation indices and phenological metrics during drought conditions. • In this way, we identify species specific and temporal trends in drought response. • Monitoring urban greenery under climate change is important for ecosystem services. [ABSTRACT FROM AUTHOR]

This review is an update of a previous systematic review and assesses the evidence for the association of work-related physical and psychosocial risk factors and specific disorders of the shoulders. Medline, Embase, Web of Science Core Collection, Cochrane Central and PsycINFO were searched and study eligibility and risk of bias assessment was performed by two independent reviewers. A total of 14 new articles were added with the majority focusing on rotator cuff syndrome (RCS) with seven studies. Nine articles reported psychosocial exposures in addition to physical exposures. The strongest evidence was found for the association between elevation, repetition, force and vibration and the occurrence of SIS and tendinosis/tendonitis. Evidence also suggests that psychosocial exposures are associated with the occurrence of RCS and tendinosis/tendonitis. Other findings were inconsistent which prevents drawing strong conclusions. [ABSTRACT FROM AUTHOR]

Previous studies on Human Factors and Ergonomics (HFE) have primarily examined the impact of Work-From-Home (WFH) on worker health and well-being, yet little research has examined the optimal implementation process of WFH programs. Work systems perspective suggests that organizational policies, leadership, and psychological factors collectively influence the success of organizational change efforts. Our study explored the roles of managerial/supervisory, psychological, and organizational policy factors in facilitating the relationship between employees' HFE awareness and their acceptance and satisfaction with the WFH arrangement. Using data from 3195 knowledge workers in the US who use computers as their primary work tool and have worked from home at least one day in the past 30 days, we employed structural equation modeling to test our hypotheses. Transformational HFE leadership and employees' general self-efficacy are pivotal in implementing ergonomic WFH arrangements. The combination of employees' HFE awareness, transformational HFE leadership, and adequate levels of self-efficacy may foster positive process outcomes (e.g., readiness for WFH arrangement, workspace design satisfaction) in WFH arrangements. Efforts that are coordinated across organizational levels determine the effectiveness of organizational change. • Optimizing managerial, psychological, and organizational policy-related factors is vital for effective ergonomic WFH arrangements. • Attention should be given to process outcomes such as readiness for change and satisfaction with home workspace design for ergonomic WFH arrangements. • Promoting transformational leadership and enhancing self-efficacy are valuable strategies for ergonomic WFH arrangements. [ABSTRACT FROM AUTHOR]

Previous research has shown there are particular patterns of license plate designs that are easier to recall. Missouri license plate patterns (AB1-C2D) somewhat diverge from what research suggests works best for recall. The current study examined whether incorporating color into license plates would improve recall, and also whether awareness or explanation of license plate formats would affect recall accuracy. Across two experiments, participants viewed license plate stimuli with and without color and attempted to recall them. The hypothesis was that incorporating color would improve recall, but the hypothesis was not supported. Results also did not show that prior exposure or explanation of formats affected accuracy. Future research should explore additional ways to improve license plate designs that would be easy to implement. Such improvements to license plate design would be useful because efforts to improve the public's awareness of formats would be expensive and likely ineffective. [ABSTRACT FROM AUTHOR]

The chattering effect, robustness and stability are the major issues in a typical Sliding Mode Control (SMC). The selection of sliding manifold parameters are complicated, time-consuming and tedious. The parameter convergence is also a challenging work. Hence, the paper accords a global optimization of Second-Order Sliding Mode Controller (SOSMC) parameters with new sliding surface using the Jaya algorithm for non-linear uncertain process tank systems. The controller (SOSMC) role is to direct the sliding surface and it's first order time derivative to zero in a finite time. The Jaya algorithm uses 'optimal features' by updating the solutions in populations. The effectiveness of proposed strategy is evaluated for five objective functions and compared with Artificial Bee Colony (ABC) based SOSMC, classical SOSMC, typical SMC, Non-dominated Sorting Genetic Algorithm based First-order SMC (NSGA-II FOSMC) and Proportional+Integral+Derivative (PID) controller which is validated through real-time experimentation. The asymptotic stability is guaranteed through direct Lyapunov candidate function. The statistical significance of algorithms has been verified by using Friedman, Dunn, critical time efficiency and p -value tests. The Jaya algorithm shows a better performance over reported methods concerning process speed, settling time, rise time, reaching time, overshoot, response time and parameter convergence as explored from simulation, statistical, and experimental results. Furthermore, the elevated dead-time and oscillatory processes have been presented to show the efficacy of proposed strategy. [Display omitted] • The Jaya-SOSMC method has been proposed for process control system. • Non-parametric tests verifies the statistical significance of algorithm. • A performance comparison with the prevalent control designs. • The proposed strategy has better multi-level set-point tracking and disturbance rejection capabilities. [ABSTRACT FROM AUTHOR]

Although there is a growing interest in the concept of climate justice, both in academia and society in general, there is still no consensus regarding the best models and data to represent it. Thus, the objective of this paper is to test and statistically validate the confirmatory factor model for climate justice. To do this, we applied Confirmatory Factor Analysis (CFA), which designs and tests statistically the significance of a multidimensional theoretical construct. The main results were: a) the individual freedoms derived from the capability approach and the climate actions of adaptation and mitigation were empirically confirmed as viable perspectives for assessing climate justice; b) the theoretical constructs of human development, climate actions and climate justice can be constructed based on a set of 25 indicators; and c) the three confirmatory models were successfully tested to validate a theoretical/measurement model for climate justice. We believe that the results observed will provide policymakers and national governments with a new perspective for the alignment of different types of human development programs with climate change actions, helping them on the establishment of possible links between actions taken at the national level and the definition of global goals. • Presence of synergies between the confirmatory model and the Sustainable Development Goals. • The proposition of definition of Climate Justice based on the Capability Approach. • The 25 selected indicators can guide nations in their transition to a fairer low-carbon economy. [ABSTRACT FROM AUTHOR]

This paper aims to establish what are the differences in The Legatum Prosperity Index between 15 non-cooperative tax jurisdictions and the rest of the countries in their geographic regions? Our data were 149,739 records (from 2021) out of the 2,165,925 (2007-2021) available from the Legatum index. We used the Friedman test to verify the significance of the differences. We found that except for Angola and Yemen, the rest of the non-cooperative tax jurisdictions (Bahrain, Cabo Verde, Guyana, Hong Kong, Kuwait, Lebanon, Liberia, Mauritius, Oman, Qatar, Seychelles, Trinidad and Tobago, Singapore) performed better on the Prosperity Legatum index than the countries of the region. Analyzing specifically the tax havens with the best performance in the index, we identify that Singapore achieves a better performance in all 12 pillars, while Hong Kong has a worse performance only in "social capital". All this in comparison to the rest of the countries in the Asia Pacific region. This study opens the door to future works related to the reasons why 13 of the 15 non-cooperative tax jurisdictions perform better on the indicators and elements that make up each of the 12 pillars of prosperity. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Dataset (CORD-19) has systematically gathered scientific contributions for 2.5 years, by collecting and indexing over one million articles—this corpus, however, does not provide an easy-to-access overview of its content. Here, we present the CORD-19 Topic Visualizer (CORToViz), a method and associated visualization tool for inspecting the CORD-19 textual corpus of scientific abstracts. Our method is based upon a careful selection of up-to-date technologies (including large language models), resulting in an architecture for clustering articles along orthogonal dimensions and extraction techniques for temporal topic mining. Topic inspection is supported by an interactive dashboard, providing fast, one-click visualization of topic contents as word clouds and topic trends as time series, equipped with easy-to-drive statistical testing for analyzing the significance of topic emergence along arbitrarily selected time windows. Overall, our pipeline is very fast and its results match our expectations on topic identification (F1-score 0.854). The processes of data preparation and results visualization are completely general and virtually applicable to any corpus of textual documents—thus suited for effective adaptation to other contexts. • Exploring CORD-19 allows to understand which topics were trending during COVID-19. • Topics' temporal dimension shows the evolution of research questions on the pandemic. • Data processing pipelines are replicable to any corpus of text documents with time. • Translating text into temporal visualization achieves powerful abstractions. [ABSTRACT FROM AUTHOR]

This work deals with the problem of designing functional observers for fault diagnosis in nonlinear systems in the presence of noises. It follows up previous work of the authors on design of functional observers (residual generators) for deterministic systems from the point of view of observer error linearization. Here, we consider the effect of noises on residual generation. The effect of sensor noises on the residuals is studied analytically and the associated probability distributions are derived. Following this, a well-known statistical hypothesis testing approach, Generalized Likelihood Ratio, is used to track changes in the mean of the residual to enable robust fault detection. The approach is also extended to process noises (plant-model mismatch) numerically. Throughout the study the methods presented are tested on a non-isothermal CSTR case study. The results show that the fault diagnosis scheme is able to quickly and accurately detect faults in the presence of both sensor and process noises. • Methodology to design observers for fault diagnosis in systems with noises. • Derivation of fault free probability distributions of the residuals in the presence of sensor noises. • Application of Generalized Likelihood Ratio for fault detection. • Extension to process noises (plant-model mismatch). [ABSTRACT FROM AUTHOR]

Background Interactions between genetic and nutritional factors can contribute to the risk of gestational diabetes mellitus (GDM). Objectives We aimed to explore the associations of cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 (CDKAL1) single-nucleotide polymorphism (SNP) rs7747752 and serum concentrations of SFAs with the risk of GDM in Chinese women. Methods We conducted a 1:1 case–control study in a prospective cohort of pregnant women in Tianjin, China. Serum SFA data were collected from a total of 243 women with GDM and their controls matched by maternal age (±1 y). Among them, 207 case–control pairs had high-quality sequencing data. P/L and S/P ratios were defined as palmitic acid (16:0)/lauric acid (12:0) and stearic acid (18:0)/palmitic acid, respectively. Conditional logistic regression analysis was performed to estimate associations of CDKAL1 SNP rs7747752 and serum concentrations of SFAs with the risk of GDM. An additive interaction between rs7747752 and palmitic acid was analyzed to test the contribution of their interaction to the risk of GDM. Results Among the 5 tested SFAs, palmitic acid was positively whereas lauric acid was negatively associated with the risk of GDM. A P/L ratio ≥12.2 and an S/P ratio ≤0.71 were independently and synergistically associated with an increased risk of GDM. The CDKAL1 rs7747752 G > C variant was significantly associated with an increased risk of GDM (P  < 0.05). Furthermore, the presence of the rs7747752 G > C variant increased the OR (95% CI) of high palmitic acid concentration from 1.55 (0.61, 3.97) to 4.34 (2.04, 9.23), with a significant additive interaction. Conclusions The interaction between high serum palmitic acid concentration and the CDKAL1 rs7747752 G > C variant played a critical role in GDM. Given that a hypocaloric low-carbohydrate diet can lower palmitic acid concentrations, it is worthwhile to test whether such a diet is effective in reducing the risk of GDM, especially among women who have both risk factors. [ABSTRACT FROM AUTHOR]

[Display omitted] • Stochastic reconstruction (SR) of complex mixtures composition is considered. • Modified SR method evaluates contribution of different types of analytical data. • 13C NMR data is the main source of isomer distributions information for SR. • Analytical information with low precision may be useless for SR. Stochastic reconstruction (SR) is one of the most usable molecular reconstruction approaches applicable to heavy petroleum cuts, using statistical distribution to describe the structure attribute of molecules in a mixture. Suitable values for distribution parameters are estimated during an optimization process on the base of mixture characterization data. This study focuses on searching the connection between the parameter of structure attribute distributions and different types of experimental data, used for hydrocarbon mixture characterization. An algorithm has been introduced that allows to determine which types of analytical data affect the distribution of structure attributes based on testing statistical hypotheses. The core idea is looking for a significant correlation between calculated properties and distribution parameters, and then comparing the magnitude of parameter impact with measurement precision. It was shown that different types of analytical data can affect different parameters of the reconstructed composition: distillation curve data is the main source of information about distributions variance, while elemental analysis and PNA helps determine mean values for various structure attributes; information concerning the number of branches in molecules are strongly connected with 13C NMR data. [ABSTRACT FROM AUTHOR]

Despite so much research in high altitude area, our existing knowledge is still lacking on otological effects of long-term stay in high altitude. This pilot study was conducted to compare the hearing thresholds of army soldiers at induction and after completion of one year in high altitude area (HAA). Hearing thresholds of 433 soldiers posted in HAA were recorded using pure tone audiometry at the time of induction and second thresholds after one year of stay in high altitude for frequencies of 500Hz, 1KHzs, 2 KHzs and 4 KHzs. The two sets of hearing thresholds for air conduction were compared using paired "t" test for any statistical significance. The mean Pure Tone Audiometry (PTA) thresholds for 433 left ears worsened from 9.43dB to 9.65dB at 500 HZs ; 14.02dB to 14.32dB at 1 KHZs ; 15.04dB to 16.09dB at 2KHzs and 18.63dB to 22.59dB at 4 KHZs. Similarly for right ear, PTA thresholds worsened from 9.43dB to 9.69dB at 500HZs; 13.95dB to 14.34dB at 1 KHZs; 15.38dB to 17.26dB at 2 KHZs and from 18.59dB to 23.06dB at 4KHZs. These results are found to be statistically significant (p<0.05) for all frequencies. This pilot study shows deterioration of hearing thresholds in tested frequencies in both ears after a long stay (one year) in high altitude area. We recommend further structured research on otologic effect of long term stay in high altitude. [ABSTRACT FROM AUTHOR]

The contemporary mobile phone has absolute capabilities to go beyond its core role as an ordinary voice communication device. With the smart concept and mobile application technology, the contemporary mobile phone makes users' lives easier than ever before. WHO declared the Covid-19 situation as a pandemic on 11 March 2020 and it has been an ongoing global pandemic since December 2019. People all around the world are experiencing direct and indirect consequences of the Covid-19 pandemic. However, mobile phone technology can provide sophisticated solutions to cope with a pandemic environment. Thus, as a pilot study in this research, the author focused on Polish consumers' mobile phone usage behaviour towards their online shopping buying behaviour before and during the Covid-19 pandemic time. An online field survey was conducted to collect primary data from the respondents during the month of April 2021, and the total number of participants was recorded as 102. In order to reach conclusions, mainly two types of statistical hypothesis tests were carried out. Hypothesis test 1 was conducted to determine whether the respondents were demonstrating a significant usage increase in the average number of online shopping transactions per month and during the Covid-19 pandemic time. Hypothesis test 2 was conducted to determine whether there was a difference in the average number of online shopping transactions per month during the pandemic time of the respondents based on their age group. Descriptive and inferential statistics, including the matched-pairs test and one-way ANOVA, were applied to the analysis. The researcher is in a position confirm that there is a significant difference in the average number of online shopping transactions per month before and during the Covid-19 pandemic. However, there is no significant difference in the average number of online shopping transactions per month during the pandemic time made by representants of two generations, generation X and Millennials. The results of the study were significant since the primary data collection was done during April 2021. The pilot study will help to understand how the respondents online buying behaviour through mobile phone usage changed during the pandemic. The added value of the conducted pilot study involves filling in a gap regarding the differences and similarities between generational groups on mobile shopping during the Covid-19 pandemic time. [ABSTRACT FROM AUTHOR]

Information retrieval (IR) systems are concerned of the processing of the textual content of the document, the query, and issues of system effectiveness. IR models are means to integrate many sources of evidence on documents to achieve an effective retrieval system. Unlike traditional parameters such as word frequency in documents, semantic information retrieval is an environment that requires the application of semantic techniques. These techniques calculate a degree of query-document relevance to assign the highest rankings to the documents which are semantically closer to the query. To meet the user's information needs and to optimize the performance of IR system, an innovative model was proposed to calculate the relevance of query-document pairs. The proposed model is a hybrid one that based on a query likelihood language model and a conceptual weighting model. It exploits the semantic similarity between document and query concepts and some document/collection statistics. The conducted experiments on the proposed model show improvement of the standard performance measures over the benchmark competitors. A statistical significance test is conducted to exclude the performance random variation of the compared models. Paired t-tests show that the proposed model is highly significant improves the most important measures. [ABSTRACT FROM AUTHOR]

Compressive strength (CS) has been considered as the utmost critical parameter while designing concrete structures. Usually, it is determined through laboratory tests, which are expensive, time consuming, and requires consumptions of materials. Therefore, correct prediction of CS before actual placement of concrete is highly desirable. The relationship among the constituent materials that forms concrete structures is highly nonlinear and necessitates application of intelligent methods. Though a few such methods like artificial neural network (ANN) based models are available in the literature; their performance in the context is limited to certain extent and they have their own merits and demerits. Hence, to address some of the limitations of ANN (non-higher order Neural Network) based models, this contribution proposed a hybrid model in which a flat network i.e., a type of higher order neural network- functional link artificial neural network (FLANN) is used as the base structure and genetic algorithm (GA) is employed to find out the optimal FLANN parameters (i.e., GA + FLANN). The training process comprises selection of connection weights, bias, as well as optimal number of basis functions of FLANN by GA rather fixing them earlier. Thus, an optimal FLANN is crafted on fly from exploitation of training data. The proposed model is used to assess the CS of concrete cements from datasets available in the literature considering samples of curing age at 3, 7, 14, 28, 56, and 91 days. A rolling window is used for input selection and five evaluation metrics are used for performance evaluation. On an average, the GA + FLANN obtained 0.477052 MAPE (mean absolute percentage of error), 0.598067 ARV (average relative variance), 0.593862 UT (U of Theil's statistic), 0.551452 NMSE (normalized mean squared error), and 0.206135 SD (standard deviation) values which are the lowest compared to others. The superiority of the model is established through comparative studies and statistical significance tests. [ABSTRACT FROM AUTHOR]

While much research has focused on the deleterious effects of stress on goal-directed behavior in recent decades, current views increasingly discuss growth under stress, often assuming dose-dependent effects of stress in a curvilinear association. This is based on the concept of hormesis, which postulates a strengthening effect of stress at low-to-moderate doses. Leveraging this approach, hormetic curves indicate under which stress dose an individual is able to maintain or even increase goal-directed behavior. The present study aimed to test the hormetic effect of low-to-moderate stress on tactical movement performance in the context of police operational scenarios in virtual reality. In teams of three to four, 37 riot police officers had to search a building for a potentially aggressive perpetrator in three scenarios with escalating stress potential (i.e., increasing weapon violence and number of civilians). Tactical movement performance as behavioral response was quantified by the sample entropy of each officer's velocity derived from positional data. To account for inter-individuality in response to the scenarios, we assessed self-reported stress, anxiety, mental effort, and vagally mediated heart rate variability. Specifically, we tested the quadratic associations between tactical movement performance and stress parameters, respectively. Random-intercept-random-slope regressions revealed neither significant linear nor quadratic associations between any of the stress parameters and performance. While we did not find evidence for hormesis in the present study, it stimulates theoretical discussions about the definition of "baseline" functioning and how the understanding of hormesis can move from psychological to behavioral adaptations to stressors. • Current research suggests that small doses of stress may enhance performance. • The concept of small-dose stimulation and high-dose inhibition is called hormesis. • Virtual reality allows to systematically vary stress doses during police training. • Our study found no evidence for hormesis for tactical movement performance. [ABSTRACT FROM AUTHOR]

The recent availability of satellite hyperspectral imaging combined with the developments in the classification techniques have paved the way towards improving our ability to obtain information on the spatiotemporal distribution of land use/land cover (LULC) at improved accuracy. In this context, the present study aims at evaluating the combined use of the recently launched Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite mission with two powerful machine learning (ML) classifiers. In particular, the Support Vector Machines (SVM) and Random Forest (RF) are used synergistically with EnMAP imagery in performing LULC mapping at a typical Mediterranean setting located in northern Greece. Evaluation of the derived LULC maps is based on the computation of a series classification accuracy metrics. The McNemar's chi-square statistical significance testing was also computed to confirm the statistical significance of the differences between the classifiers. In overall, results showed that SVM slightly outperformed RF, exhibiting a higher overall accuracy, of 92.6% and 88.1%, respectively, whereas the statistical significance of the findings was also attested by the McNemar's statistical test results. To our knowledge, this study is one of the first published so far focusing on exploring the capabilities of ENMAP imagery when combined with different ML pixel-based classifiers in the context of LULC mapping. Our results, indeed provided useful insights on the potential of EnMAP datasets in deriving information on the spatiotemporal distribution of LULC at a highly fragmented Mediterranean landscape, evidencing the EnMAP promising potential in this field. • First study exploring EnMAP satellite for LULC mapping in a Mediterranean setting. • Assessing for the first time ML algorithms combined with EnMAP in the context of LULC mapping. • A methodological scheme based solely on open-source software with user-friendly interface to exploit EnMAP datasets. [ABSTRACT FROM AUTHOR]

Automatic music recommendation systems based on human emotions are becoming popular nowadays. Since audio and lyrics can provide a rich set of information regarding a song, a fusion model including both modalities can enhance classification accuracy and is attempted in this paper. The main objective of the paper is to address music mood classification using various attention mechanisms, namely, self-attention (SA), channel attention (CA), and hierarchical attention network (HAN), on a multi-modal music mood classification system. Experimental results show that multi-modal architectures with attention have achieved higher accuracy than multi-modal architectures without attention and uni-modal architectures. Motivated by the performance of attention mechanisms, a new network architecture, HAN-CA-SA based multi-modal classification system, is proposed, which reported an accuracy of 82.35%. ROC and Kappa are also computed to see the efficacy of the proposed model. The proposed model is also evaluated using the K-fold cross-validation technique. The performance of the proposed model is compared with that of XLNet and CNN-BERT systems. In addition, McNemar's statistical hypothesis test is conducted to reaffirm the importance of the proposed approach. [ABSTRACT FROM AUTHOR]

We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This program logic is sound and relatively complete with respect to a Kripke model for hypothesis tests. We demonstrate by examples that BHL is useful for reasoning about practical issues in hypothesis testing. In our framework, we clarify the importance of prior beliefs in acquiring statistical beliefs through hypothesis testing, and discuss the whole picture of the justification of statistical inference inside and outside the program logic. [ABSTRACT FROM AUTHOR]

• We created a reservoir water bloom risk index (RWBRI) to assess water bloom risk. • TSI (trophic state index) or FAI (floating algae index) alone can't reliably assess water bloom risk. • The average value of water bloom risk for all three reservoirs is less than 50% • Reservoirs must still remain vigilant against water bloom outbreaks. The occurrence of water blooms in reservoirs poses a serious threat to water safety, so the assessment of water bloom risk is crucial. In this study, we constructed a reservoir water bloom risk index (RWBRI) using the trophic state index (TSI), floating algae index (FAI), temperature, precipitation, and wind speed datasets, and estimated and validated the water bloom risk levels of three large-scale eutrophication reservoirs in Fujian Province, China, from 2019 to 2023 based on the Sentinel-2 MSI dataset at the pixel scale. The results showed that there is no positive correlation between the TSI and FAI parameters of the three reservoirs (the significance test coefficients for Shanzai Reservoir, Dongzhen Reservoir, and Shanmei Reservoir were −0.455**, −0.371**, and −0.049, respectively, and ** indicates that the reservoir passed the statistical significance test at the 0.01 significance level). The TSI of Shanmei Reservoir was less fluctuating and more stable than that of Shanzai Reservoir and Dongzhen Reservoir, but the average value of FAI was higher. The average value of water bloom risk in Shanzai Reservoir and Dongzhen Reservoirs is higher than Shanmei Reservoir (0.47, 0.46, and 0.41, respectively), but the trend of annual average changes indicates that the overall risk of Dongzhen Reservoir is decreasing, while the risk of Shanzai Reservoir and Shanmei Reservoir is increasing. Due to the influence of weather, the water bloom risk in spring and summer is higher than that in fall and winter in the three reservoirs, and the highest risk value usually occurs in summer. The high values in Shanzai Reservoir were mainly distributed in the two inlet channels in the northern and southwestern parts of the reservoir, the bank risk was higher than in the other areas of Dongzhen Reservoir, and the high value of the risk in Shanmei Reservoir was mainly distributed in the center of the reservoir. The RWBRI method can be used for water bloom risk identification in reservoirs with eutrophication trends by comparing it with four-hourly and monthly water quality level data. [ABSTRACT FROM AUTHOR]

Background: In 2016, the American Statistical Association stated that the use of statistical significance leads to distortion of the scientific process. The principal alternative to significance or null hypothesis testing (NHT) is estimation with point estimates and confidence intervals (CIs).Objectives: The aim of this study was to determine the time trend of statistical inference and statistical reporting style in abstracts in major cardiovascular journals.Methods: A total of 84,250 abstracts published from 1975 to 2019 in 9 high-ranking cardiovascular journals (Circulation, Circulation Research, European Heart Journal, European Heart Journal: Cardiovascular Imaging, European Journal of Heart Failure, Journal of the American College of Cardiology, JACC: Cardiovascular Imaging, JACC: Cardiovascular Interventions, and JAMA Cardiology) were reviewed; in particular, proportions of abstracts containing statistical inference and its major variants (NHT, significance testing) were compared over time and among journals.Results: Overall, 49,924 abstracts (59%) contained statistical inference. Among these abstracts, NHT was the most frequent reporting style of statistical inference (79% among all journals). Journals differed considerably in the prevalence of CI reporting (1% to 78% in 2017-2019). With the exception of 2 journals, the proportion of abstracts containing CIs was higher in the more recent period. From 2013-2015 to 2017-2019, the proportion of abstracts containing only CIs increased by 5 (95% CI: 0 to 10), 18 (95% CI: 15 to 21), and 9 (95% CI: 3 to 15) percentage points in the European Heart Journal, the Journal of the American College of Cardiology, and JACC: Cardiovascular Imaging, respectively.Conclusions: NHT is still the prevailing reporting style of statistical inference in major cardiovascular journals. Reporting of CIs in abstracts of major cardiovascular journals appears to be growing more popular. [ABSTRACT FROM AUTHOR]

In the 1970s a novel branch of statistics emerged focusing its effort on the selection of a function for the pattern recognition problem that would fulfill a relationship between the quality of the approximation and its complexity. This theory is mainly devoted to problems of estimating dependencies in the case of limited sample sizes, and comprise all the empirical out-of sample generalization approaches; e.g. cross validation (CV). In this paper a data-driven approach based on concentration inequalities is designed for testing competing hypothesis or comparing different models. In this sense we derive a Statistical Agnostic (non-parametric) Mapping (SAM) for neuroimages at voxel or regional levels which is able to: (i) relieve the problem of instability with limited sample sizes when estimating the actual risk via CV; and (ii) provide an alternative way of Family-wise-error (FWE) corrected p -value maps in inferential statistics for hypothesis testing. Using several neuroimaging datasets (containing large and small effects) and random task group analyses to compute empirical familywise error rates, this novel framework resulted in a model validation method for small samples over dimension ratios, and a less-conservative procedure than FWE p -value correction to determine the significance maps from the inferences made using small upper bounds of the actual risk. [ABSTRACT FROM AUTHOR]

Developed a detailed finite element model of spine and validated with the experimental or cadaveric tests to gain insight on occupant safety. This study evaluates the influence of occupant collision state parameters such as height of the drop, occupant seating posture (occupant posture angle) and mass of the upper body on the risk of lumbar spinal injury during a frontal crash. This parametric evaluation utilizing response surface methodology (RSM) performed. ANOVA was used to test the significance of parameters. Higher axial force of 3547 N is observed with higher dropping distance of 1500 mm. Similarly, higher strain and energy absorption were observed for the same dropping condition respectively. The result shows that all the factors considered in the experiment contribute to the risk of spinal lumbar injury during the frontal crash. Among all, height of the drop and the occupant posture angle are the most significant parameters in determining the lumbar spinal injury of occupant. It is observed that the injury criteria are directly proportional to the posture angle of the seat and height of drop. [ABSTRACT FROM AUTHOR]

• Only 50% of the published trials were registered. • Unregistered trials may have collected other measures that remain unreported. • Many additional outcomes not appearing in trial registries were published. • Some reported positive findings are likely spurious due to unadjusted multiple comparisons. Sedentary behaviour research is a relatively new field, much of which has emerged since the widespread acceptance of clinical trial registration. The aim of this study was to investigate the trial registration and related issues in studies investigating the effect of frequent activity interruptions to prolonged sitting-time. Secondary analysis of a scoping review including systematic searches of databases and trial registries. We included experimental studies investigating the effects of frequent activity interruptions to prolonged sitting-time. We identified 32 trials published in 45 papers. Only 16 (50%) trials were registered, with all 16 trials being completed and published. Of the unregistered trials, we identified three (19%) for which similarities in the sample size and participant demographics across papers was suggestive of duplicate publication. Identification of potential duplicate publications was difficult for the remaining 13 (81%). Results from 53 (76%) of the 70 registered outcomes were published, but 11 (69%) registered trials reported results from additional outcomes not prospectively registered. A total of 46 different outcomes (out of 53 reported outcome measures, similar measures were collated) were reported across all trials, 31 (67%) of which were collected in ≤2 trials. We found direct evidence of trial registration issues in experimental trials of breaking up sitting-time. The lack of prospective registration of all trials, and the large number of outcomes measured per trial are key considerations for future research in this field. These issues are unlikely to be confined to the field of sedentary behaviour research. [ABSTRACT FROM AUTHOR]

Cryptococcal meningitis (CM) remains an important cause of morbidity and mortality among immunocompromised patients. Laboratory diagnostics for CM includes antigen detection, staining and culture. Data on the performance of the Biofire® FilmArray® meningitis/encephalitis (ME) panel for detecting Cryptococcus neoformans/gattii is limited, with several reports describing false negativity for this target. A retrospective analysis of 1384 physician-ordered ME panel tests ordered between January 2017 to October 2018 was performed. ME panel results were compared to cerebrospinal fluid (CSF) cryptococcal antigen (CrAg) and CSF culture testing and clinical significance of cryptococcal detection was determined. There were 34 patients positive for cryptococcal detection by either ME panel, CSF CrAg or CSF culture in 2.7% of CSF specimens tested (38/1384). Of the 34 patients positive for cryptococcal detection, 85.3% were human immunodeficiency virus positive (29/34). The ME panel detected 32/38 (84.2%) cryptococcal-positive specimens, culture detected 28/38 (73.7%) and CSF CrAg was positive in 37/38 specimens (97.4%). The ME panel had a sensitivity and specificity of 96.4% (95% CI 81.7–99.9%) and 99.6% (95% CI 99.2–99.9%) compared with culture, and 83.8% (95% CI 68.0–93.8%) and 99.9% (95% CI 99.6–100.0%) compared to CSF CrAg testing, respectively. CrAg titres were lower among ME panel-negative, culture-negative specimens compared with ME panel-positive, culture-negative specimens (reciprocal median end-point titres of 128 ± 60 vs. 1920 ± 1730, p 0.04). All five CrAg-positive, ME panel- and culture-negative specimens were obtained from previously treated CM patients. The ME panel had high correlation with CSF culture and a somewhat lower correlation with CSF CrAg testing. The potential utility of using negative ME panel test results to predict culture sterility among patients undergoing treatment for CM warrants further study. [ABSTRACT FROM AUTHOR]

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, Elephant search (ESA) based optimization is proposed to select best gene expressions from the large volume of microarray data. Firefly search (FFS) is also used to understand the effectiveness of the Elephant search method in feature selection process. Stochastic gradient descent based Deep Neural Network as Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on ten most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning approach are compared with the most recent published article for its suitability in future Bioinformatics research. Finally, Statistical significance test by one-way ANOVA with post hoc Tukey's test is conducted to deduce a number of insights on the selection of the best classification model. [ABSTRACT FROM AUTHOR]

Dietary interventions often target foods that are underconsumed relative to dietary guidelines, such as vegetables, fruits, and whole grains. Because these foods are only consumed episodically for some participants, data from such a study often contains a disproportionally large number of zeros due to study participants who do not consume any of the target foods on the days that dietary intake is assessed, thus generating semicontinuous data. These zeros need to be properly accounted for when calculating sample sizes to ensure that the study is adequately powered to detect a meaningful intervention effect size. Nonetheless, this issue has not been well addressed in the literature. Instead, methods that are common for continuous outcomes are typically used to compute the sample sizes, resulting in a substantially under- or overpowered study. We propose proper approaches to calculating the sample size needed for dietary intervention studies that target episodically consumed foods. Sample size formulae are derived for detecting the mean difference in the amount of intake of an episodically consumed food between an intervention and a control group. Numerical studies are conducted to investigate the accuracy of the sample size formulae as compared with the ad hoc methods. The simulation results show that the proposed formulae are appropriate for estimating the sample sizes needed to achieve the desired power for the study. The proposed method for sample size is recommended for designing dietary intervention studies targeting episodically consumed foods. [ABSTRACT FROM AUTHOR]