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Multi-tooth milling cutters have the advantages of minimal surface damages and low cutting forces and are widely used in composite materials milling such as carbon fiber and wood. Cortical bone is a composite biomaterial similar to carbon fiber and wood; however, the cutting performance of multi-tooth milling cutters on bone cutting is unclear. Here, this paper proposes a multi-tooth milling cutter longitudinal torsional ultrasonic vibration assisted milling (LTVUM) method for cortical bone low damage cutting. The cutting performance of sinusoidal and corn edge cutters for cortical bone LTVUM was studied, and its material removal mechanisms were also established. The results indicated that the multi-tooth milling cutter has a smaller average cutting force, and suppressing the chip burrs and tearing damages. Compared to straight edge cutter, the resultant cutting forces of sinusoidal and corn edge cutters are decreased by 52.58%–61.01% and 45.60%−65.96%. The reason is that the secondary cutting edge of multi-tooth cutter can participate in cutting, reducing the tool squeeze interference. This work provides experimental guidance for the application of multi-tooth milling cutters LTVUM in bone cutting. [ABSTRACT FROM AUTHOR]

Under in-situ conditions, natural hydraulic fractures (NHF) can occur in permeable rock structures as a result of a rapid decrease of pore water accompanied by a local pressure regression. Obviously, these phenomena are of great interest for the geo-engineering community, as for instance in the framework of mining technologies. Compared to induced hydraulic fractures, NHF do not evolve under an increasing pore pressure resulting from pressing a fracking fluid in the underground but occur and evolve under local pore-pressure reductions resulting in tensile stresses in the rock material. The present contribution concerns the question under what quantitative circumstances NHF emerge and evolve. By this means, the novelty of this article results from the combination of numerical investigations based on the Theory of Porous Media with a tailored experimental protocol applied to saturated porous sandstone cylinders. The numerical investigations include both pre-existing and evolving fractures described by use of an embedded phase-field fracture model. Based on this procedure, representative mechanical and hydraulic loading scenarios are simulated that are in line with experimental investigations on low-permeable sandstone cylinders accomplished in the Porous Media Lab of the University of Stuttgart. The values of two parameters, the hydraulic conductivity of the sandstone and the critical energy release rate of the fracture model, have turned out essential for the occurrence of tensile fractures in the sandstone cores, where the latter is quantitatively estimated by a comparison of experimental and numerical results. This parameter can be taken as reference for further studies of in-situ NHF phenomena and experimental results. [ABSTRACT FROM AUTHOR]

Background/Objectives: Rheumatoid arthritis (RA) is a systemic chronic autoimmune disease characterized by a protracted course, high rates of morbidity, and disability yet lacks effective therapeutic modalities. Levamisole (LVM), an immunomodulatory drug, has been clinically reported for its potential in RA treatment, while its therapeutic mechanism toward RA remains to be elucidated. Hence, this study provides theoretical support for the application of LVM in the treatment of RA. Methods: This study employed male Sprague–Dawley (SD) rats to construct the adjuvant-induced arthritis (AIA) model, administering LVM orally (5 mg/kg, 15 mg/kg, and 45 mg/kg) for 25 days. An evaluation of LVM's therapeutic effects on RA was conducted through arthritis index scores, paw pad thickness, paw volume, hematoxylin and eosin (H&E) staining, 3D microcomputed tomography (micro-CT) scans, serum levels of pro-/anti-inflammatory cytokines, and serum biochemical indicators. Western blotting and immunohistochemistry staining were utilized to measure the expression levels of phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) proteins in synovial and ankle joint tissues. Results: Treatment with the median dose of LVM (15 mg/kg, M-LVM) significantly reduced the arthritis index (p < 0.01), paw pad thickness (p < 0.001), and paw volume (p < 0.01) without affecting body weight. Additionally, M-LVM alleviated inflammatory lesions in the synovium and ankle joints and also normalized serum levels of interleukin-1 beta (IL-1β), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-beta (TGF-β). The Model group exhibited significant increases in serum levels of alkaline phosphatase (ALP) (p < 0.01), creatine kinase (CK) (p < 0.05), and glucose (GLU) (p < 0.001) compared with the Control group; however, M-LVM effectively regulated these parameters to normal levels. Western blotting and immunohistochemistry staining revealed that PI3K-/Akt-related proteins were highly expressed in the synovial and ankle joint tissues of rats in the Model group, while treatment with M-LVM significantly reduced the expression of these proteins. Furthermore, histological examination of major organs (heart, liver, lungs, kidneys, and thymus) showed no significant pathological changes, with the exception of the spleen, where M-LVM ameliorated splenic lesions. Conclusions: We demonstrate that LVM at an optimal dose substantially relieves synovitis and bone erosion in AIA rats by inhibiting the PI3K/Akt signaling pathway. [ABSTRACT FROM AUTHOR]

Slow Transit Constipation (STC) is characterized by impaired colonic motility, but its relationship with gut microbiota remains unclear. This study investigated the correlation between specific gut microbial populations and STC, focusing on the Lactobacillus acidophilus to Lactobacillus johnsonii (A/J) ratio. We used four rat groups: Control (CON), Loperamide-induced STC (LOP), antibiotic-treated (ABX), and antibiotic plus Loperamide (ABX + LOP). Fecal samples were analyzed using 16S rRNA gene sequencing, and serum metabolites were examined through LC–MS. The LOP group showed an increased A/J ratio, while ABX and ABX + LOP groups had decreased ratios. Notably, the ABX + LOP group did not develop STC symptoms. Metabolomic analysis revealed alterations in key metabolites across groups, including changes in levels of guanidinoacetate, glycine, l-glutamine, nicotine, and nicotinate d-ribonucleotide in the LOP group, and variations in l-glutamine, l-phenylalanine, l-tyrosine, histamine, d-ornithine, and lecithin in the ABX and ABX + LOP groups. Our findings suggest a correlation between the A/J ratio and STC development, offering insights into STC pathophysiology and potential microbiome-targeted therapies. [ABSTRACT FROM AUTHOR]

Purpose: This study utilized biochar (BC) and carboxymethyl cellulose (CMC) modified nanoscale zero-valent iron (nZVI) to synthesize a novel composite material. The composite material was applied to the remediation of Cr(VI)-contaminated soil to evaluate its remediation performance and investigate the remediation mechanism. Materials and methods: The BC supported CMC stabilized nZVI composite (CMC-nZVI@BC) was prepared by the liquid-phase reduction method. The composites were analyzed and tested by SEM, XRD and FTIR characterization and Cr(VI) removal experiments in solution. The immobilization efficiency of the composites on Cr(VI) in soil was evaluated by testing the soil Cr(VI) content. The toxicity characteristic leaching procedure (TCLP) and simplified bioaccessibility extraction test (SBET) were used to assess the leachability and in vitro bioaccessibility of Cr in soil to humans. The changes in Cr speciation were evaluated by the BCR sequential extraction method. The changes in soil physicochemical properties and remediation mechanism of CMC-nZVI@BC were also investigated. Results and discussion: The results of characterization and batch experiments in solution showed that the nZVI in CMC-nZVI@BC was in the form of long chains, which were encapsulated by CMC and loaded on the BC surface. The agglomeration and surface passivation of the nanoparticles were significantly improved, and the modified nZVI exhibited better stability and reactivity. Soil remediation experiments showed that after adding 3 g/kg CMC-nZVI@BC to 100 g of contaminated soil, the immobilization efficiency of Cr(VI) in the soil reached 97.96% after 21 days of incubation, which was 1.88 times greater than that of the single nZVI system. This process significantly promoted the conversion of unstable acid soluble Cr to other stable forms. The TCLP and SBET leaching Cr concentrations decreased by 97.47% and 98.40%, respectively, indicating that CMC-nZVI@BC greatly reduced the leaching toxicity and bioavailability of Cr. In addition, the soil pH, CEC, SOM, and available iron content increased to different degrees after the application of CMC-nZVI@BC. The mechanisms of Cr(VI) stabilization in soil primarily included extraction, adsorption, reduction, and coprecipitation. Conclusion: In this study, CMC-nZVI@BC composites were successfully synthesized. Soil remediation experiments proved that CMC-nZVI@BC is an ideal material to remediate Cr(VI)-contaminated soil and provides a reference for future research. [ABSTRACT FROM AUTHOR]

As an environmentally friendly refrigeration system, the heat-driven Stirling refrigerator, which has demonstrated high efficiency and promising application prospects, is receiving significant attention for utilizing the waste heat to generate the cooling capacity. In this study, a heat-driven direct-coupled Stirling refrigerator, featuring an engine unit and a refrigeration unit directly coupled through a thermal buffer tube rather than utilizing a piston-based mechanism, is designed and tested. In comparison to the conventional heat-driven Stirling refrigeration systems, the proposed system exhibits the potential for significantly increased reliability and simplicity. Simulations and experiments were carried out to investigate the output characteristics of the system under different heating temperatures and mean pressures. The results show that higher heating temperature is beneficial for producing the acoustic power, thereby increasing the cooling capacity, with the mean pressure among 2.7–3.2 MPa. In the experiments, the system can provide a cooling capacity of 363W with a coefficient of performance of 0.17 when the heating, ambient, and cooling temperatures are 250, 35, and 7 °C, respectively. The results have validated the feasibility of the heat-driven direct-coupled Stirling refrigerator, which is a potential alternative for air-conditioning through waste-heat recovery. [ABSTRACT FROM AUTHOR]

Background: This study aimed to reveal the association between the gut microbiota (GM) and six diabetic complications: diabetic hypoglycemia; ketoacidosis; nephropathy; neuropathy; retinopathy; and Charcot's foot. Methods: GM data were obtained from the MiBioGen consortium and Dutch Microbiome Project while data on the six diabetic complications were obtained from the FinnGen consortium. Two-sample Mendelian randomization (TSMR) was performed to explore the association between GM and the common diabetic complications. Inverse MR analysis was conducted to examine the effect of diabetic complications on the identified GM. Sensitivity tests were conducted to validate the stability of the results. Finally, multivariate MR (MVMR) was performed to determine whether GM had a direct influence on the diabetic complications. Results: After multiple corrections, the inverse variance weighted (IVW) results predicted 61 suggestive markers between GM and six diabetic complications. In particular, the IVW results revealed that the Bacteroidia class and Bacteroidales order were positively associated with diabetic hypoglycemia while the Verrucomicrobiae class and Verrucomicrobiales order were positively associated with diabetic nephropathy. Based on the replication analysis, these results were identified to be stable. MVMR showed that the results remained stable after accounting for traditional risk factors. Conclusion: Extensive causal associations were found between GM and diabetic complications, which may provide new insights into the mechanisms of microbiome-mediated complications of diabetes. [ABSTRACT FROM AUTHOR]

The impact of SARS‐CoV‐2 infection shortly after vaccination on vaccine‐induced immunity is unknown, which is also one of the concerns for some vaccinees during the pandemic. Here, based on a cohort of individuals who encountered BA.5 infection within 8 days after receiving the fourth dose of a bivalent mRNA vaccine, preceded by three doses of inactivated vaccines, we show that booster mRNA vaccination provided 48% protection efficacy against symptomatic infections. At Day 7 postvaccination, the level of neutralizing antibodies (Nabs) against WT and BA.5 strains in the uninfected group trended higher than those in the symptomatic infection group. Moreover, there were greater variations in Nabs levels and a significant decrease in virus‐specific CD4+ T cell response observed in the symptomatic infection group. However, symptomatic BA.5 infection significantly increased Nab levels against XBB.1.9.1 and BA.5 (symptomatic > asymptomatic > uninfected group) at Day 10 and resulted in a more gradual decrease in Nabs against BA.5 compared to the uninfected group at Day 90. Our data suggest that BA.5 infection might hinder the early generation of Nabs and the recall of the CD4+ T cell response but strengthens the Nab and virus‐specific T cell response in the later phase. Our data confirmed that infection can enhance host immunity regardless of the short interval between vaccination and infection and alleviate concerns about infections shortly after vaccination, which provides valuable guidance for developing future vaccine administration strategies. [ABSTRACT FROM AUTHOR]

Environmental pollution caused by organic pollutants has been attracted more and more attention by the researchers. In this paper, ZnIn2S4 modified TiO2 nanotube array (ZnIn2S4/TiO2) composite electrodes are prepared by loading chalcogenide semiconductor material ZnIn2S4 on the surface of TiO2 nanotube array (TiO2 NTA) for the degradation of methylene blue (MB). The physicochemical properties of ZnIn2S4/TiO2 composite electrodes are analyzed by X-ray photoelectron spectroscopy, X-ray diffraction, photoluminescence spectroscopy and so on. The photoelectrocatalytic degradation rate of ZnIn2S4/TiO2 composite electrode with the concentration of ZnIn2S4 as 0.5 mmol/L (ZISTO-2) reaches 93.3% under the bias voltage of 0.7 V and simulated sunlight irradiation for 180 min against MB, which is 22.44% higher than that of pure TiO2 NTA electrode, and the degradation rate of ZISTO-2 composite electrode toward MB is maintained at 78.2% after five cycles. The radical trapping experiments indicate that h+, ·O2− and ·OH has significant contribution in the photoelectrocatalytic degradation. This paper provides a new ideal way to solve the problem of water pollution under visible light. [ABSTRACT FROM AUTHOR]

Abstractive summarization (AS) aims to generate more flexible and informative descriptions than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original article. To address this problem, some existing approaches attempt to evaluate or verify factual consistency, or design models to reduce factual errors. However, most of the efforts either have limited effects or result in lower rouge scores while reducing factual errors. In other words, it is challenging to promote factual consistency while maintaining the informativeness of generated summaries. Inspired by the knowledge graph embedding technique, in this paper, we propose a novel cross-modal knowledge guided model (CKGM) for AS, which embeds a multimodal knowledge graph (MKG) combining image entity-relationship information and textual factual information (FI) into BERT to accomplish cross-modal information interaction and knowledge expansion. The pre-training method obtains rich contextual semantic information, while the knowledge graph supplements the textual information. In addition, an entity memory embedding algorithm is further proposed to improve information fusion efficiency and model training speed. We elaborately conducted ablation experiments and evaluated our model on the Visual Genome, FewRel, MSCOCO, and CNN/DailyMail datasets. Experimental results demonstrate that our model can significantly improve the FI consistency and informativeness of generated summaries. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) technique has been widely used for content authentication. However, most conventional RDH schemes generally concentrates on the rate-and-distortion performance (namely high capacity and low distortion), instead of their undetectability performance (namely security). In other words, the stego images with conventional RDH schemes could be easily detected by most steganalysis tools, e.g., SPAM, to reveal some suspicious secret data hidden in it, which increases the embedding risk. Therefore, their applications in some circumstances are restricted. In this paper, enhanced images generated by common image enhancement techniques are built as a desired carrier for RDH, to achieve both better rate-and-distortion and desired undetectability performance due to its special statistical histogram distribution, where many vacant interspaces are scattered in the histogram and suitable for histogram shifting based RDH process. In addition, considering that the enhanced images could provide much more candidate spaces for embedding, some strategies for security improvement, such as multi-feature sorting technique and so on, could be introduced. Experimental results verify the superiority of proposed scheme compared with other related algorithms. [ABSTRACT FROM AUTHOR]

Background: The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. Methods: Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. Results: A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. Conclusions: PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC. [ABSTRACT FROM AUTHOR]

Levamisole (LVM) is considered an immunomodulatory agent that has the potential to treat various cancer and inflammation diseases. However, there is still much debate surrounding the toxicokinetic and toxicological information of LVM. Therefore, it is crucial to assess its toxicity to provide useful data for future human LVM risk assessments. In this study, a barrier environment was established under the guidance of good laboratory practice (GLP) at the Fujian Center for New Drug Safety Evaluation. Male beagle dogs were orally administered with 5, 15, and 30 mg/kg of LVM daily for four weeks. Toxicity assessment was based on various factors such as mortality, clinical signs, food and water consumption, body weight, body temperature, electrocardiogram, ophthalmological examination, hematology, serum biochemistry, organ/body coefficients, histopathological study, and toxicokinetic analysis. The results of this study showed that LVM did not exhibit any significant toxicological effects on beagle dogs at the exposure levels tested. A no observed adverse effect level (NOAEL) of LVM was set at 30 mg/kg/day for male beagle dogs, which is equivalent to a 12-fold clinical dose in humans. Moreover, the repeated exposure to LVM for four weeks did not lead to any bioaccumulation. These findings provide valuable insights for future human LVM risk assessments. [ABSTRACT FROM AUTHOR]

Eye diseases often affect human health. Accurate detection of the optic disc contour is one of the important steps in diagnosing and treating eye diseases. However, the structure of fundus images is complex, and the optic disc region is often disturbed by blood vessels. Considering that the optic disc is usually a saliency region in fundus images, we propose a weakly-supervised optic disc detection method based on the fully convolution neural network (FCN) combined with the weighted low-rank matrix recovery model (WLRR). Firstly, we extract the low-level features of the fundus image and cluster the pixels using the Simple Linear Iterative Clustering (SLIC) algorithm to generate the feature matrix. Secondly, the top-down semantic prior information provided by FCN and bottom-up background prior information of the optic disc region are used to jointly construct the prior information weighting matrix, which more accurately guides the decomposition of the feature matrix into a sparse matrix representing the optic disc and a low-rank matrix representing the background. Experimental results on the DRISHTI-GS dataset and IDRiD dataset show that our method can segment the optic disc region accurately, and its performance is better than existing weakly-supervised optic disc segmentation methods. Graphical abstract of optic disc segmentation [ABSTRACT FROM AUTHOR]

Objective: To evaluate the genetic causality between alcohol intake, smoking, coffee consumption, and arthritis. Methods: Mendelian randomization (MR) studies with alcohol, smoking, and coffee consumption behaviors as exposures, and osteoarthritis (OA) and rheumatoid arthritis (RA) as outcomes were retrieved from up to July 2023. Two researchers with relevant professional backgrounds independently assessed the quality and extracted data from the included studies. Meanwhile, we applied MR analyses of four lifestyle exposures and five arthritis outcomes (two for OA and three for RA) with gene-wide association study (GWAS) data that were different from the included studies, and the results were also included in the meta-analysis. Statistical analyses were performed using Stata 16.0 and R software version 4.3.1. Results: A total of 84 studies were assessed. Of these, 11 were selected for meta-analysis. As a whole, the included studies were considered to be at a low risk of bias and were of high quality. Results of the meta-analysis showed no significant genetic causality between alcohol intake and arthritis (odds ratio (OR): 1.02 (0.94–1.11)). Smoking and arthritis had a positive genetic causal association (OR: 1.44 (1.27–1.64)) with both OA (1.44 (1.22–1.71)) and RA (1.37 (1.26–1.50)). Coffee consumption and arthritis also had a positive genetic causal association (OR: 1.02 (1.01–1.03)). Results from the subgroup analysis showed a positive genetic causality between coffee consumption and both OA (OR: 1.02 (1.00–1.03)) and RA (OR: 1.56 (1.19–2.05)). Conclusion: There is positive genetic causality between smoking and coffee consumption and arthritis (OA and RA), while there is insufficient evidence for genetic causality between alcohol intake and arthritis. [ABSTRACT FROM AUTHOR]

The importance of physical activity (PA) to people's health has become a consensus around the world, and regular long-term PA has been accepted as an alternative preventive measure for many chronic medical conditions. Although the daily PA have several benefits for the public, the systematic research on its effect in human physiology, cognition and cerebral nerve level is not fully studied. Hence, in this study, we aim to investigate this question in several specific aspects: basal heart rate, executive function, and neural oscillatory activity in the brain. A total of 146 subjects participated in this study and they were divided into two groups. One group (SG) is the long-term training (more than 8 years) subjects in soccer (n = 31), and the other group (CG) is a normal control group (n = 115). The heart rate was monitored with a portable equipment. Besides, 24 subjects (14 in SG and 10 in CG) participated the Go/No-Go task and EEG recording before and after exercise fatigue task. In the physiology level, we found that in the non-training time, the heart rate in CG group is significantly higher than that of the SG group (P < 0.001). In the cognition level, we found that the SG group has a faster reaction time that that of CG group (P < 0.01), while for the accuracy, two groups did show significant difference. In the neural level in the brain, we found a significant abnormal increased beta-band (around 25 Hz) activity in CG group after the exercise fatigue task immediately. Long-term high-intensity physical activity reduces basal heart rate, improves executive function, and improve the central tolerance of the body under the stimulation of fatigue and stress. These benefits of long-term activity could be used as a manual to guide people's healthy life. [ABSTRACT FROM AUTHOR]

In application, training data and test data collected via indoor positioning algorithms usually do not come from the same ideal conditions. Changes in various environmental conditions and signal drift can cause different probability distributions between the data sets. Existing positioning algorithms cannot guarantee stable accuracy when facing these issues, resulting in dramatic reduction and the infeasibility of the positioning accuracy of indoor location algorithms. Considering these restrictions, domain adaptation technology in transfer learning has proven to be a promising solution in past research in terms of solving the inconsistent probability distribution problems. However, most localization algorithms based on transfer learning do not perform well because they only learn a shallow representation feature, which can only slightly reduce the domain discrepancy. Based on the deep network and its strong feature extraction ability, it can learn more transferable features for domain adaptation and achieve better domain adaptation effects. A Deep Joint Mean Distribution Adaptation Network (DJMDAN) is proposed to align the global domain and relevant subdomain distributions of activations in multiple domain-specific layers across domains to achieve domain adaptation. The test results demonstrate that the performance of the proposed method outperforms the comparison algorithm in indoor positioning applications. [ABSTRACT FROM AUTHOR]

In most pure metals and metallic systems, although the electronic thermal conductivity is believed to dominate thermal transport, the magnitude of lattice (phononic) thermal conductivity (LTC) is usually not negligibly low. We report two exceptional metallic materials, namely cubic half-Heusler-type PbAuGa and CsKNa, by solving the phonon Boltzmann transport equation (BTE) based on first-principles calculations. The two crystals possess record low LTCs of 0.064 W mK−1 and 0.031 W mK−1 at room temperature, respectively, among all pure metals and metallic systems we have known so far. Such LTCs, which are even comparable to that of air (about 0.025 W mK−1 under ambient conditions), only contribute 0.37% and 0.29% to the overall thermal transport. By quantitatively characterizing both phonon–phonon and phonon–electron interactions, it is demonstrated that the anomalously low LTC stems from low group velocity and strong anharmonicity which can be traced back to anti-bonding nature at the electronic level. The acoustic modes of PbAuGa and CsKNa originating from Au and Cs respectively dominate the thermal transport. By examining the mean square displacement, potential energy well, and crystal orbital Hamiltonian population, we find that the magnitude of movement of loosely bonded Au and Cs atoms in PbAuGa and CsKNa respectively is appreciable, and the Au and Cs atoms act as intrinsic rattlers and thus induce strong phonon anharmonicity and ultrashort lifetime reaching the Ioffe–Regel limit. This study deepens our understanding of heat conduction in metals and metallic systems and provides a route for searching for novel materials with nearly zero lattice thermal conductivity for future emerging applications. [ABSTRACT FROM AUTHOR]

The accumulated amount of nickel–iron slag has increased with the rapid development of the nickel-iron industry. To determine a method for comprehensively utilizing nickel–iron slag, triaxial compression tests of nickel–iron slag cement-based composite materials under the action of sodium sulfate were conducted, and the effects of the sodium sulfate concentration on the stress-strain relation, shear strength, cohesion, and internal friction angle of the composite materials were analyzed. In addition, the influence mechanism of the nickel–iron slag content and sodium sulfate concentration on the composite was examined. The results revealed that the stress–strain curve of the nickel–iron slag cement-based composites reflected softening. With the increase in the sodium sulfate concentration, the brittleness increased, while the shear strength, cohesion, and internal friction angle decreased; the addition of nickel–iron slag slowed down the rate at which these parameters decrease. Scanning electron microscopy images revealed that nickel–iron slag can improve the internal structure of the cement composite soil, enhance its compactness, and improve its corrosion resistance. The optimum nickel–iron slag content of 14% can improve the cementitious composites' resistance to sodium sulfate erosion in terms of solid waste utilization and cementitious soil performance. The results obtained can provide technical parameters for preparing and designing cement-based composite materials as well as certain theoretical significance and engineering reference value. [ABSTRACT FROM AUTHOR]

The stress of rock-like materials first increases and then decreases with an increase in the strain, and finally become damaged under tensile or compressive loads. It is not suitable to use the traditional bond-based peridynamics model to simulate the crack propagation of rock-like materials. Based on the bond-based peridynamics theory, a constitutive model has here been proposed that can reflect the characteristic of the stress with an increase in strain (i.e., that the stress of the rock-like materials first increases and then decreases, and finally fails). This makes up for the weakness with the traditional bond-based peridynamics theory, which fails to reflect the stress-strain change in rock-like materials. The strain energy density of the proposed constitutive model of rock-like materials has been derived and compared against the classical elasticity theory to obtain the model constants with respect to the elasticity moduli for plane stress conditions. Based on the here proposed constitutive model for rock-like materials, a numerical solution program for rock-like materials has been written using the Fortran language. For different loading conditions, the crack propagation process for an intact specimen has been simulated and compared with experimental results. This was also the situation for a specimen with a pre-existing flaw, a single non-straight flaw, and with three pre-existing flaws for plane stress conditions. The numerical simulation results were in good agreement with the corresponding experimental results. By using this proposed constitutive model, it was possible to simulate and predict the mechanical properties of rock-like materials, and the process of crack initiation, propagation, and coalescence under different loading conditions. The results from the present study can thus provide a reference for practical engineering. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients. [ABSTRACT FROM AUTHOR]

With the rapid development of the electric vehicle industry, the problem of electric vehicle mileage limitation still exists. Recent studies focus on the minimum energy consumption path planning method or the charging route planning method, with a lack of studies considering midway charging scenarios. In this study, we propose a graph processing method for the electric vehicle, given the energy consumption and road conditions, and establish a mixed integer planning model for the electric vehicle path planning problem. The objective is the shortest time, with energy consumption as a constraint, aiming at the problem of shortest path planning for electric vehicles with midway charging. Taking into account real-time traffic information and energy consumption information related to road conditions, a three-step heuristic algorithm based on preprocessing traffic network, charging path planning, and subpath planning is proposed for the electric vehicle path planning problem. The simulation results show that the proposed method can determine optimal paths including charging stations and effectively alleviate electric vehicles' "range anxiety" problem during medium- and long-distance travel. [ABSTRACT FROM AUTHOR]

Different devices in the smart home environment are subject to different levels of attack. Devices with lower attack frequencies confront difficulties in collecting attack data, which restricts the ability to train intrusion detection models. Therefore, this paper presents a novel method called EM-FEDE (enhancement method based on feature enhancement and data enhancement) to generate adequate training data for expanding few-shot datasets. Training intrusion detection models with an expanded dataset can enhance detection performance. Firstly, the EM-FEDE method adaptively extends the features by analyzing the historical intrusion detection records of smart homes, achieving format alignment of device data. Secondly, the EM-FEDE method performs data cleaning operations to reduce noise and redundancy and uses a random sampling mechanism to ensure the diversity of the few-shot data obtained by sampling. Finally, the processed sampling data is used as the input to the CWGAN, and the loss between the generated and real data is calculated using the Wasserstein distance. Based on this loss, the CWGAN is adjusted. Finally, the generator outputs effectively generated data. According to the experimental findings, the accuracy of J48, Random Forest, Bagging, PART, KStar, KNN, MLP, and CNN has been enhanced by 21.9%, 6.2%, 19.4%, 9.2%, 6.3%, 7%, 3.4%, and 5.9%, respectively, when compared to the original dataset, along with the optimal generation sample ratio of each algorithm. The experimental findings demonstrate the effectiveness of the EM-FEDE approach in completing sparse data. [ABSTRACT FROM AUTHOR]

Liquid sodium is an attractive working fluid for thermoacoustic conversion. Herein, a numerical study on a standing-wave thermoacoustic electricity generation system with liquid sodium as the working fluid is presented, based upon the Swift model. The characteristics of the thermoacoustic conversion and the output performance of the system have been investigated. The results show that the sodium engine can reach a power density much higher than the classical gas engine. Due to the strong acoustic coupling between components, the electricity output is significantly affected by the input heating power, the magnetic flux density, and the load ratio. In a typical case, the thermal-to-electric efficiency and the relative Carnot efficiency can reach 4.6% and 7.8%, respectively, with a temperature difference of 563 K and an input heat of 5 kW. More importantly, the output electricity density reaches 150 kW/m3, higher than some commercially available technologies. These results demonstrate the potential of such technology for small-scale electricity generation. Its extremely simple structure without any mechanical moving part endows the system with high reliability and long lifetime, if risks of corrosion and exposure to air and water can be avoided. [ABSTRACT FROM AUTHOR]

A high-temperature plasma sheath is generated on the surface of the re-entry vehicle through the conversion of kinetic energy to thermal and chemical energy across a strong shock wave at the hypersonic speed. Such a condition results in the forming of a blackout which significantly affects the communication components. To analyze the re-entry vehicle at the hypersonic speed, an unconditionally stable system incorporated factorization-splitting (SIFS) algorithm is proposed in finite-difference time-domain (FDTD) grids. The proposed algorithm shows advantages in the entire performance by simplifying the update implementation in multi-scale problems. The plasma sheath is analyzed based on the modified auxiliary difference equation (ADE) method according to the integer time step scheme in the unconditionally stable algorithm. Higher order perfectly matched layer (PML) formulation is modified to simulate open region problems. Numerical examples are carried out to demonstrate the performance of the algorithm from the aspects of target characteristics and antenna model. From resultants, it can be concluded that the proposed algorithm shows considerable accuracy, efficiency, and absorption during the simulation. Meanwhile, plasma sheath significantly affects the communication and detection of the re-entry vehicle. [ABSTRACT FROM AUTHOR]

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lacking consideration for potential driving risks. Therefore, this study proposes a deep-reinforcement-learning-based driving decision algorithm to address these problems. In this study, a non-deterministic vehicle driving risk assessment method is proposed for intersection scenarios and introduced into a learning-based intelligent driving decision algorithm. In addition, this study proposes an attention network based on state information. In this study, a typical intersection scenario was constructed using simulation software, and experiments were conducted. The experimental results show that the algorithm proposed in this paper can effectively derive a driving strategy with both driving efficiency and driving safety in the intersection driving scenario. It is also demonstrated that the attentional neural network designed in this study helps intelligent vehicles to perceive the surrounding environment more accurately, improves the performance of intelligent vehicles, as well as accelerates the convergence speed. [ABSTRACT FROM AUTHOR]

Microdamage in a rock induces a change in the rock's internal structure, affecting the stability and strength of the rock mass. To determine the influence of dissolution on the pore structure of rocks, the latest continuous flow microreaction technology was used, and a rock hydrodynamic pressure dissolution test device simulating multifactor coupling conditions was independently developed. The micromorphology characteristics of carbonate rock samples before and after dissolution were explored using computed tomography (CT) scanning. To conduct the dissolution test on 64 rock samples under 16 groups of working conditions, 4 rock samples under 4 groups were scanned by CT under working conditions, twice before and after corrosion. Subsequently, the changes in the dissolution effect and pore structure before and after dissolution were quantitatively compared and analyzed. The results show that the dissolution results were directly proportional to the flow rate, temperature, dissolution time, and hydrodynamic pressure. However, the dissolution results were inversely proportional to the pH value. The characterization of the pore structure changes before and after sample erosion is challenging. After erosion, the porosity, pore volume, and aperture of rock samples increased; however, the number of pores decreased. Under acidic conditions near the surface, carbonate rock microstructure changes can directly reflect structural failure characteristics. Consequently, heterogeneity, the presence of unstable minerals, and a large initial pore size result in the formation of large pores and a new pore system. This research provides the foundation and assistance for predicting the dissolution effect and evolution law of dissolved pores in carbonate rocks under multifactor coupling, offering a crucial guide for engineering design and construction in karst areas. [ABSTRACT FROM AUTHOR]

Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large‐scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state‐of‐the‐art methods and has a high practical application value. [ABSTRACT FROM AUTHOR]

Due to extreme operating conditions such as high-speed and heavy loads, ball screws are prone to damages, that affect the accuracy and operational safety of the mechanical equipment. As strong background noise and weak fault characteristics, it is difficult to capture the inherent fault state only depending on the time-domain or frequency-domain information from the vibration signal. In this paper, a fault diagnosis method for the ball screw based on continuous wavelet transform (CWT) and two-dimensional convolutional neural network (2DCNN) is proposed. The noise-reducing vibration signal is obtained via CWT. The time-frequency graph of the noise reduction signal can more comprehensively reflect the fault information of the ball screw. The time-frequency graph is used as the input to train and test the 2DCNN. Finally, diagnosis results of different types of faults reveal that the proposed CWT-2DCNN fault diagnosis method can achieve an average recognition rate of 99.67%. Compared with one-dimensional convolutional neural network (1DCNN) and traditional BP neural network, the proposed method has fast network convergence and high recognition accuracy. Time-frequency graphs of the noise-reduced signal used as fault features for classification can effectively avoid the problem of uncertainty due to the manual extraction of features. The proposed method has high application potential in the field of ball screw pair fault diagnosis. [ABSTRACT FROM AUTHOR]

Municipal sludge (MS) is used to prepare coal water slurry (CWS). This practice is beneficial for resource utilization and reduces treatment costs and environmental pollution. In this study, alkali-modified municipal sludge (AMS) was prepared with Ca(OH)2 as modifier and mixed with coal to produce AMS-CWS. The effect and mechanism of MS and AMS on slurry ability, stability, and combustion characteristics of the CWS were explored. The results of the pulping experiments showed that the slurry concentration of the AMS-CWS was approximately 10% greater than that of MS-CWS. The water separation rates of MS-CWS and AMS-CWS were 5% and 5.26%, respectively, which were 13.62% and 13.36% less than that of CWS (18.62%). The zeta potential experiments verified these results. Combustion performance research shows that both MS and AMS have positive effects on combustion performance. The results of the contact angle experiments showed that the hydrophobicity of AMS was enhanced, which was beneficial for improving the pulping capacity of MS-CWS. [ABSTRACT FROM AUTHOR]

A new type of waterborne acrylic resin was prepared by the solution polymerization with octafluoroamyl methacrylate (OF-PMA), 2-butoxy ethanol, and acrylic monomers as raw materials and N, N-dimethyl ethanolamine as pH regulator. Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy showed that the OF-PMA monomers were successfully grafted onto the resin. The effects of the addition mode and content of OF-PMA on the properties of waterborne acrylic resin were studied. The properties of waterborne acrylic polymer and its coating were tested by a thermogravimetric analyzer, an optical contact angle measuring instrument, and a tensile testing machine. The results showed that after the introduction of OF-PMA monomer into acrylic resin, the tensile strength of the resin was increased by 14.31 MPa. Moreover, the water resistance and heat resistance of resin coating were also improved obviously and the as-modified resin coating exhibited better fullness, adhesion of Gt0, and the hardness of 4H. The novel acrylic resin exhibits a potential application prospect in the fields of waterborne wood coatings. [ABSTRACT FROM AUTHOR]

Image super-resolution reconstruction is a research hotspot in the field of computer vision. Traditional image super-resolution reconstruction methods based on deep learning mostly up-sample low-resolution images ignoring categories and instances, which will cause some problems such as unrealistic texture in the reconstructed images or sawtooth phenomenon on the edge of instance. In this manuscript, we propose an image super-resolution reconstruction method based on instance spatial feature modulation and feedback mechanism. First, the prior knowledge of instance spatial features is introduced in the reconstruction process. Instance spatial features of low-resolution images are extracted to modulate super-resolution reconstruction features. Then, based on the feedback mechanism, the modulated low-resolution image features are iteratively optimized for the reconstruction results, so that the model can finally learn instance-level reconstruction ability. Experiments on COCO-2017 show that, compared with traditional deep learning-based image super-resolution reconstruction methods, the proposed method can obtain better image reconstruction results, and the reconstructed images have more realistic instance textures. [ABSTRACT FROM AUTHOR]

The effects of different SARS-CoV-2 vaccinations and variant infection histories on imprinting population immunity and their influence on emerging escape mutants remain unclear. We found that Omicron (BA.1) breakthrough infection, regardless of vaccination with two-dose mRNA vaccines (M-M-o) or two-dose inactivated vaccines (I-I-o), led to higher neutralizing antibody levels against different variants and stronger T-cell responses than Delta breakthrough infection after two-dose inactivated vaccine vaccination (I-I-δ). Furthermore, different vaccination-infection patterns imprinted virus-specific T-cell differentiation; M-M-ο showed higher S/M/N/E-specific CD4+ T cells and less portion of virus-specific CD45RA+CD27–CD8+ T cells by ex vivo assay. Breakthrough infection groups showed higher proliferation and multi-function capacity by in vitro assay than three-dose inactivated vaccine inoculated group (I-I-I). Thus, under wide vaccination coverage, the higher immunogenicity with the Omicron variant may have helped to eliminate the population of Delta variant. Overall, our data contribute to our understanding of immune imprinting in different sub-populations and may guide future vaccination programs. [ABSTRACT FROM AUTHOR]

Many results from cyclic triaxial experiments indicate that porous media, such as clays, exhibit various long-term behaviours under different cyclic stress ratios (CSRs). These can be classified into three main categories, namely, cyclic shakedown, cyclic stable and cyclic failure. Modelling these soil deformation responses, along with pore pressure and other fundamental cyclic aspects, such as closed hysteresis cycles and degradation, is still an open challenge, and research to date is limited. In order to properly describe and capture these characteristics, an enhanced plasticity model, based on the bounding surface and stress distance concepts, is developed here. In detail, a new uniform interpolation function of the plastic modulus, suitable for all loading stages, is proposed, and a new damage factor associated with the plastic shear strain and the deformation type parameter, is also incorporated into the plastic modulus. Accordingly, cyclic shakedown and cyclic failure can be distinguished, and degradation is achieved. Closed hysteresis loops, typical of clays, are obtained through a radial mapping rule along with a moving projection centre, located by the stress reversal points. Comparisons between the obtained numerical results and the experimental ones from literature confirm the suitability of the constitutive approach, which is capable of correctly capturing and reproducing the key aspects of clays' cyclic behaviour. [ABSTRACT FROM AUTHOR]

The latest continuous flow micro reaction technology was adopted to independently develop carbonate rock dissolution test equipment. Carbonate rock dissolution tests were conducted under different temperatures, flow rates, and dynamic water pressure conditions to study the dissolution process of carbonate rocks under the coupling of heat-water-chemistry. The dissolution effect and development law of carbonate rocks were explored by quantitatively studying carbonate rock dissolution rate and chemical composition of karst water. The results showed that the self-designed dissolution test equipment has obvious advantages. After dissolution, carbonate rock specimens were damaged to varying degrees. The dissolution rate was proportional to water velocity and hydrodynamic pressure, with the velocity effect being greater than the hydrodynamic pressure effect. The pH value, conductivity, and Ca2+ ion content of the reaction solution gradually increased after dissolution. The development and application of the equipment have proved that, at low dynamic water pressures (2 MPa), the water flow velocity effect on the dissolution velocity was 1.5 times that when the dynamic water pressure was high (6 MPa); at a low water flow velocity of 15 mL/min, the dynamic water pressure effect on the dissolution velocity was three times that when the water flow velocity was high (75 mL/min). The development process is gradually becoming strong and stable. Its research has important theoretical significance and engineering application value to provide technical means and guarantee for the early identification, karst development, and safety evaluation of karst geological disasters. [ABSTRACT FROM AUTHOR]

Since rolling bearings determine the stable operation of industrial equipment, it is necessary to diagnose thir faults. To improve fault diagnosis accuracy, this paper proposes a method based on a stacked sparse autoencoder (SSAE) combined with a softmax classifier. First, SSAE is used to extract the frequency-domain features of vibration signals. Then, an improved K-fold cross-validation is employed to obtain the features’ pre-train set, train set, and test set. Finally, the SSAE-model is constructed via the pre-train set, while the tuned model is built via the train set. The model performance is evaluated based on accuracy, macro-precision, macro-recall, and macro-F1 score. The proposed model is validated by the Case Western Reserve University and XJTU-SY data with 99.15% and 100% accuracy, respectively. [ABSTRACT FROM AUTHOR]

WSNs (Wireless Sensor Networks) are critical components of the Internet of Things (IoT). With the internationalization of the IoT and the widespread use of apps, it is crucial to increase WSNs localization algorithms' accuracy and their flexibility to dynamic and changing surroundings. To this end, it is proposed in this article a wireless sensor network location algorithm based on Manhattan distance (MDV-Hop) to solve many existing problems encountered in the wireless sensor network location algorithm. The MDV-Hop localization algorithm improves over present algorithms regarding frequency hopping, length, and least-squares of nodes to enhance the WSNs nodes' location accuracy and algorithm's adaptability in multi-variable environments. Manhattan distance has the characteristics of the sum of the projection distance of the line segment between two points on the coordinate axis in Euclidean space. The Manhattan distance measurement method is combined with Euclidean distance to determine the second minimum frequency hopping between beacon nodes, which substantially increases the DV-Hop algorithm's localization performance. On this foundation, the multi-objective genetics (NSGA-II) algorithm is employed to refine the outcomes of the least-squares approach to increase the suggested algorithm's localization accuracy, as it succeeds the simplicity and flexibility of the original DV-Hop method. Extensive simulations are performed in network scenarios with sparse and unevenly distributed anisotropic sensor nodes, and experimental results show that the MDV-Hop method outperforms the current WSNs node localization techniques in terms of performance and precision. [ABSTRACT FROM AUTHOR]

Neuroblastoma is the most malignant childhood tumor. The outcome of neuroblastoma is hard to predict due to the limitation of prognostic markers. In our study, we constructed a 16-miRNA prognostic model to predict the overall survival of neuroblastoma patients for early diagnosis. A total of 205 DE miRNAs were screened using RNA sequencing data from GSE121513. Lasso Cox regression analysis generated a 16-miRNA signature consisting of hsa-let-7c, hsa-miR-135a, hsa-miR-137, hsa-miR-146a, hsa-miR-149, hsa-miR-15a, hsa-miR-195, hsa-miR-197, hsa-miR-200c, hsa-miR-204, hsa-miR-302a, hsa-miR-331, hsa-miR-345, hsa-miR-383, hsa-miR-93, and hsa-miR-9star. The concordance index of multivariate Cox regression analysis was 0.9, and the area under the curve (AUC) values of 3-year and 5-year survival were 0.92 and 0.943, respectively. The mechanism was further investigated using the TCGA and GSE90689 datasets. Two miRNA–gene interaction networks were constructed among DEGs from two datasets. Functional analysis revealed that immune-related processes were involved in the initiation and metastasis of neuroblastoma. CIBERSORT and survival analysis suggested that lower CD8 T-cell proportion and higher SPTA1 expressions were related to a better prognosis. Our study demonstrated that the miRNA signature may be useful in prognosis prediction and management improvement. [ABSTRACT FROM AUTHOR]

Contour error compensation of the computer numerical control (CNC) machine tool is a vital technology that can improve machining accuracy and quality. To achieve this goal, the tracking error of a feeding axis, which is a dominant issue incurring the contour error, should be firstly modeled and then a proper compensation strategy should be determined. However, building the precise tracking error prediction model is a challenging task because of the nonlinear issues like backlash and friction involved in the feeding axis; besides, the optimal compensation parameter is also difficult to determine because it is sensitive to the machining tool path. In this paper, a set of novel approaches for contour error prediction and compensation is presented based on the technologies of deep learning and reinforcement learning. By utilizing the internal data of the CNC system, the tracking error of the feeding axis is modeled as a Nonlinear Auto-Regressive Long-Short-Term Memory (NAR-LSTM) network, considering all the nonlinear issues of the feeding axis. Given the contour error as calculated based on the predicted tracking error of each feeding axis, a compensation strategy is presented with its parameters identified efficiently by a Time-Series Deep Q-Network (TS-DQN) as designed in our work. To validate the feasibility and advantage of the proposed approaches, extensive experiments are conducted, testifying that our approaches can predict the tracking error and contour error with very good precision (better than about 99% and 90% respectively), and the contour error compensated based on the predicted results and our compensation strategy is significantly reduced (about 60~85% reduction) with the machining quality improved drastically (machining error reduced about 50%). [ABSTRACT FROM AUTHOR]

The establishment of a hyperacute rejection (HAR) model of ABO-incompatible kidney transplantation (ABOi-KTx) in nonhuman primates is of great significance for the study of the relevant clinical pathophysiological processes and related interventions in ABOi-KTx. In this study, blood group B cynomolgus monkeys were presensitized with synthetic blood group A-antigen conjugated to keyhole limpet hemocyanin (A-KLH) to boost circulating anti-A antibody levels. The serum anti-A antibody levels were measured by flow cytometry using type A human reagent red blood cells (RBCs) or monkey primary renal tubular epithelial cells (RTECs) as target cells. ABOi-KTx was performed in type B monkeys using type A monkeys as donors. After 14 days of A-KLH sensitization, 12 of 16 (75%) type B monkeys had significantly elevated anti-A antibody levels. We found that in order to avoid irregular results in the detection of blood group antibodies by flow cytometry, it was more effective to use RTECs rather than RBCs as target cells. In the absence of presensitization, ABOi-KTx in three monkeys with relatively high levels of natural anti-A antibodies did not produce HAR. However, when four Type B monkeys with significantly increased anti-A antibodies after presensitization were randomly selected as recipients for ABOi-KTx, the allografts in all four monkeys developed HAR with typical pathologic characteristics. Thus, we have successfully established a monkey model of HAR in ABOi-KTx via blood group antigen presensitization, which will be helpful for the further study of rejection, accommodation, and clinical intervention in ABOi-KTx. [ABSTRACT FROM AUTHOR]

The pyrolysis characters and fire behavior of ceiling materials have a great impact on the fire retardant of the whole bus because the fire might spread along the ceiling. This study investigated the pyrolysis characters of five plastic ceiling components with TG tests at four heating rates, and then, the kinetic parameters were calculated with Kissinger method, Friedman method and an advanced isoconversional Vyazovkin method. The obtained kinetic parameters were also validated by reconstruction of conversion curves. Finally, the flammability of each component from three ceiling materials was estimated using PCFC, and the fire behavior of three ceiling materials was investigated with cone calorimeter at different heat fluxes. Results show the pyrolysis characters of surface material could explain the early stage fire behavior of ceiling materials. The ignition and fire resistance were also highly dependent on the properties of surface materials. A transition between limited and more fierce combustion occurs at some heat flux for three ceiling materials. This property was related to the composition and structure of ceilings. Below this transition heat flux level, only the surface layer of ceiling was ignited and the fire load was limited. Above this level, the underlying layer was involved in combustion and leads to a sharply increased heat release. These results suggest that the flame retardant of surface material was important to promote fire retardant of the whole ceiling. Moreover, the obtained parameters can be used for predicting fire propagation in bus ceiling. [ABSTRACT FROM AUTHOR]

Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum. [ABSTRACT FROM AUTHOR]