Abstract Background Clinical prediction models (CPMs) are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo. Methods Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new â localâ population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression. Results While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance. Conclusion This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling strategy particularly with sparse data on the local population.

Data from 27 randomised controlled trials included in this systematic review was independently extracted by two review authors and entered into a pre-piloted data extraction form. If information was missing, study authors were contacted with up to four follow-up Emails.We calculated standardised effect sizes for intermediate outcomes and distal outcomes. Standardised effect sizes are scale-free and provide comparable information about the magnitude and direction of each effect and can thus be aggregated across studies. For continuous outcome measures, standardised mean differences (SMDs) were calculated. To adjust for potential bias from small sample sizes, we used Hedges’ g correction for all effect sizes. For outcomes that were measured on a continuous scale in some studies and dichotomised in other studies (e.g. increases in saving amounts), we transformed odds ratios into SMDs and used Hedges’ g correction as described above. Intermediate outcomes included (a) increases in total savings, (b) financial literacy, and (c) savings attitudes, (d) investments in profitable business. It is crucial to account for potential crowd-out effects that can arise from the shifting of resources to the saving device endorsed by the interventions. We have therefore made efforts to focus on total household savings and otherwise sought to aggregate all information on savings held in different places to reach an average effect. Distal outcomes included (a) business profits, (b) food security, (c) investments in and status of health, (d) investments in education and educational attainment, and (e) household poverty measured through assets or expenditure/income.

Data set presented in publication 'Assessment of aggregate mixture reactivity in concrete at 60°C' Structure and Environment 2024, vol. 16, (3), pp. 153-157'Data_set_expansion': Length changes measurements (expansion) of concrete beams in Miniature Concrete Prism Test (AASHTO T 380). Tests were performed on 6 aggregate mixtures (SB, SW, ST, AB, AX, AW) stored in molar sodium hydroxide solution at 60°C for 84 days. Measurements were performed on dilatometer design in IPPT PAN (patent WUP 08/2022). Measurements were made on 3 samples, the length change was calculated in acc. to AASHTO T 380. The expansion results (mean value of the length change) are presented in Fig. 1 in the above publication.‘Data_set_compressive_strength': Compressive strength measurements on 50 mm cubic samples cut from the concrete beams. The samples stored in 1 molar sodium hydroxide solution (1 M NaOH) at 60°C and water at 20°C for 84 days. Compressive strength was performed on at least 3 samples (with load rate 2400 N/s) using concrete compression machine Controls AUTOMAX MULTITEST.‘Data_set_modulus’: The resonant modulus of elasticity of the concrete was measured with the impulse excitation technique using a GrindoSonic MK5 device with a piezoelectric detector on the concrete beams stored in 1 molar sodium hydroxide solution (1 M NaOH) at 60°C and water at 20°C for 84 days. Presented data contains mean values for a concrete beam calculated in software WINEMOD - Version 2.05.

Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method for the aggregation problem. This method aims to minimise changes of classes and to create compact shapes, subject to hard constraints ensuring aggregates of sufficient size for the target scale. To quantify class changes we apply a semantic distance measure. We give a graph theoretical problem formulation and prove that the problem is NP-hard, meaning that we cannot hope to find an efficient algorithm. Instead, we present a solution by mixed-integer programming that can be used to optimally solve small instances with existing optimisation software. In order to process large datasets, we introduce specialised heuristics that allow certain variables to be eliminated in advance and a problem instance to be decomposed into independent sub-instances. We tested our method for a dataset of the official German topographic database ATKIS with input scale 1:50,000 and output scale 1:250,000. For small instances, we compare results of this approach with optimal solutions that were obtained without heuristics. We compare results for large instances with those of an existing iterative algorithm and an alternative optimisation approach by simulated annealing. These tests allow us to conclude that, with the defined heuristics, our optimisation method yields high-quality results for large datasets in modest time.

This Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a data input file to correctly organise your MEC data, a calculator file to analyse your data, and instructional videos. The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview, (2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).

Soil stoichiometric characteristics and aggregate stability are affected by vegetation restoration in degraded land. Yet, it is not known that how the aggregate stability is related to soil stoichiometric characteristics under different vegetation restorations. A 5-year in situ experiment was conducted to investigate the effects of vegetation restoration on the composition, stability and stoichiometric properties of soil aggregates. In the northwest part of Beijing, Bromus inermis Leyss. and Medicago sativa L. were planted in a typical area of desertification, and natural restoration was used as control. Boosted regression trees (BRTs) were applied to partition the factors that control aggregate stability. The results showed that the mean weight diameter (MWD) of soil water-stable aggregates under natural restoration (CK) and Medicago sativa L. sowing treatments (AF) was significantly higher than that under the restoration of the Bromus inermis Leyss. sowing treatments (SB). Compared with CK, AF significantly increased the geometric mean diameter (GMD) water stable aggregates, while SB showed the opposite result. AF significantly increased the proportion of soil aggregates >2 mm compared with CK. AF could improve the stability of soil aggregates by increasing the proportion of large aggregates. For the stoichiometric characteristics of the aggregates, AF increased significantly the value of C/P in 0.053−0.125 mm particle size aggregates in all soil layers. The MBC/MBN ratio aggregates at depths of 0–10 cm and 10–20 cm was also significantly increased in the treatment of AF. The BRTs indicated that stoichiometric ratio is the main factor driving the stability of soil aggregates rather than microbial community characteristics and soil nutrients. The C/P is the main driving factor affecting the MWD, in which the overall stoichiometric influence accounts for 46%, followed by the microbial influence of 36%. For the GMD, MBC/MBN is the main driving factor, and the stoichiometric influence accounts for 94%. Our findings indicate that AF is beneficial to the stability of deep soil aggregates, and their stoichiometric characteristics of soil are the key factors affecting the stability of soil aggregates.

Aggregate statistics (mean, (standard deviation) [minimum, maximum]) of daily entropy rate and limit of probability of different types of segments over 10 houses.

Weekly aggregate data on primary and secondary outcomes at the level of each care home.

ABSTRACT The aggregates shape properties (form, angularity, and surface texture) have an influence on the asphalt pavement surface texture, which is directly related to tire-pavement adhesion, to stability and to road safety. Due to the importance of studying these characteristics, this research has the main objective of evaluating the effect of aggregate degradation on their shape properties. In order to achieve this, the shape properties of aggregates with three different sizes were analyzed by means of traditional methods and also with the use of aDigital Image Processing (DIP) technique. These properties were analyzed with the use of the Aggregate Image Measurement System 2 (AIMS2), before and after the use of the Micro Deval (MD) and the Los Angeles abrasion equipment, besides the conventional test of uncompacted void content. The aggregates particles angularity values decreased during the degradation process for both equipment used in the analyses. The aggregates sphericity after the use of MD did not change significantly. This could be explained because smaller abrasive charges are used in this particular test. The surface texture evaluated after the Los Angeles abrasion test did not vary significantly, which can be explained due to the tendency of breakage during the test, a process that is more related changes in form conditions. Furthermore, the results showed that the aggregate size and the presence of water influenced the degradation processes of the aggregates. The combination of the characterization of aggregate shape properties by means of image techniques with the use of laboratorial mechanical processes that are capable of modifying these properties might be an adequate solution for the prediction of asphalt pavements performance in relation to adhesion loss throughout the time.

This map service is a synthesis of the baseline characterization of kelp and shallow rock ecosystems inside and outside of several North Central Coast (NCC) MPAs at the time of their implementation. MPAs in the NCC study region (NCCSR) were implemented on May 1, 2010. Baseline characterizations were conducted by the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) between August and October of 2010 and 2011. Visual SCUBA surveys took place at sites within MPAs and at their associated reference sites (sites outside MPA) to establish quantitative baselines for measuring future MPA effects (i.e., changes in community structure due to MPA implementation). This particular map service focuses on PISCO's characterization of fish communities aimed at estimating fish densities and fish size distribution. Refer to the following link for specifics regarding PISCO's “fish survey design” and “fish sampling methodology”: http://www.piscoweb.org/research/science-by-discipline/ecosystem-monitoring/kelp-forest-monitoring/subtidal-sampling-protoco#FishSurvey.Surveys for baseline characterization of kelp forest communities focused on the following MPAs: Point Arena SMR; Sea Lion Cove SMCA; Saunders Reef SMCA; Del Mar SMR; Stewarts Point SMR/SMCA; and Salt Point SMCA. From Point Arena to Salt Point, 35 cells (fundamental sampling units) were sampled via fish transects (inside and outside of MPAs) using stratified sampling across shore and at various depths in the kelp forests (5m, 10m, 15m and 20m). Utilizing PISCO's GPS coordinates of the 35 study cells (points), and details from PISCO's methods (see link above), we created estimated footprints of the areas in which these transects were surveyed (the 35 fundamental sampling units). From there, we also estimated the aggregated site polygons (aggregate sampling units) that comprise either an MPA or an MPA reference site; this resulted in 12 new polygons that were representative of the kelp and shallow rock ecosystems surveyed by PISCO. Stewarts Point SMR and SMCA are combined in the survey summaries to make up one of the ‘aggregate sampling units’ and therefore those feature classes have 11 instead of 12 features.These estimated study areas (fundamental and aggregate sampling units) were then used to synthesize and represent PISCO's fish survey results into the following map service product. The map service is a conglomeration of fish density and fish length metrics for the regions most abundant rockfish and non-rockfish species. The original PISCO data set used to comprise this map service can be found and downloaded at the following Ocean Spaces link: http://oceanspaces.org/data/north-central-coast-pisco-surveys-fish-shallow-rocky-reefs-and-kelp-forest-habitats-2010-2011.The complete ESRI ArcMap project and source summary data can be downloaded at the following California Department of Fish and Wildlife link: ftp://ftp.wildlife.ca.gov/R7_MR/BIOLOGICAL/NCCSR_MPAbaseline_PISCOsubtidal_2010-2011.zip

The study of cell aggregation in vitro has a tremendous importance these days. In cancer biology, aggregates and spheroids serve as model systems and are considered as pseudo-tumors that are more realistic than 2D cell cultures. Recently, in the context of brain tumors (gliomas), we developed a new poly(ethylene glycol) (PEG)-based hydrogel, with adhesive properties that can be controlled by the addition of poly(L-lysine) (PLL), and a stiffness close to the brain’s. This substrate allows the motion of individual cells and the formation of cell aggregates (within one day), and we showed that on a non-adhesive substrate (PEG without PLL is inert for cells), the aggregates are bigger and less numerous than on an adhesive substrate (with PLL). In this article, we present new experimental results on the follow-up of the formation of aggregates on our hydrogels, from the early stages (individual cells) to the late stages (aggregate compaction), in order to compare, for two cell lines (F98 and U87), the aggregation process on the adhesive and non-adhesive substrates. We first show that a spaceless model of perikinetic aggregation can reproduce the experimental evolution of the number of aggregates, but not of the mean area of the aggregates. We thus develop a minimal off-lattice agent-based model, with a few simple rules reproducing the main processes that are at stack during aggregation. Our spatial model can reproduce very well the experimental temporal evolution of both the number of aggregates and their mean area, on adhesive and non-adhesive soft gels and for the two different cell lines. From the fit of the experimental data, we were able to infer the quantitative values of the speed of motion of each cell line, its rate of proliferation in aggregates and its ability to organize in 3D. We also found qualitative differences between the two cell lines regarding the ability of aggregates to compact. These parameters could be inferred for any cell line, and correlated with clinical properties such as aggressiveness and invasiveness.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete’s crack resistance is represented by the CT index. 107 CT Index data samples from the University of Transport Technology’s lab are measured. These data samples are used to establish a database from which a Machine Learning (ML) model for predicting the CT Index of asphalt concrete can be built. For creating the highest performing machine learning model, three well-known machine learning methods are introduced: Random Forest (RF), K-Nearest Neighbors (KNN), and Multivariable Adaptive Regression Spines (MARS). Monte Carlo simulation is used to verify the accuracy of the ML model, which includes the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The RF model is the most effective ML model, according to analysis and evaluation of performance indicators. By SHAPley Additive exPlanations based on RF model, the input Aggregate content passing 4.75 mm sieve (AP4.75) has a significant effect on the variation of CT Index value. In following, the descending order is Reclaimed Asphalt Pavement content (RAP) > Bitumen content (BC) > Flash point (FP) > Softening point > Rejuvenator content (RC) > Aggregate content passing 0.075mm sieve (AP0.075) > Penetration at 25°C (P). The results study contributes to selecting a suitable AI approach to quickly and accurately determine the CT Index of asphalt concrete mixtures.

Results of Combination Experiments at Various Detection Scales.

Summary of the statistical measures for the training and testing datasets.

Summary of primary and secondary outcomes in control and intervention arms.

Comparisons of means and relative frequencies across outcome variables in the matched groups.

Detecting Trichosanthes Kirilowii Maxim (Cucurbitaceae) in complex mountain environments is critical for developing automated harvesting systems. However, the environmental characteristics of brightness variation, inter-plant occlusion, and motion-induced blurring during harvesting operations, detection algorithms face excessive parameters and high computational intensity. Accordingly, this study proposes a lightweight T.Kirilowii detection algorithm for complex mountainous environments based on YOLOv7-tiny, named KPD-YOLOv7-GD. Firstly, improve the multi-scale feature layer and reduce the complexity of the model. Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. The experimental results showed that the mean average precision of the improved network KPD-YOLOv7-GD reached 93.2%. Benchmarked against mainstream single-stage algorithms (YOLOv3-tiny, YOLOv5s, YOLOv6s, YOLOv7-tiny, and YOLOv8), KPD-YOLOv7-GD demonstrated mean average precision improvements of 4.8%, 0.6%, 3.0%, 0.6%, and 0.2% with corresponding model compression rates of 81.6%, 68.8%, 88.9%, 63.4%, and 27.4%, respectively. Compared with similar studies, KPD-YOLOv7-GD exhibits lower complexity and higher recognition speed accuracy, making it more suitable for resource-constrained T.kirilowii harvesting robots.

Best selection variables for mRS scores and the corresponding mean errors and standard deviations (the maximum volume is 250.99 cm3).