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In this paper, biodiesel was used to upgrade bio-oil by extracting the high-quality fuel fractions in bio-oil through solvent extraction. After the extraction, the upper layer blends of biodiesel and the high-quality bio-oil components, also called the "extraction phase liquid", was obtained and denoted as EPBB. Firstly, Thermogravimetric (TG) and Fourier transform infrared (FTIR) analyses indicated that the light components originally in bio-oil, such as alcohols, ethers, ketones and carboxylic acids were extracted from bio-oil to biodiesel and formed the EPBB. Then the fuel properties of EPBB were measured at different temperatures. The results showed that the density of EPBB was higher, while its viscosity, surface tension and lower heating value (LHV) were lower than those of biodiesel. Accordingly, EPBB revealed better atomization and evaporation characteristics as compared to biodiesel. Finally, the engine performance of EPBB was tested on an unmodified direct-injection diesel engine. When compared with biodiesel, the ignition delay of EPBB was longer, the premixed combustion duration was longer, while the total combustion duration was shorter. The fuel economy of EPBB was comparable to that of biodiesel, and the NOx, soot, CO and UHC emissions of EPBB were all lower than those of biodiesel. • The renewable extraction phase liquid of bio-oil/biodiesel blends (EPBB) is prepared. • EPBB is consisted of biodiesel and the best fuel fractions from bio-oil. • The fuel atomization and evaporation of EPBB are better than those of biodiesel. • EPBB has a comparable fuel economy and lower exhaust emissions than biodiesel. [ABSTRACT FROM AUTHOR]

• The proposed framework is proposed specifically for long narrow sampling windows. • The proposed framework is distribution-free and independent of sampled histograms. • The use of contained trace length is emphasized. • The proposed framework is applicable to curved-alignment sampling surfaces. • The fracture sampling strategy for long narrow sampling windows is suggested. Determining the fracture trace length distribution for a rock mass is an essential and difficult step in statistical fracture modeling. This requires the collection of representative mapping data, which for many large hydropower projects includes mapping in underground exploration tunnels resulting in long and narrow sampling windows. This limits the quality of the data collected using the long narrow sampling windows, and the traditional method for estimating true trace length distribution is challenged, especially for small samples. This paper presents a modified analysis framework for determining trace length distributions for long, narrow sampling windows. The proposed framework does not require the pre-selection of a distribution function and is independent of the sampled histogram. Instead, the distribution form is identified using L-moment ratio diagrams, and the corresponding distribution parameters are estimated using the L-moments. Two graphical tools and the average weighted orthogonal distance are introduced to assist in distribution selection on the L-moment ratio diagram. The multi-scanline method used to estimate the mean trace length is improved to make it applicable to curved tunnels (i.e., curved sampling surfaces). In addition, the proposed framework emphasizes the use of contained trace lengths to replace commonly used observed trace lengths for distribution inference and parameter estimation. Specifically, we assume that the true trace lengths have the same distribution form and coefficient of variation as the contained trace lengths. Guidance and recommendations are provided on the fracture sampling strategy. Validation of the proposed framework is performed by applying it to a synthetic data set with known characteristics. The application of the framework is demonstrated for a practical engineering case using mapping data collected from a hydropower project. The results show that the proposed framework is reliable and robust. [ABSTRACT FROM AUTHOR]

Robotic metamaterials represent an innovative approach to creating synthetic structures that combine desired material characteristics with embodied intelligence, blurring the boundaries between materials and machinery. Inspired by the functional qualities of biological skin, integrating tactile intelligence into these materials has gained significant interest for research and practical applications. This study introduces a Soft Robotic Metamaterial (SRM) design featuring omnidirectional adaptability and superior tactile sensing, combining vision-based motion tracking and machine learning. The study compares two sensory integration methods to a state-of-the-art motion tracking system and force/torque sensor baseline: an internal-vision design with high frame rates and an external-vision design offering cost-effectiveness. The results demonstrate the internal-vision SRM design achieving an impressive tactile accuracy of 98.96%, enabling soft and adaptive tactile interactions, especially beneficial for dexterous robotic grasping. The external-vision design offers similar performance at a reduced cost and can be adapted for portability, enhancing material science education and robotic learning. This research significantly advances tactile sensing using vision-based motion tracking in soft robotic metamaterials, and the open-source availability on GitHub fosters collaboration and further exploration of this innovative technology (https://github.com/bionicdl-sustech/SoftRoboticTongs). • Introducing Soft Robotic Metamaterials (SRM) with vision-based tactile sensing using machine learning. • Proposed and compared two sensory integration methods: internal-vision and external-vision designs. • Impressive tactile accuracy of 98.96% achieved by internal-vision SRM design. • Cost-effective and portable external-vision SRM design for shareable and reproducible robotic learning. • Demonstrated application for robotic teleoperation and the creation of portable teaching tools for robot learning. [ABSTRACT FROM AUTHOR]

• EA could alleviate EAE mice model. • EA could correct CD4+ T cell subsets imbalance. • MiR-155 mimic reversed the therapeutic effect of EA on EAE. • MiR-155 mimic could inhibit the expression of POMC in vivo and vitro. • EA alleviated the pathogenesis of EAE by inhibiting miR-155 and promoting POMC. Multiple sclerosis (MS) is a neurodegenerative and demyelinating autoimmune disease mediated by autoreactive T cells that affects the central nervous system (CNS). Electroacupuncture (EA) has emerged as an alternative or supplemental treatment for MS, but the mechanism by which EA may alleviate MS symptoms is unresolved. Here, we examined the effects of EA at the Zusanli (ST36) acupoint on mice with experimental autoimmune encephalomyelitis (EAE), the predominant animal model of MS. The effects of EA on EAE emergence, inflammatory cell levels, proinflammatory cytokines, and spinal cord pathology were examined. EA treatment attenuated the EAE clinical score and associated spinal cord demyelination, while reducing the presence of proinflammatory cytokines in mononuclear cells (MNCs), downregulating microRNA (miR)-155, and upregulating the opioid peptide precursor proopiomelanocortin (POMC) in the CNS. Experiments in which cultured neurons were transfected with a miR-155 mimic or a miR-155 inhibitor further showed that the direct modulation of miR-155 levels could regulate POMC levels in neurons. In conclusion, the alleviation of EAE by EA is characterized by reduced proportions of Th1/Th17 cells and increased proportions of Th2 cells, POMC upregulation, and miR-155 downregulation, while miR-155 itself can suppress POMC expression. These results, support the hypothesis that the effects of EA on EAE may involve the downregulation of miR-155. [ABSTRACT FROM AUTHOR]

Highlights • Up to 92% of ball-milled chitin was liquefied by liquefaction technique. • Waste shrimp shells were directly liquefied and first used to modify the polymer materials. • LBMC and LBMS obviously enhanced the mechanical property, thermal stability and antibacterial activity of blend membranes. • The properties of LBMS/PVA blend membranes were comparable to those of LBMC/PVA blend membranes. Abstract Ball-milled chitin was liquefied with an optimal yield of 92% under sulfuric acid in diethylene glycol (DEG) at 160 °C for 120 min. The resulting liquid mixture was roughly separated into two portions: the real products of the reaction (liquefied ball-milled chitin, LBMC) and the remaining unreacted DEG. LBMC was further mingled with polyvinyl alcohol (PVA) to prepare LBMC/PVA blend membranes. To promote the direct utilization of shellfishery waste, raw shrimp shells were used to replace chitin for the liquefaction and membrane preparation operations. Liquefied ball-milled shrimp shells (LBMS) and the corresponding LBMS/PVA blend membranes were obtained. After adding LBMC or LBMS, the mechanical, thermal, water content and antibacterial performance of blend membranes were significantly improved compared to pure PVA membrane. Surprisingly, all the measured properties of LBMC/PVA and LBMS/PVA blend membranes were comparable, and even some properties of the latter were slightly superior than those of the former. [ABSTRACT FROM AUTHOR]

Background: Studies on environmental exposures during pregnancy commonly use maternal residence at time of delivery, which may result in exposure misclassification and biased estimates of exposure and disease association. Studies on residential mobility during pregnancy are needed in various populations to aid studies of the environmental exposure and birth outcomes. However, there is still a lack of studies investigating residential mobility patterns in Asian populations.Methods: We analyzed data from 10,542 pregnant women enrolled in a birth cohort study in Lanzhou, China (2010-2012), a major industrial city. Multivariate logistic regression was used to evaluate residential mobility patterns in relation to maternal complications and birth outcomes.Results: Of the participants, 546 (5.2%) moved during pregnancy; among those who moved, 40.5%, 34.8%, and 24.7% moved during the first, second, and third trimester, respectively. Most movers (97.3%) moved once with a mean distance of 3.75 km (range: 1-109 km). More than half (66.1%) of the movers moved within 3 km, 13.9% moved 3-10 km, and 20.0% moved > 10 km. Pregnant women who were > 30 years or multiparous, or who had maternal complications were less likely to have moved during pregnancy. In addition, movers were less likely to deliver infants with birth defects, preterm births, and low birth weight.Conclusions: Residential mobility was significantly associated with several maternal characteristics and complications during pregnancy. The study also showed a lower likelihood of adverse birth outcomes among movers than non-movers, suggesting that moving might be related to reduce exposure to environmental hazards. These results confirm the hypothesis that residential mobility may be important with respect to exposure misclassification and that this misclassification may vary by subpopulations. [ABSTRACT FROM AUTHOR]

Fracture intensity, the degree of fracturing in rock masses, is one of the fundamental parameters used in characterizing rock mass as a fracture network. Among fracture intensity measures, volumetric fracture intensity (P 32 ) is the most useful, since it directly reflects the degree of rock mass fracturing, independent of fracture orientation and size distribution. P 32 represents the total area of fractures per cubic meter, and it can simultaneously reflect the spacing and size component of fractures. Because P 32 cannot be directly measured in the field, previous works have developed various inference methods based on available 1D and 2D measures collected from field surveys. At present, discrete fracture network (DFN) models are deemed to be a suitable method for the characterization of fractured rock mass. Hence, calculating P 32 from a DFN model is also a widely accepted approach. However, the geometry-based method cannot effectively address the irregular simulation space, and the methods employed are ill-suited if polygonal fractures appear in the DFN model. To solve these deficiencies, a computational framework based on the Delaunay triangulation of 3D uniformly distributed random points is introduced, and a detailed description is presented. The proposed computational framework is suitable for various types of DFN models, such as Baecher disk models, elliptical disk models and Dershowitz polygon models. A simulated example was generated to verify the proposed computational framework. Finally, a DFN model is built based on fracture data acquired from the Songta hydropower station, and the proposed computational framework was used to calculate P 32 for the DFN model. [ABSTRACT FROM AUTHOR]

The harmonic currents injected by high speed electric trains cause not only the pollution of power quality (PQ), but can also excite harmonic resonance in the traction power supply system (TPSS) of high speed railway (HSR). It is essential to build a proper harmonic distribution and prediction model for high speed electric trains. This paper presents an approach to obtain the probability density of harmonic current based on a non-parametric Kernel density estimation method, which can describe the probability distribution characteristics using a data-driven mechanism. A prediction algorithm for harmonic current based on confidence intervals, which can simulate harmonic current injection using a continuous sampling method, is presented in this paper. The new high speed CRH380AL electric multiple units (EMU) are used to illustrate the method. The results show that the proposed approach and algorithm can be useful for harmonic source simulation in harmonic power flow calculation and PQ assessment. [ABSTRACT FROM AUTHOR]

A stepwise algorithm for determining the intersections between fractures is presented. The stepwise approach is based on the bounding volume technology and separating axis theorem. The length and orientation distribution of the fracture intersections can be obtained by the stepwise approach. The stepwise approach is used to study the connectivity of the rock mass at Songta hydropower station. According to the traces data acquired from a tunnel namely PD231, a DFN model was generated for intersection analysis and connectivity analysis. The results of intersection analysis show that the stepwise approach is effective. Connectivity parameters such as the average number of fracture intersections per fracture, and the length of intersection lines per unit volume, can be calculated. The mean length of intersections is 1.184 m and the variance is 0.622 m. The self-intersection behavior and direction vector of the intersections are studied. The simulations show that the sub-vertical joint set 3 and the shallow joint set 1 constitute the main flow pathways in the fractured rock at dam site. Besides, the results suggest that the sub-vertical joint sets (joint set 2 and 3) are more connected than the shallow joint set (joint set 1). [ABSTRACT FROM AUTHOR]

Importance Exposure to ambient particulate matter during pregnancy has been suggested as a risk factor for preterm birth. However results from limited epidemiologic studies have been inconclusive. Very few studies have been conducted in areas with high air pollution levels. Objective We investigated the hypothesis that high level exposure to particulate matter with aerodynamic diameter no larger than 10 μm (PM 10 ) during pregnancy increases the risk of preterm birth. Methods A birth cohort study was carried out between 2010 and 2012 in Lanzhou, China, including 8969 singleton live births with available information on daily PM 10 levels from four monitoring stations, individual exposures during pregnancy were calculated using inverse-distance weighting based on both home and work addresses. Unconditional logistic regression modeling was used to examine the associations between PM 10 exposure and risk of preterm birth and its clinical subtypes. Results Increased risk of very preterm birth was associated with exposure to PM 10 during the last two months of pregnancy (OR, 1.07; 95%CI, 1.02–1.13 per 10 μg/m 3 increase for last four weeks before delivery; 1.09; 1.02–1.15 for last six weeks before delivery; 1.10; 1.03–1.17 for last eight weeks before delivery). Compared to the U.S. National Ambient Air Quality Standard (150 μg/m 3 ), higher exposure level (≥ 150 μg/m 3 ) of PM 10 during entire pregnancy was associated with an increased risk of preterm birth (1.48; 1.22–1.81) and the association was higher for medically indicated preterm birth (1.80, 1.24–2.62) during entire pregnancy and for very preterm during last 6 weeks before delivery (2.03, 1.11–3.72). Conclusions and relevance Our study supports the hypothesis that exposure to high levels of ambient PM 10 increases the risk of preterm birth. Our study also suggests that the risk may vary by clinical subtypes of preterm birth and exposure time windows. Our findings are relevant for health policy makers from China and other regions with high levels of air pollution to facilitate the efforts of reducing air pollution level in order to protect public health. [ABSTRACT FROM AUTHOR]

Metformin, the most widely used medicine for type 2 diabetes, displays anti-inflammatory functions via activating AMP-activated protein kinase (AMPK). Circulating autoantibodies and disequilibrium of helper T cells and regulatory T cells are pathological hallmarks of myasthenia gravis (MG). Rectify the imbalance of different T cell populations has become an important therapeutic strategy to treat MG. In this study, we assessed the effect of metformin on the development of autoimmunity using an experimental autoimmune myasthenia gravis (EAMG) rat model. We first provided evidence that oral administration of metformin attenuated the onset of EAMG. This effect was accompanied by a substantial decrease of circulating auto-antibody levels with no effect on blood glucose level. While metformin treatment in vitro showed little effect on inducible Treg, metformin strongly inhibited Th17 cell differentiation through the increase of reactive oxygen species and AMPK. Furthermore, an attenuation of antigen-induced IgG2b antibody production by two different doses of metformin was also observed in the AChR-specific recall response. In conclusion, the above results indicate that metformin may have therapeutic value for the clinical treatment of MG. • Metformin attenuates the onset of experimental autoimmune myasthenia gravis. • Metformin inhibits Th17 cells differentiation without hindering inducible Treg (iTreg) differentiation via AMPK pathway. • Metformin inhibits anti-AChR antibody secretion by B cells. [ABSTRACT FROM AUTHOR]