Observed Differences In Performance Research Articles

Interspecific hybridisation provides a low-risk option for increasing genetic diversity of reef-building corals.

Interspecific hybridisation increases genetic diversity and has played a significant role in the evolution of corals in the genus Acropora. In vitro fertilisation can be used to increase the frequency of hybridisation among corals, potentially enhancing their ability to adapt to climate change. Here, we assessed the field performance of hybrids derived from the highly cross-fertile coral species Acropora sarmentosa and Acropora florida from the Great Barrier Reef. Following outplanting to an inshore reef environment, the 10-month survivorship of the hybrid offspring groups was intermediate between that of the purebred groups, although not all pairwise comparisons were statistically significant. The A. florida purebreds, which had the lowest survivorship, were significantly larger at 10 months post-deployment compared to the other three groups. The four offspring groups harboured the same intracellular photosymbiont communities (Symbiodiniaceae), indicating that observed performance differences were due to the coral host and not photosymbiont communities. The limited differences in the performance of the groups and the lack of outbreeding depression of the F1 hybrids in the field suggest that interspecific hybridisation may be a useful method to boost the genetic diversity, and as such increase the adaptive capacity, of coral stock for restoration of degraded and potentially genetically eroded populations.

Intuitionistic fuzzy twin proximal SVM with fuzzy hyperplane and its application in EEG signal classification

Twin support vector machine (TSVM) is a contemporary machine learning technique to tackle classification and regression problems. However, TSVM lacks the ability to differentiate between support vectors and noises since it neglects the positional information of input data samples, and hence it is sensitive to noises. Additionally, it fails to consider uncertainties associated with the data, which reduces its capacity for generalization. To address these drawbacks, we propose a novel fuzzy hyperplane based intuitionistic fuzzy twin proximal support vector machine. The first significant feature of the proposed approach is that it gives an intuitionistic fuzzy number based on the relevance to each data vector. This efficiently reduces the impact of noise and outliers by leveraging the local neighborhood information within the data points by incorporating membership and non-membership weights. Secondly, all the parameters present in the model are fuzzy variables, including the offset term and the elements of the normal vector. The suggested fuzzy hyperplane successfully reflects the inherent ambiguity prevalent in real-world categorization problems by reflecting vagueness in the input data through the use of fuzzy variables. The model’s efficiency is enhanced by solving two systems of linear equations to obtain two non-parallel classifiers rather than solving two quadratic programming problems as in standard TSVM. Utilizing non-linear kernel functions within the feature space enables the method to effectively identify complex patterns or non-linear relationships within the datasets. In order to demonstrate the effectiveness of the suggested approach, extensive computer experiments have been conducted on a set of eighteen benchmark datasets with both linear and non-linear kernels. In addition, rigorous statistical analysis, including Friedman and post-hoc Nemenyi tests, have been employed to assess the significance of the observed performance differences. Furthermore, we also performed numerical experiments utilizing linear, Gaussian, and polynomial kernels to classify electroencephalogram (EEG) signals. The outcomes of the experiment are analyzed in terms of average accuracy, processing time, and F-measure. The results demonstrate that the proposed method outperforms existing methods and achieves better generalization.

Effects of Aronia melanocarpa juice-powder on hindgut function and performance in post-weaned pigs

Twenty-four gilts and barrows (n = 8/trt) were divided into three treatment groups: control (CON), 0.5 % Aronia melanocarpa juice powder (AM; LoBerry), and 1.0 % AM juice powder (HiBerry). Pigs were fed for 14 d and tissues were collected. No differences in performance were observed. Ileum IL-18 tended to be lower in LoBerry pigs, while colonic IFN-γ was increased in pigs fed either AM diet. There were several changes in ileal gene expression in pigs fed the LoBerry diet compared to pigs fed the CON diet, including BMI1, CLDN2, JAM2, and MYLK, which are largely related to barrier and stem cell function. In the colon, CLDN2, REG3G, SI, and SLC6A19 were increased in pigs fed the LoBerry diet. There were no differences found in the colonic microbiome due to diets. In conclusion, feeding AM juice powder may have a positive impact in young pigs, but may require longer feeding time to observe performance differences.

Role of knowledge and reasoning processes as predictors of resident physicians’ susceptibility to anchoring bias in diagnostic reasoning: a randomised controlled experiment

BackgroundDiagnostic errors have been attributed to reasoning flaws caused by cognitive biases. While experiments have shown bias to cause errors, physicians of similar expertise differed in susceptibility to bias. Resisting...

Ultrasound breast images denoising using generative adversarial networks (GANs)

INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35–40 dB) and Structural Similarity Index (SSIM, 0.90–0.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms.

Understanding transfer learning and gradient-based meta-learning techniques

Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approach to improve the generalization of these networks from a limited amount of data. Whilst meta-learning techniques have been observed to be successful at this in various scenarios, recent results suggest that when evaluated on tasks from a different data distribution than the one used for training, a baseline that simply finetunes a pre-trained network may be more effective than more complicated meta-learning techniques such as MAML, which is one of the most popular meta-learning techniques. This is surprising as the learning behaviour of MAML mimics that of finetuning: both rely on re-using learned features. We investigate the observed performance differences between finetuning, MAML, and another meta-learning technique called Reptile, and show that MAML and Reptile specialize for fast adaptation in low-data regimes of similar data distribution as the one used for training. Our findings show that both the output layer and the noisy training conditions induced by data scarcity play important roles in facilitating this specialization for MAML. Lastly, we show that the pre-trained features as obtained by the finetuning baseline are more diverse and discriminative than those learned by MAML and Reptile. Due to this lack of diversity and distribution specialization, MAML and Reptile may fail to generalize to out-of-distribution tasks whereas finetuning can fall back on the diversity of the learned features.

Performance and plasma diagnostics of the Air-breathing Microwave Plasma CAThode (AMPCAT) coupled to a cylindrical Hall thruster

The Air-breathing Microwave Plasma CAThode (AMPCAT) has been developed for air-breathing electric propulsion in very-low Earth orbit. In this study, the standalone AMPCAT plasma characteristics are analyzed by means of several diagnostic tools and operation on xenon is compared to a conventional hollow cathode. A transition of AMPCAT extracted current from a lower (<0.1 A) to higher-current (>0.5 A) mode, triggered by increasing the negative cathode bias voltage, is accompanied by a significant rise in internal electron density and external electron temperature. The AMPCAT is coupled with a cylindrical Hall thruster in the 100–300 W power-level running on 0.5–0.7 mg/s of xenon, and the thrust is directly measured for cathode operation with both xenon and air. Stable thruster operation is demonstrated for the AMPCAT running on both propellants. For xenon, the performance is compared to a hollow cathode, which reveals matching discharge current profiles but a significantly higher thrust for the AMPCAT at low discharge voltages, approximately two times higher at 200 V. Langmuir probe measurements highlight a 30–40 V lower plasma potential in the cathode vicinity for the AMPCAT with xenon compared to both the hollow cathode and AMPCAT with air. This indicates a significantly improved coupling of cathode electrons to the thruster discharge, yielding an increased degree of ionization. Faraday probe and Wien filter results show that a larger current utilization efficiency drives the observed performance difference at low discharge voltages, rather than a significant change in ion acceleration or plume divergence.

Excipient and Packaging Material Impact on Glass and Polymer-Based Prefilled Syringe Functionality.

Compared to glass prefilled syringes (PFSs), cyclic olefin polymer (COP) PFS showed more consistent and predictable extrusion forces when exposed to a variety of excipient combinations (buffers, tonicity agents, and surfactants) at various accelerated storage conditions. Furthermore, COP PFSs also showed significantly less variance in extrusion forces within each individual stroke, which is critical for precision applications. Observed performance differences can be explained by fundamental differences in the stability and homogeneity of the primary packaging materials (i.e., COP vs siliconized glass) and their physicochemical interactions with excipients.

Growth of mesoporous carbon supported (bimetallic) phosphides derived from Prussian blue for supercapacitors

In the context of supercapacitor energy storage, maintaining high cycle stability while enhancing energy density and coulomb efficiency is crucial. In this investigation, NiCo Prussian blue analogue (PBA) electrode materials were synthesized using the coprecipitation method, and subsequently modified via sulfurization and phosphating to obtain oxides (PBAO), sulfides (PBAS) and phosphides (PBAP) derived from Prussian blue cubes. The microphysical structure of PBA was engineered to enhance conductivity and electrochemical performance, with an in-depth exploration of the effects of sulfurization and phosphating. Additionally, mesoporous carbon (SAC) was doped into the previously prepared PBA, leading to the successful preparation of SAC@PBA-P electrode material with exceptional electrochemical performance. By utilizing electrochemical testing and various characterization techniques, the underlying sources of the observed performance differences were analyzed both experimentally and through electrochemical mechanisms. Furthermore, the addition of 0.01 M potassium ferricyanide to the 3 M KOH electrolyte significantly improved the coulomb efficiency of the material under the condition of high current density and long cycle time. Finally, an asymmetric supercapacitor (ASC) was constructed using industrial activated carbon as the negative electrode with the prepared SAC@PBA-P electrode material, and its electrochemical performance was evaluated to explore the practical application potential and prospects of this material.

Identifying and Addressing Item Bias in the General Pediatrics Certifying Examination.

This article describes a 2-phase process implemented by the American Board of Pediatrics in 2021 to investigate and remove potential bias on its General Pediatrics Certifying Examination at the item (question) level based on gender or race and ethnicity. Phase 1 used a statistical technique known as differential item functioning (DIF) analysis to identify items in which 1 subgroup of the population outperformed another subgroup after controlling for overall knowledge level. Phase 2 involved a review of items flagged for statistical DIF by the American Board of Pediatrics' Bias and Sensitivity Review (BSR) panel, a diverse group of 12 voluntary subject matter experts tasked with identifying language or other characteristics of those items that may have contributed to the observed performance differences. Results indicated that no items on the 2021 examination were flagged for DIF by gender and 2.8% of the items were flagged for DIF by race and ethnicity. Of those items flagged for race and ethnicity, 14.3% (0.4% of total items administered) were judged by the BSR panel to contain biased language that may have undermined what the item was intending to measure and were therefore recommended to be removed from operational scoring. In addition to removing potentially biased items from the current pool of items, we hope that repeating the DIF/BSR process after each examination cycle will increase our understanding of how language nuances and other characteristics impact item performance so that we can improve our guidelines for developing future items.

Executive functioning in children with posterior cortex epilepsy compared to temporal and frontal lobe epilepsies

ObjectivePeople with epilepsy are at an increased risk of experiencing executive dysfunction, particularly those with frontal lobe epilepsy (FLE). The literature has also demonstrated alterations in executive functioning (EF) in patients with temporal lobe epilepsy (TLE). However, few studies have examined the neuropsychological profile of posterior cortex epilepsy (PCE), and little attention has been given to cognitive impairments in the pediatric population with PCE. This study aims to investigate EF performance in children with drug-resistant PCE compared to patients with FLE and TLE. MethodsWe analyzed neuropsychological data from 217 patients aged 6–18 years who underwent preoperative evaluation for epilepsy surgery. The EF of patients with PCE was compared to patients with FLE and TLE. ResultsThere was no significant difference in Full-Scale Intelligence Quotient (FSIQ) means between groups. However, we found a significant effect of brain region on the Coding task, in which patients with PCE and FLE performed worse than those with TLE (p = 0.034). We also observed performance differences between groups on the Stroop test (p = 0.005), with patients with PCE and FLE performing worse than the TLE group. SignificanceThese findings suggest that children with PCE have alterations in their EF that are similar to the deficits found in FLE compared to patients with TLE. This emphasizes the importance of understanding the neuroanatomy of executive functions and the model of neural networks extending beyond the prefrontal cortex.

Correlating the role of nanofillers with active layer properties and performance of thin-film nanocomposite membranes

Thin-film nanocomposite (TFN) membranes are emerging water-purification membranes that could provide enhanced water permeance with similar solute removal over traditional thin-film composite (TFC) membranes. However, the effects of nanofiller incorporation on active layer physico-chemical properties have not been comprehensively studied. Accordingly, we aimed to understand the correlation between nanofillers, active layer physico-chemical properties, and membrane performance by investigating whether observed performance differences between TFN and control TFC membranes correlated with observed differences in physico-chemical properties. The effects of nanofiller loading, surface area, and size on membrane performance, along with active layer physico-chemical properties, were characterized in TFN membranes incorporated with Linde Type A (LTA) zeolite and zeolitic imidazole framework-8 (ZIF-8). Results show that nanofiller incorporation up to ~0.15 wt% resulted in higher water permeance and unchanged salt rejection, above which salt rejection decreased 0.9–25.6 % and 26.1–48.3 % for LTA-TFN and ZIF-8-TFN membranes, respectively. Observed changes in active layer physico-chemical properties were generally unsubstantial and did not explain observed changes in TFN membrane performance. Therefore, increased water permeance in TFN membranes could be due to preferential water transport through porous structures of nanofillers or along polymer-nanofiller interfaces. These findings offer new insights into the development of high-performance TFN membranes for water/ion separations.

Uncertainty-aware mixed-variable machine learning for materials design

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

Bite Performance of Captive Alligator Snapping Turtles (Macrochelys temminckii) Improves after Reintroduction

Alligator Snapping Turtles (Macrochelys temminckii) possess unique head morphology that suggests strong natural selection for bite performance, which likely influences foraging and prey selection, as well as the outcomes of intrasexual aggressive encounters, mating, and defense against predators. Therefore, bite performance has the potential to directly and indirectly impact fitness. In this study, we assessed the effects of captivity on bite force by comparing the performance of captive and reintroduced M. temminckii. On average, free-ranging M. temminckii bite with greater force than do individuals residing in captivity, and captive individuals housed under seminatural conditions in outdoor ponds outperformed those housed indoors. Further, we found that free-ranging M. temminckii released into different river systems performed comparably and required less provocation than captives to display gaping and biting behavior. It remains to be determined whether the observed performance differences were more strongly influenced by physiological limitations on muscle performance or by behavioral variation in motivation to bite with maximum force.

Mitochondrial genome sequencing, mapping, and assembly benchmarking for Culicoides species (Diptera: Ceratopogonidae)

BackgroundMitochondrial genomes are the most sequenced genomes after bacterial and fungal genomic DNA. However, little information on mitogenomes is available for multiple metazoan taxa, such as Culicoides, a globally distributed, megadiverse genus containing 1,347 species.Aim Generating novel mitogenomic information from single Culicoides sonorensis and C. biguttatus specimens, comparing available mitogenome mapping and de novo assembly tools, and identifying the best performing strategy and tools for Culicoides species.ResultsWe present two novel and fully annotated mitochondrial haplotypes for two Culicoides species, C. sonorensis and C. biguttatus. We also annotated or re-annotated the only available reference mitogenome for C. sonorensis and C. arakawae. All species present a high similarity in mitogenome organization. The general gene arrangement for all Culicoides species was identical to the ancestral insect mitochondrial genome. Only short spacers were found in C. sonorensis (up to 30 bp), contrary to C. biguttatus (up to 114 bp). The mitochondrial genes ATP8, NAD2, NAD6, and LSU rRNA exhibited the highest nucleotide diversity and pairwise interspecific p genetic distance, suggesting that these genes might be suitable and complementary molecular barcodes for Culicoides identification in addition to the commonly utilized COI gene.We observed performance differences between the compared mitogenome generation strategies. The mapping strategy outperformed the de novo assembly strategy, but mapping results were partially biased in the absence of species-specific reference mitogenome. Among the utilized tools, BWA performed best for C. sonorensis while SPAdes, MEGAHIT, and MitoZ were among the best for C. biguttatus. The best-performing mitogenome annotator was MITOS2. Additionally, we were able to recover exogenous mitochondrial DNA from Bos taurus (biting midges host) from a C. biguttatus blood meal sample.ConclusionsTwo novel annotated mitogenome haplotypes for C. sonorensis and C. biguttatus using High-Throughput Sequencing are presented. Current results are useful as the baseline for mitogenome reconstruction of the remaining Culicoides species from single specimens to HTS and genome annotation. Mapping to a species-specific reference mitogenome generated better results for Culicoides mitochondrial genome reconstruction than de novo assembly, while de novo assembly resulted better in the absence of a closely related reference mitogenome. These results have direct implications for molecular-based identification of these vectors of human and zoonotic diseases, setting the basis for using the whole mitochondrial genome as a marker in Culicoides identification.

Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example

BackgroundThere are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example, this study adopted the missing data processing evaluation criteria more suitable for clinical decision making, aiming at systematically exploring the performance and applicability of single machine learning algorithms and ensemble learning (EL) under different data missing scenarios, as well as whether they had more advantages than traditional methods, so as to provide basis and reference for the selection of suitable missing data processing method in practical clinical decision making.MethodsThe whole process consisted of four main steps: (1) Based on the original complete data set, missing data was generated by simulation under different missing scenarios (missing mechanisms, missing proportions and ratios of missing proportions of each group). (2) Machine learning and traditional methods (eight methods in total) were applied to impute missing values. (3) The performances of imputation techniques were evaluated and compared by estimating the sensitivity, AUC and Kappa values of prediction models. (4) Statistical tests were used to evaluate whether the observed performance differences were statistically significant.ResultsThe performances of missing data processing methods were different to a certain extent in different missing scenarios. On the whole, machine learning had better imputation performance than traditional methods, especially in scenarios with high missing proportions. Compared with single machine learning algorithms, the performance of EL was more prominent, followed by neural networks. Meanwhile, EL was most suitable for missing imputation under MAR (the ratio of missing proportion 2:1) mechanism, and its average sensitivity, AUC and Kappa values reached 0.908, 0.924 and 0.596 respectively.ConclusionsIn clinical decision making, the characteristics of missing data should be actively explored before formulating missing data processing strategies. The outstanding imputation performance of machine learning methods, especially EL, shed light on the development of missing data processing technology, and provided methodological support for clinical decision making in presence of incomplete data.

Exploiting low-grade waste heat to produce electricity through supercapacitor containing carbon electrodes and ionic liquid electrolytes

Low-grade thermal energy harvesting presents great challenges to traditional thermoelectric systems based on the Seebeck effect, the thermogalvanic effect, and the Soret effect due to fixed temperature gradient and low voltage output. In this study, we report an ionic thermoelectric system, essentially a supercapacitor (SC) containing an ionic liquid (IL) electrolyte and activated carbon electrodes, which works on the thermocapacitive effect and does not require any fixed temperature gradient, rather it works in a homogeneously changing temperature. A systematic investigation is carried out on SCs containing two different ILs, 1-Ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl), EMIm TFSI, and 1-Ethyl-3-methylimidazolium acetate, EMIm OAc. A high voltage output of 176 mV is achieved for EMIm TFSI containing SC by exposing just to 60 °C environment. Moreover, a large voltage of 502 mV is recovered from the SC upon subjecting to heat after one electrical charge/discharge cycle. A system containing two SCs in series demonstrates a significant voltage of 947 mV. The observed performance difference between the two ILs is rationalized in terms of the extent of asymmetry in the interfaces of the electrical double layer that essentially originates from different diffusivity of individual ions. The mechanism can be applied to a plethora of ILs to exploit low-grade heat to store electricity without a fixed temperature gradient, opening up the possibility to merge different scientific communities and enrich this rising research field.

Assessing the agreement of ICESat-2 terrain and canopy height with airborne lidar over US ecozones

Despite its critical importance to carbon storage modeling, forest vertical structure remains poorly characterized over large areas. Canopy height estimates from current satellite missions such as ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) offer promise to close this knowledge gap, but their validation is critically important to inform their measurement uncertainties and scientific utility. Using existing airborne laser scanning (ALS) data, the agreement of a variety of terrain and aboveground canopy height metrics including summary height statistics and percentiles, from ICESat-2’ Land, Water and Vegetation Elevation product (ATL08) product was assessed in 12 sites across six major biomes in the United States. The agreement between ATL08 and ALS heights was assessed using the mean bias (Bias, ATL08 – ALS), the mean absolute error (MAE) and their percent equivalents, percent bias (pBias) and percent MAE (pMAE), respectively. In general, the agreement between ATL08 and ALS terrain heights was high (Bias 0.18 m, pBias 0.1%) while canopy heights showed lower agreement (Bias −1.71 m, pBias −15.9%). Analyses by biome, time of acquisition and beam strength of the ICESat-2 photon data also showed generally higher agreement for ATL08 terrain than canopy heights. Analyses also showed the performance of ATL08 heights varied with canopy cover with ATL08 terrain heights showing the best agreement when canopy cover was between 40 and 70% while the best performance for ATL08 canopy heights was observed when canopy cover was greater than 80%. This observation, coupled with analyses by biome, indicate that ATL08 canopy heights are more suitable in relatively dense canopy environments such as conifer and broadleaf forests than relatively sparse environments such a temperate grassland and Savannas. Higher level canopy height percentiles (95th and 98th) showed higher agreement (mean Bias −12.5%) with ALS heights than lower percentiles (minimum, 25th, mean pBias ~39.2%). These findings indicate that ATL08 canopy heights show more promise for routine canopy height characterization using the 95th and 98% percentiles but is limited in characterizing intermediate vertical structure. The observed performance differences between ATL08 terrain and canopy heights are attributed to differences in photon sampling rates over terrain and canopy surfaces which, compounded with background noise in ICESat-2 photon data, led to different effectiveness for ATL08 processing routines in filtering terrain and off-terrain points. This assessment of the impact of a variety of factors provides the vegetation community with an understanding of the capabilities and limitations of height estimates from the ICESat-2 ATL08 product.

Performance of Two Thin Epoxy Overlays on New Concrete under Laboratory and Outdoor Exposure Conditions

Thin epoxy overlays are used for improving the condition and extending the service life of bridge decks. The tensile bond pull-off strength, evaluated as per ASTM C1583, is used as the performance indicator. A failure in the substrate with a tensile strength of 250 pounds per square inch (psi) or greater is considered acceptable. However, the performance of in-service bridge decks when evaluated shows inconsistent results. Such studies failed to record and correlate the parameters that influence overlay performance during testing to clarify the observed variations. Laboratory studies by several researchers have documented a distinct performance difference when the overlays are exposed to room temperatures in comparison with elevated temperatures. However, the most influential parameters, such as the variation of substrate moisture against temperature and epoxy softening under elevated temperatures, were not measured and correlated to the observed performance. This study was initiated to provide clarification of the observed performance differences by evaluating the impact of concrete age at the time of epoxy application, concrete mix ingredients, exposure conditions, concrete microstructure development, and substrate moisture and temperature on the performance of two epoxy overlays. Experimental results confirm that (i) the performance of epoxy overlays improves when the concrete mix contains slag and (ii) substrate moisture vapor pressure and epoxy softening under elevated temperature negatively affect the overlay performance.

Measuring Distinct Social Skills via Multiple Speed Assessments

Abstract. Social skills (e.g., persuading others, showing compassion, staying calm) are of key importance in work and education settings. Accordingly, the goal of many selection processes is to identify candidates who excel in desired skills. For this, high-fidelity simulations such as assessment centers (ACs) are regarded as ideal procedures because they can be used to evoke, observe, and evaluate candidates’ actual behavior. However, research has repeatedly shown that observed performance differences in ACs are not sufficiently driven by the specific skill dimensions that are defined for assessment. Building on multiple speed assessments and incorporating insights from behavioral personality science, we offer an alternative approach for the reliable and valid assessment of distinct social skills. We hereby (a) selected skills on the basis of a bottom-up analysis of observable and distinguishable interpersonal behaviors and (b) specifically designed exercises around these skills (i.e., one skill per exercise, multiple exercises per skill). Here, we present the initial results of this newly developed procedure across three samples in a high-stakes selection context ( N = 589). Generalizability theory analyses showed that a substantial amount of variance in assessor ratings could be attributed to the selected skills. This underlines the importance of more behaviorally focused selection procedures.