Recent advances in tissue engineering have adapted the additive manufacturing technology, also known as three-dimensional printing, which is used in several industrial applications, for the ...fabrication of bioscaffolds and viable tissue and/or organs to overcome the limitations of other in vitro conventional methods. 3D bioprinting technology has gained enormous attention as it enabled 3D printing of a multitude of biocompatible materials, different types of cells and other supporting growth factors into complex functional living tissues in a 3D format. A major advantage of this technology is its ability for simultaneously 3D printing various cell types in defined spatial locations, which makes this technology applicable to regenerative medicine to meet the need for suitable for transplantation suitable organs and tissues. 3D bioprinting is yet to successfully overcome the many challenges related to building 3D structures that closely resemble native organs and tissues, which are complex structures with defined microarchitecture and a variety of cell types in a confined area. An integrated approach with a combination of technologies from the fields of engineering, biomaterials science, cell biology, physics, and medicine is required to address these complexities. Meeting this challenge is being made possible by directing the 3D bioprinting to manufacture biomimetic-shaped 3D structures, using organ/tissue images, obtained from magnetic resonance imaging and computerized tomography, and employing computer-aided design and manufacturing technologies. Applications of 3D bioprinting include the generation of multilayered skin, bone, vascular grafts, heart valves, etc. The current 3D bioprinting technologies need to be improved with respect to the mechanical strength and integrity in the manufactured constructs as the presently used biomaterials are not of optimal viscosity. A better understanding of the tissue/organ microenvironment, which consists of multiple types of cells, is imperative for successful 3D bioprinting.
Prediction of fire risk based on cloud computing Zhang, Xiaoying
Alexandria Engineering Journal /Alexandria Engineering Journal,
February 2021, 2021-02-00, 2021-02-01, Letnik:
60, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The development of cloud computing and big data analysis has given rise to various disaster prediction methods. To reduce the probability of fire accidents, it is critical to predict the fire risk by ...mining the massive historical data on fire. Considering the advantages of MapReduce, a cloud computing method, in parallel processing of data, this paper puts forward a novel prediction method for fire risk that mines the association rules in the time domain. Firstly, the risk of disaster-causing factors and the ability of disaster-reducing factors were evaluated. Based on the evaluation results, an evaluation index system was constructed for fire risk, and the indices were quantified through proper weighting. Facing the historical fire data, the authors designed the spatiotemporal density-based spatial clustering of applications with noise (spatiotemporal DBSCAN), and quantitatively evaluated fire risk by the association rule mining algorithm based on time domain partition (TDP). The effectiveness of our method in fire risk prediction was verified through experiments. The research results provide reference for the risk prediction of other disasters.
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► The preparation of nanostructured transition metal nitrides is discussed. ► Recent researches of transition metal nitrides for energy conversion and storage are reviewed. ► The ...concept of mixed (ionic and electronic) conduction is highlighted.
The research of advanced electrodes for energy storage and energy conversion has been an important strategy to satisfy the ever increasing need for future electrochemical power sources. In this review, we describe the recent studies on the preparation and application of nanostructured transition metal nitrides as alternative electrode materials. We also highlight the design of nanostructured materials needed to balance the electronic and ionic conduction, which are of great importance for achieving enhanced performance of electrode in electrochemical devices. By providing this brief survey, we aim to illustrate that transition metal nitrides with proper nanostructure would result in improved electrochemical performance of electrode materials in energy storage and conversion applications.
•A flexible optimal experiment design framework is developed.•The optimized network provides sufficient information with high data worth.•The monitoring locations are optimized for enhancing plume ...characterization.•The test duration is selected with information entropy and parameter uncertainty.
This study develops an integrated framework to guide the monitoring network optimization and duration selection for solute transport in heterogeneous sand tank experiments. The method is designed based on entropy and data worth analysis. Numerical models are applied to approach prior observation datasets and to support optimization analysis. Several candidate monitoring locations are synthetically assumed in numerical models. Entropy analysis considers local scale heterogeneity in experiment and identifies stable monitoring locations through extracting maximum information and minimizing optimization redundancies. Data worth analysis quantifies the potential of observation data to reduce the uncertainty of key parameters and selects the monitoring locations with higher data worth. Final monitoring network comprises of optimized monitoring locations obtained based on entropy and data worth analysis. A lab-scale tracer experiment is presented to explore the applicability of the proposed framework. Results show that the optimized monitoring network can accurately characterize the distribution of contaminant plumes in 3D domains and provides estimation of key flow and transport parameters (e.g., hydraulic conductivity and dispersivity). With the extension of experiment time, the total information of monitoring network is maximized, while the uncertainty of key parameters is minimized. The recommended experimental duration is the time by which both joint entropy and parameter variation coefficients are stabilized. Our developed methodology can be used as a flexible and powerful tool to design more complex transport experiments at different spatiotemporal scales.
•The spatial diversity of bacterial and archaeal community in mangrove-inhabited mudflat is studied•Gradient variation of microbial community composition is found from high tidal flat to mid/low ...tidal flat•Stratified distribution of microbial communities is detected from surface to subsurface layer at a depth of 50 cm•The metabolism functions, such as photoautotrophy, sulfur-related respiration, and nitrification are varied in the profile•DO and DTN are the main physiochemical factors affecting the distribution of functional microbes in the mudflat
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The gradient distribution of microbial communities has been detected in profiles along many natural environments. In a mangrove seedlings inhabited mudflat, the microbes drive a variety of biogeochemical processes and are associated with a dramatically changed environment across the tidal zones of mudflat. A better understanding of microbial composition, diversity and associated functional profiles in relation to physicochemical influences could provide more insights into the ecological functions of microbes in a coastal mangrove ecosystem. In this study, the variation of microbial community along successive tidal flats inhabited by mangrove seedlings were characterized based on the 16S rDNA gene sequences, and then the factors that shape the bacterial and archaeal communities were determined. Results showed that the tidal cycles strongly influence the distribution of bacterial and archaeal communities. Dissimilarity and gradient distribution of microbial communities were found among high tidal flat, mid-low tidal flat and seawater. Discrepancies were also as well observed from the surface to subsurface layers specifically in the high tidal flat. For example, Alphaproteobacteria displayed an increasing trend from low tidal to high tidal flat and vice versa for Deltaproteobacteria; Cyanobacteria and Thaumarchaeota were more dominant in the surface layer than the subsurface. In addition, by classifying the microorganisms into metabolic functional groups, we were able to identify the biogeochemical pathway that was dominant in each zone. The (oxygenic) photoautotrophy and nitrate reduction were enhanced in the mangrove inhabited mid tidal flat. It revealed the ability of xenobiotic metabolism microbes to degrade, transform, or accumulate environmental hydrocarbon pollutants in seawater, increasing sulfur-related respiration from high tidal to low tidal flat. An opposite distribution was found for major nitrogen cycling processes. The shift of both composition and function of microbial communities were significantly related to light, oxygen availability and total dissolved nitrogen instead of sediment types or salinity.
This study focuses on the source apportionments of polycyclic aromatic hydrocarbons (PAHs) in road dust (RD) with four size fractions through three receptor models of principal component analysis ...with multiple linear regression (PCA-MLR), positive matrix factorization (PMF) and Unmix. The concentrations of total PAHs range from 0.45 to 2.03μgg−1. Results show that the concentrations of PAHs increased with a decreasing size fraction. Similar potential sources to PAHs in RD were extracted by three models with a little difference in numbers and percent load contributions of each identified sources. The overall proportion of the identified sources were ranked as vehicular emission>coke oven>surface pavement>others in each size fractions. In terms of risk assessment, the mean values of incremental lifetime cancer risk (ILCR) of the total cancer risk of PAHs in RD were lower than the baseline value of an acceptable risk. However, PAHs in smaller size fraction prone to have a higher adverse effect on children via ingestion. Furthermore, the ecological risk assessment of hazard quotients and mean hazard quotients indicated that PAHs in RD had a 9% probability of being toxic to the benthic organisms and aquatic environment.
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•PAHs contents in road dust decreased with an increasing size fraction.•Model comparison between PCA-MLR, PMF and Unmix.•PCA-MLR identified a lower number of PAHs sources.•The differentiation of vehicular emissions by Unmix was dataset dependent.•PMF could give a more complete source apportionment.•Traffic-related sources were primary contributors to PAHs in road dust.
Daily precipitation data of 741 meteorological stations ranging from 1956 to 2005 were used to run a preliminary investigation of changes in temporal and spatial distribution of precipitation ...intensity and frequency in ten hydrological regions in China. Average annual and seasonal spatial values of indices of precipitation characteristics (i.e. precipitation amount, intensity and frequency) were obtained using a Kriging interpolation method. Temporal tendencies were calculated by Mann-Kendall's method. The trends of extreme rainfall events and precipitation-based droughts were also discussed in ten hydrological regions using the maximum daily precipitation and dry spell duration in a year. Results show that precipitation intensity has significant increasing trends while precipitation frequency has significant decreasing trends over China. Meanwhile precipitation has a major decline in autumn and a slight increase in winter. Both extreme rainfall events and precipitation-based droughts show a general increasing trend. The aggravated spatial and temporal unevenly distributed precipitation leads to more water shortage as well as floods in China.
•We examine the precipitation pattern in hydrological regions in China.•Significantly increasing trend is found in precipitation intensity.•Significantly decreasing trend is found in precipitation frequency.•These trends vary in different hydrological regions and different seasons.
The hydrological response to climate change and human activities plays a pivotal role in the field of water resource management within a given basin. This study was conducted with a primary focus on ...the Du River basin, aiming to assess and quantify the impacts of climate change and human activities on changes in runoff patterns. The study utilized the Budyko framework in conjunction with the Soil and Water Assessment Tool (SWAT) model to project future changes in runoff while also employing statistical tests like the Pettitt and Mann–Kendall tests to identify abrupt shifts and monotonic trends in the data. The results shows that (1) The analysis of runoff data spanning from 1960 to 2016 revealed a significant declining trend (p < 0.05) in annual runoff, with an abrupt change point identified in 1994. The multi-year average runoff depth was determined to be 495 mm. (2) According to the Budyko framework, human activities were found to be the dominant driver behind runoff changes, contributing significantly at 74.42%, with precipitation changes contributing 24.81%. (3) The results obtained through the SWAT model simulation indicate that human activities accounted for 61.76% of the observed runoff changes, whereas climate change played a significant but slightly smaller role, contributing 38.24% to these changes. (4) With constant climate conditions considered, the study predicted that runoff will continue to decrease from 2017 to 2030 due to the influence of ongoing and future human activities. However, this downward trend was found to be statistically insignificant (p > 0.1). These findings provide valuable insights into the quantitative contributions of climate change and human activities to runoff changes in the Du River basin. This information is crucial for decision-makers and water resource managers, as it equips them with the necessary knowledge to develop effective and sustainable strategies for water resource management within this basin.
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•Predict groundwater levels (GWLs) using wavelet and artificial neural network hybrid model.•Analyze the correlation between GWL and semi-diurnal tide using wavelet coherence.•Wavelet ...improves ANN model prediction performance.
It is challenging to predict groundwater level (GWL) accurately and reliably in reclaimed coastal areas, the dynamic of which has been greatly altered by the land reclamation activities, due to its diverse engineering boundaries. In this study, the data-driven Artificial Neural Network (ANN) models (nonlinear input-output network (NIO), nonlinear autoregressive network with exogenous inputs (NARX), and wavelet-NARX (WA-NARX)) are used to predict the GWL in a newly reclaimed land on Zhoushan Island, China. The models use semi-diurnal tide (SDT) and precipitation as input variables, and the wavelet coherence (WTC) is applied to analyze the response of GWL to SDT. The results show that the WA-NARX hybrid model provides better prediction performance, especially for short-term periods. Meanwhile, we also explore the correlations between GWL and SDT in the filled and clay layers by using the WTC and the global coherence coefficient (GCC). The results show strong correlations at 0.5-, 1-, and 15-day time scales (resonance periodicities), which are then used as prediction periods for ANN models. The correlation is stronger for the filled layer than that for the clay layer indicates GWL in the filled layer is more sensitive to SDT, especially at 0.5-day time scale. The predicted results also confirm that SDT and precipitation have great influences on GWL with better prediction in the filled layer.
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