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A wide variety of bridge barriers are used on highways. The bearing capacities of different types of deck slabs are measured in this study by applying the yield line theory to determine the worst-case scenario. An improved configuration for the worst-case barrier base and deck slab is developed, namely the universal double-beam assembled bridge barrier, which can enhance the safety performance of exiting concrete-base bridge barriers. According to the simulated impact test results, the new barrier meets SS-level requirements in terms of containment, redirective, and buffering performance as specified in the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). The barrier structure's compatibility with various bases is also analyzed. SS-level impact tests are conducted on real vehicles, including cars, buss, and trucks. The results show that the safety performance of the new barrier configuration reaches SS-level, and the barrier is universally compatible with concrete bases with a height of 63 cm and above. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has created a public health crisis. This study aimed to evaluate the diagnostic performance of the Panbio and STANDARD Q COVID-19 antigen rapid diagnostic tests (RDTs) against the real-time polymerase chain reaction (RT-PCR) at one of the largest hospitals in southern Ethiopia. Nasopharyngeal samples, which were collected during the pandemic from individuals suspected of COVID-19 and stored at − 70 °C, were analyzed in June and July 2022. The performance of the Panbio COVID-19 antigen tests was evaluated in 200 randomly selected nasopharyngeal samples (100 positives and 100 negatives for severe acute respiratory syndrome 2 by RT-PCR). The STANDARD Q test was evaluated using 100 positive and 50 negative samples. The respective sensitivity, specificity, positive predictive value and negative predictive values were 88%, 99%, 98.9% and 89.2% for the Panbio test and 91%, 98%, 98.9% and 84.5%, for the STANDARD Q test. The kappa values were 0.87 for the Panbio and 0.86 for the STANDARD Q test. Based on the findings presented here, the RDTs could be utilized as an alternative to conventional RT-PCR when it is challenging to diagnose COVID-19 owing to a lack of time, skilled lab personnel, or suitable equipment or electricity. [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]

Mechanical testing by sporting governing bodies ensures artificial turf surfaces conform with performance standards. Rotational traction is measured using two equivalent devices: a rotational traction tester (RTT) and a lightweight rotational traction tester (LRTT). The devices differ in target rotational velocity; 72 deg/s for the RTT and 30 deg/s for the LRTT. The purpose of this study was to investigate the influence of rotational velocity on peak torque during rotational traction testing. An automated rotational traction tester examined nine rotational velocities between 10 and 210 deg/s, and ten artificial turf surface systems with a range of performance infill materials, infill depths and carpet specifications. Rotations at 10 deg/s produced the lowest peak torques on nine of the ten surfaces. Infill materials with intrinsic viscoelastic properties produced significantly higher peak torques at higher rotational velocities, whereas less elastic infill materials saw no significant increase in peak torque. A mean difference in peak torque of 2.6 Nm was found between the target velocities of the RTT and LRTT. The results support the synchronisation of target velocities for the RTT and LRTT. During standards testing, trials completed below a particular velocity should be repeated to negate velocity effects on peak torque. [ABSTRACT FROM AUTHOR]

Sustained multisensory integration over long inter-stimulus time delays is typically found in older adults, particularly those with a history of falls. However, the extent to which the temporal precision of audio-visual integration is associated with longitudinal fall or fall risk trajectories is unknown. A large sample of older adults (N = 2319) were grouped into longitudinal trajectories of self-reported fall incidents (i.e., decrease, stable, or increase in number) and, separately, their performance on a standard, objective measure of fall risk, Timed Up and Go (TUG; stable, moderate decline, severe decline). Multisensory integration was measured once as susceptibility to the Sound-Induced Flash Illusion (SIFI) across three stimulus onset asynchronies (SOAs): 70 ms, 150 ms and 230 ms. Older adults with an increasing fall number showed a significantly different pattern of performance on the SIFI than non-fallers, depending on age: For adults with increasing incidents of falls, those aged 53–59 years showed a much smaller difference in illusion susceptibility at 70 ms versus 150 ms than those aged 70 + years. In contrast, non-fallers showed a more comparable difference between these SOA conditions across age groups. There was no association between TUG performance trajectories and SIFI susceptibility. These findings suggests that a fall event is associated with distinct temporal patterns of multisensory integration in ageing and have implications for our understanding of the mechanisms underpinning brain health in older age. [ABSTRACT FROM AUTHOR]

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. [ABSTRACT FROM AUTHOR]

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability. [ABSTRACT FROM AUTHOR]

Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested − GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested − GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability. [ABSTRACT FROM AUTHOR]

Quantum annealing has been actively researched since D-Wave Systems produced the first commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one of the real-world applications utilizing quantum annealing. In this study, we propose a formulation to control the traveling routes to minimize the travel time. We validate our formulation through simulation in a virtual plant and authenticate the effectiveness for faster distribution compared to a greedy algorithm that does not consider the overall detour distance. Furthermore, we utilize reverse annealing to maximize the advantage of the D-Wave's quantum annealer. Starting from relatively good solutions obtained by a fast greedy algorithm, reverse annealing searches for better solutions around them. Our reverse annealing method improves the performance compared to standard quantum annealing alone and performs up to 10 times faster than a commercial classical solver, Gurobi. This study extends a use of optimization with general problem solvers in the application of multi-AGV systems and reveals the potential of reverse annealing as an optimizer. [ABSTRACT FROM AUTHOR]

Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios—will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13 % more frequently ( χ 2 = 59.06 , p < 0.001 ) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13 % less likely to identify the correct solution ( χ 2 = 58.79 , p < 0.001 ). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work. [ABSTRACT FROM AUTHOR]

Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based on an interplay between community and hierarchical structures. It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed. The sparsity of the network is exploited for implementing a more efficient algorithm. We demonstrate our method on synthetic and real-world data and compare performance with two standard approaches for community detection and ranking extraction. We find that the algorithm accurately retrieves the overall node's preference in different scenarios, and we show that it can distinguish small subsets of nodes that behave differently than the majority. As a consequence, the model can recognize whether a network has an overall preferred interaction mechanism. This is relevant in situations where there is no clear "a priori" information about what structure explains the observed network datasets well. Our model allows practitioners to learn this automatically from the data. [ABSTRACT FROM AUTHOR]

Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses. [ABSTRACT FROM AUTHOR]

Early diagnosis of hepatitis C virus (HCV) infection is essential for prompt initiation of treatment and prevention of transmission, yet several logistical barriers continue to limit access to HCV testing. Dried blood spot (DBS) technology involves a simple fingerstick that eliminates the need for trained personnel, and DBS can be stored and transported at room temperature. We evaluated the use of DBS whole blood samples in the modified Abbott ARCHITECT anti-HCV assay, comparing assay performance against the standard assay run using DBS and venous plasma samples. 144 HCV positive and 104 HCV negative matched venous plasma and whole blood specimens were selected from a retrospective study with convenience sampling in Cameroon. Results obtained using a modified volume DBS assay were highly correlated to the results of the standard assay run with plasma on clinical samples and dilution series (R2 = 0.71 and 0.99 respectively). The ARCHITECT Anti-HCV assay with input volume modification more accurately detects HCV antibodies in DBS whole blood samples with 100% sensitivity and specificity, while the standard assay had 90.97% sensitivity. The use of DBS has the potential to expand access to HCV testing to underserved or marginalized populations with limited access to direct HCV care. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

High-performance ultra-thin oxide layers are required for various next-generation electronic and optical devices. In particular, ultra-thin resistive switching (RS) oxide layers are expected to become fundamental building blocks of three-dimensional high-density non-volatile memory devices. Until now, special deposition techniques have been introduced for realization of high-quality ultra-thin oxide layers. Here, we report that ultra-thin oxide layers with reliable RS behavior can be self-assembled by field-induced oxygen migration (FIOM) at the interface of an oxide-conductor/oxide-insulator or oxide-conductor/metal. The formation via FIOM of an ultra-thin oxide layer with a thickness of approximately 2-5 nm and 2.5% excess oxygen content is demonstrated using cross-sectional transmission electron microscopy and secondary ion mass spectroscopy depth profile. The observed RS behavior, such as the polarity dependent forming process, can be attributed to the formation of an ultra-thin oxide layer. In general, as oxygen ions are mobile in many oxide-conductors, FIOM can be used for the formation of ultra-thin oxide layers with desired properties at the interfaces or surfaces of oxide-conductors in high-performance oxide-based devices. [ABSTRACT FROM AUTHOR]

One disadvantage of the JCR impact factor, the most commonly used assessment tool for ranking and evaluating scientific journals, is its inability in distinguishing among different shapes of citation distribution curves, leading to unfair evaluation of journals in some cases. This paper aims to put forward an alternative impact factor (IF') that can properly reflect citation distributions. The two impact factors are linearly and positively correlated, and have roughly the same order of magnitude. Because of the ability of IF9 in distinguishing among different shapes of citation distribution curves, IF' may properly reflect the academic performance of a scientific journal in a way that is different from the JCR impact factor with some unique features that reward journals with highly cited papers. Therefore, it is suggested that IF9 could be used to complement the JCR impact factor. [ABSTRACT FROM AUTHOR]