The stability of side-exposed backfill is essential to ensure a successful mining operation. Until now, it has been analyzed without considering the creep of rock mass. In practice, stope excavation ...and backfilling are always scheduled with different time during which fill mechanical properties can evolve and rocks exhibit more or less creep deformation. In this study, time-dependent stability and minimum required cohesion (
c
min
) of side-exposed backfill associated with the creep of surrounding rock mass are, for the first time, analyzed through numerical modeling with FLAC
3D
. A distinction is made between the cohesion at failure and
c
min
. Results show that the empty time of primary stope does not significantly affect the stability and
c
min
. When mine depth is small and the rock exhibits little creep, it deserves to wait longer time before adjacent extraction for the backfill to gain more strength. When the mine depth is large or/and the rock exhibits heavy creep, the instability of side-exposed backfill can be dictated by crushing failure. A stronger backfill means also a harder backfill, which absorbs larger compressive stress and is more prone to be crushed. In this condition, a softer backfill can be better through the use of lower binder content or/and with a shorter curing time. The adjacent secondary stope should be filled as soon as possible to avoid failure of side-exposed backfill. More simulations were done on the effects of stope geometry and mechanical properties of backfill and rock mass on the stability and
c
min
of side-exposed backfill.
Highlights
The empty time of primary stope does not significantly affect the stability of side-exposed backfill.
When the mine depth is small and the rock exhibits little creep, it deserves to wait longer time before adjacent extraction for the backfill to gain more strength.
When the mine depth is large and the rock exhibits heavy creep, a softer backfill can be better through the use of lower binder content or with a shorter curing time to prevent crushing failure.
The extracted secondary stope should be filled as soon as possible to avoid failure of side-exposed backfill.
Optimization of sizes for the primary and secondary stopes is needed to minimize the overall backfilling cost.
Proton conducting oxide electrolyte materials could potentially lower the operating temperature of metal-supported solid oxide cells (MS-SOCs) to the intermediate range 400 to 600 °C. The porous ...metal substrate provides the advantages of MS-SOCs such as high thermal and redox cycling tolerance, low-cost of structural materials, and mechanical ruggedness. In this work, viability of co-sintering fabrication of metal-supported proton conducting solid oxide cells is investigated. Candidate proton conducting oxides including perovskite oxides BaZr0.7Ce0.2Y0.1O3−δ, SrZr0.5Ce0.4Y0.1O3−δ, and Ba3Ca1.18Nb1.82O9−δ, pyrochlore oxides La1.95Ca0.05Zr2O7−δ and La2Ce2O7, and acceptor doped rare-earth ortho-niobate La0.99Ca0.01NbO4 are synthesized via solid state reactive or sol-gel methods. These ceramics are sintered at 1450 °C in reducing environment alone and supported on Fe-Cr alloy metal support, and their key characteristics such as phase formation, sintering property, and chemical compatibility with metal support are determined. Most electrolyte candidates suffer from one or more challenges identified for this fabrication approach, including: phase decomposition in reducing atmosphere, evaporation of electrolyte constituents, contamination of the electrolyte with Si and Cr from the metal support, and incomplete electrolyte sintering. In contrast, La0.99Ca0.01NbO4 is found to be highly compatible with the metal support and co-sintering processing in reducing atmosphere. A metal-supported cell is fabricated with La0.99Ca0.01NbO4 electrolyte, ferritic stainless steel support, Pt air electrode and nanoparticulate ceria-Ni hydrogen electrocatalyst. The total resistance is 50 Ω·cm2 at 600 °C. This work clearly demonstrates the challenges, opportunities, and breakthrough of metal-supported proton-conducting solid oxide cells by co-sintering fabrication.
•LCZ and LCO do not survive sintering in reducing atmosphere.•BZCY is not fully densified at 1450 °C (the upper limit for 434 SS metal support).•Most candidates suffer evaporation and reaction with Si/Cr from the metal support.•LCN can be densified at 1300 °C and does not evaporate or react with metal support.•LCN based metal-supported cell shows total resistance of 50 Ω·cm2 at 600 °C.
Finite element analysis (FEA) is a widely used tool in a variety of industries and research endeavors. With its application to spine biomechanics, FEA has contributed to a better understanding of the ...spine, its components, and its behavior in physiological and pathological conditions, as well as assisting in the design and application of spinal instrumentation, particularly spinal interbody cages (ICs). IC is a highly effective instrumentation for achieving spinal fusion that has been used to treat a variety of spinal disorders, including degenerative disc disease, trauma, tumor reconstruction, and scoliosis. The application of FEA lets new designs be thoroughly "tested" before a cage is even manufactured, allowing bio-mechanical responses and spinal fusion processes that cannot easily be experimented upon
to be examined and "diagnosis" to be performed, which is an important addition to clinical and
experimental studies. This paper reviews the recent progress of FEA in spinal ICs over the last six years. It demonstrates how modeling can aid in evaluating the biomechanical response of cage materials, cage design, and fixation devices, understanding bone formation mechanisms, comparing the benefits of various fusion techniques, and investigating the impact of pathological structures. It also summarizes the various limitations brought about by modeling simplification and looks forward to the significant advancement of spine FEA research as computing efficiency and software capabilities increase. In conclusion, in such a fast-paced field, the FEA is critical for spinal IC studies. It helps in quantitatively and visually demonstrating the cage characteristics after implanting, lowering surgeons' learning costs for new cage products, and probably assisting them in determining the best IC for patients.
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer’s disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the ...normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.
Flower color can be applied to landscaping and identification of the purity of seeds in hybrid production. However, the molecular basis of white flower trait remains largely unknown in Brassica rapa. ...In this study, an F
population was constructed from the cross between 15S1040 (white flower) and 92S105 (yellow flower) for fine mapping of white flower genes in B. rapa. Genetic analysis indicated that white flower trait is controlled by two recessive loci, Brwf1 and Brwf2. Using InDel and SNP markers, Brwf1 was mapped to a 49.6-kb region on chromosome A01 containing 9 annotated genes, and among them, Bra013602 encodes a plastid-lipid associated protein (PAP); Brwf2 was located in a 59.3-kb interval on chromosome A09 harboring 12 annotated genes, in which Bra031539 was annotated as a carotenoid isomerase gene (CRTISO). The amino acid sequences of BrPAP and BrCRTISO were compared between two yellow-flowered and three white-flowered lines and critical amino acid mutations of BrPAP and BrCRTISO were identified between yellow-flowered and white-flowered lines. Therefore, Bra013602 and Bra031539 were predicted as potential candidates for white flower trait. Our results provide a foundation for further identification of Brwf and increase understanding of the molecular mechanisms underlying white flower formation in Chinese cabbage.
1,4-Dioxane (dioxane), an emerging groundwater contaminant, is frequently detected in landfill leachates with its structural analog, tetrahydrofuran (THF). Along with undesirable leakage of landfill ...leachates, dioxane and THF inevitably percolate into groundwater leading to a broader region of contamination. Cometabolic bioremediation is an effective approach to manage commingled THF and dioxane pollution. In this study, a newly isolated bacterium Arthrobacter sp. WN18 is able to co-oxidize dioxane with THF as the primary substrate. Meanwhile, the THF-induced thmADBC gene cluster was responsible for the dioxane degradation rate indicating THF monooxygenase is the essential enzyme that initializing α-hydroxylation of THF and dioxane. Further, γ-butyrolactone and HEAA were characterized as the key metabolites of THF and dioxane, respectively. In addition, WN18 can tolerate the inhibition of trichloroethylene (5.0 mg/L) as a representative of co-existing leachate constituent, and sustain its activity at various pH (5−11), temperatures (15–42 °C), and salinities (up to 4%, as NaCl wt). Like other Arthrobacter species, WN18 also exhibited the capability of fixing nitrogen. All this evidence indicates the feasibility and advantage of WN18 as a thmADBC-catalyzed inoculator to bioremediate co-contamination of THF and dioxane.
Display omitted
•The Arthrobacter sp. WN18 was first proved can co-degrade dioxane.•THF-induced thm gene organization that initially cleaved dioxane was identified.•The possible degradation pathway of dioxane and THF were proposed.•WN18 has been shown to degrade dioxane and THF under multiple adverse conditions.
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the ...normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.
Introduction
The composition and structure of natural soil are very complex, leading to the difficult contact between hydrophobic organic compounds and degrading-bacteria in contaminated soil, making ...pollutants hard to be removed from the soil. Several researches have reported the bacterial migration in unsaturated soil mediated by fungal hyphae, but bacterial movement in soil of different particle sizes or in heterogeneous soil was unclear. The remediation of contaminated soil enhanced by hyphae still needs further research.
Methods
In this case, the migration and biodegradation of
Diaphorobacter
sp. LW2 in soil was investigated in presence of
Pythium ultimum
.
Results
Hyphae could promote the growth and migration of LW2 in culture medium. It was also confirmed that LW2 was able to migrate in the growth direction and against the growth direction along hyphae. Mediated by hyphae, motile strain LW2 translocated over 3 cm in soil with different particle size (CS1, 1.0–2.0 mm; CS2, 0.5–1.0mm; MS, 0.25–0.5 mm and FS, <0.25 mm), and it need shorter time in bigger particle soils. In inhomogeneous soil, hyphae participated in the distribution of introduced bacteria, and the total number of bacteria increased.
Pythium ultimum
enhanced the migration and survival of LW2 in soil, improving the bioremediation of polluted soil.
Discussion
The results of this study indicate that the mobilization of degrading bacteria mediated by
Pythium ultimum
in soil has great potential for application in bioremediation of contaminated soil.
The effect of structure on the vibration response was explored for four piano soundboards with different but commonly adopted structures. The vibration response was obtained using the free-vibration ...method, and the values of the dynamic modulus of elasticity and dynamic shear modulus obtained using the free-vibration frequency method (
and
) were compared with the dynamic modulus of elasticity obtained using the Euler beam method (
) and dynamic shear modulus obtained using the free-plate torsional vibration method (
), respectively. It was found that the soundboards with different structures had different vibration modes and that excitation at different locations highlighted different vibration modes. For all the soundboards analyzed, the
and
were higher than
and
by 2.2% and 24.3%, respectively. However, the trends of the results of these methods were the same. The four piano soundboards with different structures possessed varying dynamic moduli of elasticity and dynamic shear moduli. These rules are consistent with the grain directions of the soundboards and the anisotropy of the wood (the direction of the units of the soundboards). The results show that the vibration mode of the piano soundboard is complex. The dynamic elastic modulus of the soundboard can be calculated using the Euler beam method. The results provide a reference for studies on the vibration response, material selection, production technology, and testing of piano soundboards.
Background
The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as ...Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.
Objective
The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.
Methods
First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.
Results
Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.
Conclusion
These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.
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