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Artificial intelligence-based algorithms are becoming essential tools in materials science-related fields because of their excellent functionality in reflecting physics in the training database and predicting the properties of unexplored materials with outstanding accuracy. Designing novel materials with engineered properties, such as metamaterials, is the key to revolutionizing material discovery, and machine learning (ML) and deep learning (DL) can be powerful and indispensable tools for acceleration. This review focuses on the implementation of ML/DL-based approaches for designing metamaterials. Quantum–mechanical, atomistic, and macroscale simulation methods are also assessed as database construction processes. Forward and inverse design methods are summarized in detail, and breakthroughs in generative models are particularly introduced. Moreover, applications in fundamental property prediction and material structural design are reviewed. Finally, the remaining challenging tasks for future related work are presented.

Feed enzymes have been extensively used in livestock diets to enhance nutrient digestion and promote their growth performance. Indeed, recent research has indicated that feed enzymes, notably phytase, protease, and xylanase, function as catalysts, facilitating the breakdown of phytic acid, proteins, and β-1,4-xylan bonds and offering prospective advantages linked to the intestinal well-being and microbiota of young pigs and chickens. Various feed enzymes are currently being added to the diets of swine and broiler chickens. The potential enzymes used in the feed industry include cellulase, β-mannanase, β-glucanases, xylanases, phytases, proteases, lipases, and galactosidases. Though significant research has been conducted on phytase, protease, and xylanase, consistent findings, particularly in terms of improving nutrient digestibility and promoting growth performance of monogastric animals, are still limited. Also, the outcome of recent studies raises the question whether phytase and xylanase could play functional roles beyond increasing nutrient digestibility and intestinal health, such as positively modulating the intestinal microbiota and reducing environmental problems. Therefore, in this review we aimed to address the functional roles of exogenous enzyme activities in monogastric animal diets. Also, we sought to explore the advantages of these enzymes in enhancing the nutritional value of both alternative and conventional feedstuffs.

Because using social media has become a major part of people's daily lives, many of their personal characteristics are often implicitly or explicitly reflected in the content they share. We present a study of two personal characteristics-age and gender-related to user engagement on Instagram that can be determined through the characterization of images and tags. We demonstrate the strong influence of age and gender on Instagram use in terms of topical and content differences. We then build age and gender classification models that yield F1 scores of up to 88% and 74% in the detection of age and gender, respectively, and that better characterize users by images than by tags. We further demonstrate the robustness of our models using a new set of test data, with which the models exhibit greater overall performance than human raters. Our study highlights that future research should look to exploit images to a greater degree because they complement text and there are many unexamined images with no embedded text available.

Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.

This paper presents a multiresolution topology optimization (MTOP) scheme to obtain high resolution designs with relatively low computational cost. We employ three distinct discretization levels for the topology optimization procedure: the displacement mesh (or finite element mesh) to perform the analysis, the design variable mesh to perform the optimization, and the density mesh (or density element mesh) to represent material distribution and compute the stiffness matrices. We employ a coarser discretization for finite elements and finer discretization for both density elements and design variables. A projection scheme is employed to compute the element densities from design variables and control the length scale of the material density. We demonstrate via various two- and three-dimensional numerical examples that the resolution of the design can be significantly improved without refining the finite element mesh.

Aging water infrastructure in the United States (U.S.) is a growing concern. In the U.S., over 90,000 dams were registered in the 2018 National Inventory of Dams (NID) database, and their average age was 57 years old. Here, we aim to assess spatiotemporal patterns of the growth of artificial water storage of the existing dams and their hazard potential and potential economic benefit. In this study, we use more than 70,000 NID-registered dams to assess the cumulative hazard potential of dam failure in terms of the total number and the cumulative maximum storage of dams over the 12 National Weather Service River Forecast Center (RFC) regions. In addition, we also estimate potential economic benefits of the existing dams based on their cumulative storage capacity. Results show that the ratios of the cumulative storage capacity to the long-term averaged precipitation range from 8% (Mid-Atlantic) to 50% (Colorado), indicating the significant anthropogenic contribution to the land surface water budget. We also find that the cumulative storage capacity of the dams with high (probable loss of human life is if the dam fails) and significant (potential economic loss and environmental damage with no probable casualty) hazard potential ranges from 50% (North Central) to 98% (Missouri and Colorado) of the total storage capacity within the corresponding region. Surprisingly, 43% of the dams with either high or significant potential hazards have no Emergency Action Plan. Potential economic benefits from the existing dams range from$0.7 billion (Mid Atlantic) to $ 15.4 billion (West Gulf). Spatiotemporal patterns of hazard potential and economic benefits from the NID-registered dams indicate a need for the development of region-specific preparation, emergency, and recovery plans for dam failure. This study provides an insight about how big data, such as the NID database, can provide actionable information for community resilience toward a safer and more sustainable environment.

Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.

Detecting failure cases is an essential element for ensuring the safety self-driving system. Any fault in the system directly leads to an accident. In this paper, we analyze the failure of semantic segmentation, which is crucial for autonomous driving system, and detect the failure cases of the predicted segmentation map by predicting mean intersection of union (mIoU). Furthermore, we design a deep neural network for predicting mIoU of segmentation map without the ground truth and introduce a new loss function for training imbalance data. The proposed method not only predicts the mIoU, but also detects failure cases using the predicted mIoU value. The experimental results on Cityscapes data show our network gives prediction accuracy of 93.21% and failure detection accuracy of 84.8%. It also performs well on a challenging dataset generated from the vertical vehicle camera of the Hyundai Motor Group with 90.51% mIoU prediction accuracy and 83.33% failure detection accuracy.

This paper proposes a dual adaptive Kalman filter to identify parameters of a dynamic system that may experience sudden damage by a dynamic excitation such as earthquake ground motion. While various filter techniques have been utilized to estimate system’s states, parameters, input (force), or their combinations, the filter proposed in this paper focuses on tracking parameters that may change suddenly using sparse measurements. First, an advanced state-space model of parameter estimation employing a regularization technique is developed to overcome the lack of information in sparse measurements. To avoid inaccurate or biased estimation by conventional filters that use covariance matrices representing time-invariant artificial noises, this paper proposes a dual adaptive filtering, whose slave filter corrects the covariance of the artificial measurement noises in the master filter at every time-step. Since it is generally impossible to tune the proposed dual filter due to sensitivity with respect to parameters selected to describe artificial noises, particle swarm optimization (PSO) is adopted to facilitate optimal performance. Numerical investigations confirm the validity of the proposed method through comparison with other filters and emphasize the need for a thorough tuning process.

Sargassum horneri, a brown seaweed, is known for its various health benefits; however, there are no reports on its effects on depression. This study aimed to investigate the antidepressant effects of S. horneri ethanol extract (SHE) in mice injected with corticosterone (CORT) and to elucidate the underlying molecular mechanisms. Behavioral tests were conducted, and corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and CORT levels were measured. A fluorometric monoamine oxidase (MAO) enzyme inhibition assay was performed. Neurotransmitters like serotonin, dopamine, and norepinephrine levels were determined. Moreover, the ERK-CREB-BDNF signaling pathway in the prefrontal cortex and hippocampus was evaluated. Behavioral tests revealed that SHE has antidepressant effects by reducing immobility time and increasing time spent in open arms. Serum CRH, ACTH, and CORT levels decreased in the mice treated with SHE, as did the glucocorticoid-receptor expression in their brain tissues. SHE inhibited MAO-A and MAO-B activities. In addition, SHE increased levels of neurotransmitters. Furthermore, SHE activated the ERK-CREB-BDNF pathway in the prefrontal cortex and hippocampus. These findings suggest that SHE has antidepressant effects in CORT-injected mice, via the regulation of the hypothalamic-pituitary-adrenal axis and monoaminergic pathway, and through activation of the ERK-CREB-BDNF signaling pathway. Thus, our study suggests that SHE may act as a natural antidepressant.