The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.

The impetus for this study was a review of the Society for American Archaeology (SAA) 86th Annual Meeting program in 2021. Finding that no single poster or presentation referenced looting or antiquities trafficking despite these issues being ethical considerations that all SAA members are expected to recognize, we sought to investigate whether this was an irregularity – perhaps due to the virtual format of the meeting – or whether it was more common than not. For a broader understanding of if, how, and where these topics are discussed by archaeologists outside of the SAA, we expanded the investigation and studied the archives of 14 other archaeological and anthropological conferences. The results of the study show that despite there being an overall increase in mentioning looting and antiquities trafficking at conferences, it remains a niche and infrequently discussed topic.1

In the present study, a comparison of the thermal-insulation and mechanical performances of cement and heat-stabilized compressed earthen blocks (CEBs) was carried out to determine the factors which influence those properties. The raw clays used consist mainly of kaolinite, orthoclase and quartz. The mechanical strength increased with increase in both the amount of cement added and the firing temperature. However, the responses are better for cement-stabilized CEBs. The thermal insulation of fired bricks is greater than that of cement-stabilized bricks. This difference was related to the decrease in porosity and the formation of continuous-surface. The decrease in thermal insulation is mainly related to the formation of continuous-surface in cement-stabilized CEBs, whereas in the fired CEBs, it is due to the modification of pore volume. The mineralogy of the raw clays is statistically correlated to porosity and continuous-surface development that were confirmed as the main factors in the modification of both the mechanical strength and the thermal insulation. In cement-stabilization, the decrease in insulation is due to the development of continuous surface, while for heat-stabilization, mineral transformations during the sintering reduced continuous-surface formation and the insulation was controlled by both radiation and reduced surface conduction. The influence of the mineralogy of the raw material shows that clay content favours the insulation in fired bricks obtained at T ≤ 1000°C, while sand contents favour densification. In contrast, clay contents reduce the mechanical response of cement-stabilized material due to limited cement–clay interactions. In general, the mechanical response is more favourable in cement stabilization, while thermal insulation is better in fired bricks.

Statistical analyses of chemical data from the literature of corrensite minerals suggest a large compositional variability, more evident in octahedral than in tetrahedral coordination. Mg occupies 40–80% of the octahedral sites, with Al and Fe2+ making up the remainder. Approximately 15–30% of the tetrahedral sites are filled by Al. Despite this compositional variability, distinct fields for the several types of mixed-layer trioctahedral chlorite/trioctahedral swelling layer are not apparent. Statistical analyses of the composition of corrensite compared with saponite, vermiculite, and chlorite suggest that corrensite is an intermediate between trioctahedral chlorite and trioctahedral smectite. If Fe/(Fe + Mg) > 50%, chlorite alone is favored, but with increasing Mg, chlorite appears to transform into corrensite and then, by iron oxidation, into trioctahedral smectite. Despite the chemical variability between corrensite, chlorite, and saponite, corrensite appears chemically to be a well-defined species. On the other hand, corrensite cannot be characterized chemically on the basis of its swelling component. Thus, the current definition of corrensite as a regular 1:1 interstratification of trioctahedral chlorite and either trioctahedral smectite or vermiculite is appropriate.

The Upper Cretaceous Gammon Shale has served as both source bed and reservoir rock for accumulations of natural gas. Gas-producing and nonproducing zones in the Gammon Shale are differentiated on the basis of geophysical log interpretation. To determine the physical basis of the log responses, mineralogical, cation-exchange, textural, and chemical analyses were conducted on core samples from both producing and nonproducing portions of a well in the Gammon Shale from southwestern North Dakota. Statistical treatment (2 sample t-test and discriminant function analysis) of the laboratory data indicate that the producing and nonproducing zones differ significantly in mixed-layer clay content (7 vs. 12%), weight proportion of the clay-size (0.5-1.0 μm) fraction (5.3 vs. 6.3%) ratio of Ca2+ to Na+ extracted during ion exchange (1.4 vs. 1.0), and abundance of dolomite (10 vs. 8%). The geophysical logs apparently record subtle differences in composition and texture which probably reflect variations in the original detrital constituents of the Gammon sediments. Successfully combining log interpretation and clay petrology aids in understanding the physical basis of log response in clay-rich rocks and enhances the effectiveness of logs as predictive geologic tools.

The effective reproduction number $ R $ was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the $ R $ value published on the UK Government Dashboard has been generated as a combined value from an ensemble of epidemiological models via a collaborative initiative between academia and government. In this paper, we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined $ R $ estimate can be generated from an ensemble of epidemiological models. We analyse the $ R $ values calculated for the period between April 2021 and December 2021, to show that this $ R $ is robust to different model weighting methods and ensemble sizes and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how $ R $ can be generated from different data sources and show that it is a good summary indicator of the current dynamics in an epidemic.

Compositional data for 464 clay minerals (2:1 type) were analyzed by statistical techniques. The objective was to understand the similarities and differences between the groups and subgroups and to evaluate statistically clay mineral classification in terms of chemical parameters. The statistical properties of the distributions of total layer charge (TLC), K, VIAl, VIMg, octahedral charge (OC) and tetrahedral charge (TC) were initially evaluated. Critical-difference (P = 1%) comparisons of individual characteristics show that all the clay micas (illite, glauconite and celadonite) differ significantly from all the smectites (montmorillonite, beidellite, nontronite and saponite) only in their TLC and K levels; they cannot be distinguished by their VIAl, VIMg, TC or OC values which reveal no significant differences between several minerals.

Linear discriminant analysis using equal prior was therefore performed to analyze the combined effect of all the chemical parameters. Using six parameters [TLC, K, VIAl, VIMg, TC and OC], eight minerals groups could be derived, corresponding to the three clay micas, four smectites (mentioned above) and vermiculite. The fit between predicted and experimental values was 88.1%. Discriminant analysis using two parameters (TLC and K) resulted in classification into three broad groups corresponding to the clay micas, smectites and vermiculites (87.7% fit). Further analysis using the remaining four parameters resulted in subgroup-level classification with an 85–95% fit between predicted and experimental results. The three analyses yielded D2 Mahalanobis distances, which quantify chemical similarities and differences between the broad groups, within members of a subgroup and also between the subgroups. Classification functions derived here can be used as an aid for classification of 2:1 minerals.

Typhoid fever is a major cause of illness and mortality in low- and middle-income settings. We investigated the association of typhoid fever and rainfall in Blantyre, Malawi, where multi-drug-resistant typhoid has been transmitting since 2011. Peak rainfall preceded the peak in typhoid fever by approximately 15 weeks [95% confidence interval (CI) 13.3, 17.7], indicating no direct biological link. A quasi-Poisson generalised linear modelling framework was used to explore the relationship between rainfall and typhoid incidence at biologically plausible lags of 1–4 weeks. We found a protective effect of rainfall anomalies on typhoid fever, at a two-week lag (P = 0.006), where a 10 mm lower-than-expected rainfall anomaly was associated with up to a 16% reduction in cases (95% CI 7.6, 26.5). Extreme flooding events may cleanse the environment of S. Typhi, while unusually low rainfall may reduce exposure from sewage overflow. These results add to evidence that rainfall anomalies may play a role in the transmission of enteric pathogens, and can help direct future water and sanitation intervention strategies for the control of typhoid fever.

The measurement and communication of the effect size of an independent variable on a dependent variable is critical to effective statistical analysis in the Social Sciences. We develop ideas about how to extend traditional methods of evaluating relationships in multivariate models to explain and illustrate the statistical power of a focal independent variable. Even with a growing acceptance of the need to report effect sizes, scholars in the management community have few well-established protocols or guidelines for reporting effect sizes. In this editorial essay, we: (1) review the necessity of reporting effect sizes; (2) discuss commonly used measures of effect size and accepted cut-offs for large, medium, and small effect sizes; (3) recommend standards for reporting effect sizes via verbal descriptions and graphical presentations; and (4) present best practice examples of reporting and discussing effect size. In summary, we provide guidance for authors on how to report and interpret effect sizes, advocating for rigor and completeness in statistical analysis.

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics. Here, we introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the statistical method used for keyword detection. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions. Using the text-mining method, we further identify codon sequences that are potential candidates for siRNA-based antiviral drugs. Significantly, one of the candidates identified in this work corresponds to the first seven codons of an epitope of the spike glycoprotein, which is the only SARS-CoV-2 immunogenic peptide without a match to a human protein.

As mechanical simulations play an increasingly role in engineering projects, an appropriate integration of simulations into design-oriented product development processes is essential for efficient collaboration. To identify and overcome barriers between design and simulation departments, the BRIDGES approach was elaborated for barrier reduction in design engineering and simulation. This paper shows the industrial evaluation of the approach using a multi-method study of an online survey and focus group workshops. The experts' assessments were statistically analysed to build a connection matrix of barriers and recommendations. 59 participants from multiple industries with practical experience in the field contributed to the online survey, while 24 experts could be acquired for the focus group workshops. As a result of the workshops, both the data-based and the workshop-based part of the BRIDGES approach were assessed as beneficial to raise the efficiency of collaboration and practically applicable. This provides an empirically secured connection of barriers and suitable recommendations, allowing companies to identify and overcome collaboration barriers between design and simulation.

This paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.

A method is presented to determine the feature resolution of physically relevant metrics of data obtained from segmented image sets. The presented method determines the best-fit distribution curve of a dataset by analyzing a truncated portion of the data. An effective resolvable size for the metric of interest is established when including parts of the truncated dataset results in exceeding a specified error tolerance. As such, this method allows for the determination of the feature resolution regardless of the processing parameters or imaging instrumentation. Additionally, the number of missing objects that exist below the resolution of the instrumentation may be estimated. The application of the developed method was demonstrated on data obtained via 2D scanning electron microscopy of a pressed explosive material and from 3D micro X-ray computed tomography of a polymer-bonded explosive material. It was shown that the minimum number of pixels/voxels required for the accurate determination of a physically relevant metric is dependent on the metric of interest. This proposed method, utilizing the prior knowledge of the distribution of metrics of interest, was found to be well suited to determine the feature resolution in applications where large datasets can be achieved.