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Federated computing power Internet of things (IoT) is designed to deeply integrate computing power with IoT resources, facilitating the efficient utilization of vast and ubiquitously dispersed IoT data and heterogeneous resources through federated learning. Faced with the threats of emerging privacy attacks, e.g., model inversion attacks and gradient leakage attacks, the academic and industrial communities have widely investigated and applied differential privacy (DP) as an effective privacy protection technique. However, two severe challenges have not been taken into account in the existing DP budget settings, i.e., data heterogeneity issue of local computing power nodes and the fairness of privacy budget allocation, which lead to a significant loss in model accuracy. Therefore, an adaptive optimization scheme for privacy budget was proposed in federated computing power IoT, which was called federated learning based on Cramér-Rao lower bound differential privacy (FedCDP). In specific, to adaptively adjust privacy budgets, the privacy budget estimates for edge computing power nodes based on the Cramér-Rao lower bound theory were analyzed. Furthermore, by assessing the similarity between the local model and the aggregated model, as well as their respective privacy budget proportions, the global contribution of each node was determined, which was used to fairly, also in real time, optimize and adjust the privacy budget settings in conjunction with the estimated privacy budget. Through rigorous theoretical analysis, FedCDP achieves ε-DP for local models, and ensures the convergence of the global model. Experimental results on multiple public datasets show that the proposed scheme improves the accuracy of the global model by up to 10.19% under the premise of satisfying the same privacy protection requirements.

Prior research has demonstrated significant deficits in sensory attenuation in patients with schizophrenia， which is a phenomenon that pertains to their inability to adequately modulate the intensity of sensory feedback associated with self-initiated voluntary actions. Sensory attenuation， a fundamental aspect of the sense of agency， is crucial for individuals to accurately perceive their own actions and effectively adapt to the external environment. Investigation on this phenomenon sheds light on the intricate mechanisms underlying sensory information processing deficits in patients with schizophrenia. This paper comprehensively examines the intricate relationship between abnormal sensory attenuation and the early identification of schizophrenia， delves into the underlying mechanisms that give rise to these abnormalities， and explores intervention strategies specifically designed to address them. Ultimately， our aim is to offer novel perspectives and insights that can contribute to the advancement of diagnosis and treatment of schizophrenia. ［Funded by Regional Project of National Natural Science Foundation of China （number， 32160202）］

In order to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context, this study designed a related task paradigm and applied a new generation magnetoencephalogram combined with the machine learning model to analyze the collected signals from three perspectives: behavioral response, event-related field (ERF) and single-trial detection. Results show that the unimodal semantic response was mainly concentrated in the occipital cortex, while the bimodal semantic response was mainly concentrated in the parietal cortex. At the same time, respondents' response rate and the detection accuracy of single-trial in bimodal mode were significantly higher than that in unimodal mode. Moreover, the support vector machine (SVM) showed the best classification performance among the four machine learning models, with an average classification accuracy of 75.16% for within-subject classification and 80.56% for between-subject classification. This research concludes that the combination of optically pumped magnetometer-magnetoencephalography (OPM-MEG) and machine learning model provides an efficient approach to decode the difference between audiovisual bimodal and unimodal responses of the human brain in semantic context.

Laser additive manufacturing （LAM） enables the integrated direct formation of high-performance metallic components with complex geometry. However， the non-uniform distribution of temperature gradients， in-situ heat treatment effects， non-equilibrium solid phase transformations， and heterogeneous microstructure during the process result in intricate residual stresses that significantly affect the quality and overall performance. A comprehensive review of residual stress in LAM by introducing its fundamental definition and classification is provided in this paper. Furthermore， the particularity， formation mechanisms， characterization methods， and regulation techniques associated with residual stress in LAM is systematically reviewed. The subsequent sections introduce various characterization methods for residual stress in LAM components， along with their respective characteristics. Additionally， the applicability， advantages， and disadvantages of each method are presented. Furthermore， the methods for controlling residual stress can be categorized into post-treatment control and in-situ treatment control. Finally， the joint effect of different types of residual stress should be considered in the study of the cause and evolution mechanism of residual stress in LAM. The characterization method of residual stress should be integrated with various methods to facilitate mutual verification. To mitigate the impact of stress concentration on the printing process and resultant quality， as well as minimize the influence of post-processing stress on part accuracy， it is imperative to investigate in-situ control methods for residual stress and explore real-time online monitoring techniques for LAM.

Natural zeolite is commonly used as an adsorbent for road dust suppression due to its excellent moisture absorption properties. However, during the formation process, natural zeolite tends to accumulate some zeolite water, organic impurities, and metal cations that occupy the pore channels and cavities, thereby blocking the passage of water molecules and hindering their adsorption. This study aims to improve the weak moisture absorption rate and capacity of dust suppression asphalt mixtures caused by clogged pores and impurities in natural zeolite. To achieve this different concentrations of salt solutions (NH4Cl, NaCl, MgCl2, and CaCl2) are used as modifiers for the treatment of natural zeolite. Scanning electron microscopy and Fourier transform infrared spectroscopy are used to study the interface and functional group changes between the modified zeolite substrate and asphalt mixture. The nitrogen adsorption/desorption curves of asphalt mixtures with different dosages were obtained using a nitrogen adsorption instrument. Based on this, parameters such as the specific surface area and adsorption pore volume of natural zeolite and modified zeolite asphalt mixtures were calculated using the BET and BJH methods. A macroscopic dust suppression test was further conducted to evaluate the dust suppression effect of the modified zeolite asphalt mixture on PM10. The results show that the moisture absorption rate and capacity of the modified zeolite are significantly improved. 20wt.%CaCl2 modification performs the best, and the moisture absorption capacity of the modified zeolite can reach up 10 times that of natural zeolite. Compared to the natural zeolite asphalt mixture, the specific surface area of the modified zeolite asphalt mixture is increased by 16.52%, and the adsorption pore volume is increased by 44.9%. The addition of modified zeolite powder significantly improves the dust suppression performance of the asphalt mixture. When the dosage of modified zeolite powder reaches 80%, the dust suppression effect reaches its peak, with an improvement of 16.34%.

Objective To observe the effects of combined application of argatroban and plasmin on the degree of neurological function deficit and neutrophil (NEUT) count in patients with acute cerebral infarction (ACI) and their safety. Methods A total of 221 patients with ACI admitted to The First Affiliated Hospital of Dalian Medical University from October 2018 to January 2022 were selected as the research objects, and they were randomly divided into four groups: control group (n=71), plasmin group (n=42), argatroban group (n=40), and combined group (n=68). All four groups were given conventional treatment, and no other treatment was added to the control group. The plasmin group was added with plasmin injection by intravenous drip, 100 IU on the first day of treatment, and 300 IU on the second day. The argatroban group was pumped intravenously with 10 mg of argatroban injection every 12 hours. The combined group was given argatroban injection and plasmin injection every day (the usage was the same as before). The changes of neurological function deficit and blood NEUT count before and after treatment and the adverse reactions during treatment were observed and recorded. Results After treatment, except for the control group, the National Institutes of Health Stroke Scale (NIHSS) scores of the other three groups were significantly lower than those before treatment (P＜0.01). The NIHSS score of the combined group was significantly lower than that of the control group, the plasmin group and the argatroban group, with the statistically significant differences (P＜0.05). After treatment, except for the control group, the blood NEUT count of the other three groups decreased compared to before treatment(P＜0.05). Blood NEUT count in the combined group was significantly lower than that in the control group, plasmin group, and argatroban group, with the statistically significant differences (P＜0.05). There was no obvious adverse reactions in the four groups. Conclusion The combination of argatroban and plasmin in the treatment of ACI can significantly reduce blood NEUT count, effectively improve neurological function deficit, with significant short-term efficacy and good safety.

Conventional high-field nuclear magnetic resonance (NMR) spectrometer necessitates advanced superconducting technology, which usually involves a magnet of large volume and high maintenance cost. Moreover, strong magnet often has severe magnetic field inhomogeneity, leading to NMR line broadening. Zero- to ultralow-field (ZULF) NMR emerged as a complementary approach to traditional high-field NMR techniques. In this paper, a portable zero-field NMR spectrometer based on a compact atomic magnetometer was developed, which adopted an integrated control system based on a multi-function data acquisition card (National Instruments PCIe-6353). The spectrometer is of a main magnetic field strength less than 1 nT and enables achieving high-resolution J-spectrum under zero-field environment. Specifically, a series of single pulse excitation experiments for the initial adiabatic state was carried out to calibrate the triaxial magnetic field coils. Then a modified composite pulse sequence was developed to further validate the performance of zero-field NMR spectrometer by single-spin control experiments for the 13C-1H spin system.

The test of new equipment is usually carried out in multiple batches according to the task schedule and test results. Constrained by the test environment, cost, and other factors, the amount of reliability test data in each batch is relatively limited, which brings difficulties to the accurate equipment reliability estimation work. For the reliability simulation tests conducted before each batch tests, it is particularly important to make full use of each batch tests information and simulation tests information to estimate the reliability of the equipment for small sample tests. This study takes the common normal-type life distribution equipment as the research object, and selects the normal-inverse gamma distribution as the equipment life parameters prior distribution based on the Bayesian method. Combined with the system contribution, the fusion weights of each batch tests information are determined and all the batch tests information is fused. Finally, the estimation of equipment reliability based on multiple batch tests is completed. The research results show that this method can integrate the information of each batch test and simulation test, overcome the problem of insufficient information of single batch tests, and provide an effective analytical tool for equipment reliability estimation.

Cable faults threaten the safe and stable operation of smart grids, and vibration signal diagnosis research on cables based on artificial intelligence technology can effectively enhance the reliability of smart grids. In order to improve the speed and accuracy of cable defect identification, this paper proposes a partial discharge identification method for cables based on a fully convolutional bidirectional long short-term memory neural network (BiLSTM-FCN). The time-domain characteristics of different working conditions under industrial frequency AC voltage are collected by building a vibration signal-based partial discharge test platform for cables. The Overlap strategy enhances the data set. The BiLSTM layer is introduced to process the one-dimensional time domain waveform signal, and combined with the local detail features extracted by the FCN layer, the data features are more abundant. Thus, a more accurate diagnosis of localized discharge in high voltage cables under different operating conditions. The results show that the diagnostic accuracy based on the BiLSTM-FCN model reaches 92.2%, which is 1.2% and 1.4% higher compared to the FCN model and the BiLSTM model. BiLSTM-FCN model possesses a better recognition effect and faster recognition speed in the identification of partial discharge defects type, which can effectively achieve the automatic detection of abnormal fault nodes of smart grid cables. It is significant for realizing online dynamic evaluation of small portable online monitoring equipment and provides a reference for future related research.

Intelligent manufacturing requires the full use of modern information technology in traditional production scenarios, and assembly line workshops also need intelligent industrial upgrading in production management. To this end, this paper proposes a time series-based evaluation study of the state of equipment in an assembly line workshop. Through the theoretical analysis of the characteristics of assembly line workshop equipment state data, time series data mining technology is used to collect research and analysis data, which is then pre-processed. The differential autoregressive average model is used to construct the assembly line workshop equipment state evaluation model, and the simulation analysis of the assembly line workshop equipment state is carried out by combining the corresponding parameters with the research data. The data shows that the MAPE value of the ARIMA model for equipment failure is −0.0706%, which is more significant compared to the prediction effect of the traditional CNN model. In addition, the error range of the ARIMA model prediction of equipment state parameters of the assembly line workshop is 0.01~0.02. This study can accurately assess the equipment state of the assembly line workshop and lay the foundation for its intelligent transformation.

Multiple studies have indicated that long non-coding RNAs are aberrantly expressed in cancers and are pivotal in developing various tumors. No studies have investigated the expression and function of long non-coding antisense RNA PCNA-AS1 in esophageal squamous cell carcinoma (ESCC). In this study, the expression of PCNA-AS1 was identified by qRT–PCR. Cell function assays were used to explore the potential effect of PCNA-AS1 on ESCC progression. A prediction website was utilized to discover the relationships among PCNA-AS1, miR-2467-3p and proliferating cell nuclear antigen (PCNA). Dual luciferase reporter gene and RNA immunoprecipitation (RIP) assays were executed to verify the binding activity between PCNA-AS1, miR-2467-3p and PCNA. As a result, PCNA-AS1 was highly expressed in ESCC and was associated with patient prognosis. PCNA-AS1 overexpression strongly contributed to ESCC cell proliferation, invasion and migration. PCNA-AS1 and PCNA were positively correlated in ESCC. Bioinformatics analysis, RIP and luciferase reporter gene assays revealed that PCNA-AS1 could act as a competitive endogenous RNA to sponge miR-2467-3p, thus upregulating PCNA. In conclusion, the current outcome demonstrates that PCNA-AS1 may be a star molecule in the treatment of ESCC.

In response to increasing ecological and environmental challenges in arid areas, it is of great significance to investigate the ecosystem service value (ESV), accompanying the changes in ecological sensitivity for the protection of ecologically vulnerable areas. Our analysis seeks to elucidate the ESV and ecological sensitivity changes in the middle and lower reaches of the Shiyang River to determine the trends and influencing factors of ESV under changing land use patterns. The key findings include: (1) From 1995 to 2020, the ESV in the study area witnessed fluctuations, culminating in an overall decline of 1.249 × 108 yuan. (2) In 2020, sensitivity coefficients (CSs) for ESV were as follows: 0.4335 for grassland, 0.2586 for farmland, and 0.1170 for unused land within the study area. Furthermore, coefficients of improved cross-sensitivity (CICSs) for the reciprocal transformation of farmland, grassland, and unused land were 1.10, 1.18, and 1.54, respectively, indicating the pivotal role of the three land types in driving ESV fluctuations.

Driven by the carbon peaking and carbon neutrality goals, Sichuan Province, with its abundant hydropower and renewable energy resources, possesses the feasibility to pioneer in constructing a provincial-level carbon-neutral power system in China. However, current grid planning lacks sufficient consideration for carbon emission related goals and constraints. To investigate the carbon emission evolution path of Sichuan Power Grid under the carbon neutrality goal, firstly, the distribution and development status of clean energy sources in Sichuan are clarified in this paper, and the low-carbon technology combination for Sichuan Power Grid's transition is determined. Then, load forecasting and other relevant factors are also combined to form boundary conditions for planning. Based on this, power source and grid planning research are carried out, exploring the two-stage evolutionary trajectory—transition period and formation period. Finally, employing the carbon emission flow theory, the carbon emissions at each phase are analysed, depicting the progressive realization of carbon neutrality in Sichuan Power Grid from the perspective of carbon emission flow.

Rainfall is the main influencing factor causing slope erosion, landslide, and instability in loess; thus, it is vital to comprehend the process of rainfall erosion on various slope surfaces and water penetration inside the slope. In this paper, the loess sample is from Heifangtai in Gansu Province, and triaxial shear tests were conducted on loess with roots under varying water contents to evaluate the slope-reinforcing impact of roots. The slope surface erosion process was analyzed using a soil moisture sensor and matric suction meter to monitor the variation of matric suction in the middle slope and slope foot in response to varying precipitation levels. The numerical simulation approach is utilized to analyze the fluctuation of slope stability under the effect of varying rainfall intensities and humid heat, and the analytical solution of the safety factor is compared to the model solution. The results indicate that the shortest generation time for bare slope runoff is 6 min, whereas the greatest generation time for the Bermuda grass slope is 12 min; the shorter the period, the less water penetration and the simpler it is to reach the slope erosion stage. The slope’s rise increases runoff velocity, strengthening water resistance on the slope surface. When the test slope is 30°, the maximum mass of scouring sediment on the bare slope is 15.2 g from 24 to 36 min, compared to 14.7 g from 24 to 36 min when the test slope is 60°. The amount of scouring reduces as the slope increases. The slope safety factor declined from 3.51 to 2.84 after 24 h of heavy rain, and the loss rate accelerated as the rainfall intensity increased.

The main paths of the knowledge diffusion network can reveal the important actors and diffusion process, which has an important significance in improving the efficiency of knowledge diffusion. Due to the independent path choice of project actors, knowledge diffusion networks show a dynamic characteristic in collaborative innovation projects. Taking into account this dynamic characteristic, dynamic main path analysis method for project context is proposed. The method, constructed by MATLAB simulation modeling, proposes calculation index and analysis strategies. Contrastive application of two main types of path analysis (the main path analysis method and dynamic main path analysis method) is carried out through a collaborative innovation project case, in order to verify the effectiveness and applicability of this method. The comparison results show that the main paths identified by the new method are more consistent with the actual main paths in knowledge diffusion practice, and the level of knowledge flow through main paths is higher. Therefore, our conclusion is that the dynamic main path analysis method proposed in this research has high applicability and accuracy for identifying the main paths in the collaborative innovation projects.

Summary: We demonstrate the successful implementation of a terahertz (THz) quantum-well photodetector (QWP) for effective signal collection in a scattering-type scanning near-field optical microscope (s-SNOM) system. The light source is an electrically pumped THz quantum cascade laser (QCL) at 4.2 THz, which spectrally matches with the peak photoresponse of THz QWP. The sensitive THz QWP has a low noise equivalent power (NEP) of about 1.1 pW/Hz0.5 and a spectral response range from 2 to 7 THz. The fast-responding capability of the THz QWP is vital for detecting the rapidly tip-modulated THz light which can effectively suppress the background noise. The THz images of the nanostructure demonstrate a spatial resolution of about 95 nm, corresponding to ∼λ/752 at 4.2 THz. We experimentally investigate and theoretically interpret the formation of the fringes which appear at the edge position of a gold stripe in the THz near-field image.

Natural loess slopes are characterized by a strong geological structure, which is an important factor in maintaining slope stability. The magnitude and duration of the earthquake may disturb the soil structure at different levels degrees, locally changing the arrangement between soil particles. The process of rainfall humidification weakens the cementation between soil particles, and the disturbance and humidification change the structural state of the soil, which in turn causes sliding of the slope along with the decay of soil mechanical properties. As slope instability is often the result of a series of post-earthquake ripple effects, it is of great scientific significance to study the mechanism of slope instability due to the structural decay of earthquake-damaged loess exacerbated by rainfall. In this paper, the impact of structural decay of loess on slope stability is simulated by GEOSTUDIO software under three conditions: pre-earthquake rainfall, post-earthquake rainfall and earthquake, taking the landslide in Buzi Village, Min County, Gansu Province as an example. The comparative analysis of the calculation results shows that the structural properties of the slope without earthquake disturbance are influenced by infiltration amount. When it is fully saturated, the structural properties are similar to those of saturated soil, and the safety factor is reduced by 12.9%. In addition, the earthquake intensity and duration have different degrees of structural damage to the soil. When the structure is fully damaged, it is similar to that of remodelled soil, and the safety factor is reduced by 45.84%. Notably, the process of the earthquake and the following humidification generates the most serious damage to the loess structure, with a reduction in the safety factor of up to 56.15%. The quantitative analysis above obviously illustrates that the post-earthquake rainfall causes the most severe damage to structural loess slopes, and the resulting landslide hazard should not be underestimated.

Objective To evaluate the effect of single-energy computed tomography (CT) combined with multi-artifact removal (MAR) for removing metal artifacts and optimizing blood vessel display in patients with coil embolization for intracranial aneurysms. Methods Thirty-two patients with intracranial aneurysms undergoing coil embolization under digital subtraction angiography (DSA) in our hospital between January and October in 2019 were enrolled in this study. After the surgery, the patients were scanned by gemstone spectral imaging (GSI) combined with MAR technique and post-processing with spectral monoenergy imaging technique. Two senior physicians scored the cranial vascular images acquired at a 10 KeV interval within 40 KeV to 140 KeV (the images acquired at 40 KeV and ≥100 KeV were excluded for poor quality). Nine single-energy images were reconstructed at 5 KeV intervals between 50 KeV and 90 KeV, and the noise level, signal-to-noise ratio (SNR) and artifact index (AI) of the images were evaluated. Results The SNR of the images tended to decrease with the increase of the energy level and showed significant differences between the energy levels 65 KeV and 70 KeV and between 75 KeV and 80 KeV (P < 0.05), but not between the other energy levels. The images acquired at 60~65 KeV had low AI indexes and the highest objective score for image quality with optimal display the blood vessels after coil embolization. Conclusion Energy spectral CT within the range of 60~65 KeV combined with MAR can effectively remove metal artifacts in patients after coil embolization for intracranial aneurysms.

The COVID-19 pandemic has had a profound psychological and behavioral impact on people around the world. Consumer purchase behaviors have thus changed greatly, and consumer services companies need to adjust their business models to adapt to this change. From the perspective of consumer psychology, this paper explores the impact of consumer purchase behavior changes over the course of the pandemic on the business model design of consumer services companies using a representative survey of 1,742 individuals. Our results show that changes in consumer purchase behavior have a significant impact on the design of consumer services firms’ business models. Specifically, changes in consumers’ purchase object, motive, and timeframe are more likely to spark a novelty-centered business model design, whereas changes in purchase method tend to inspire an efficiency-centered one. Our findings provide a theoretical reference for consumer services companies in designing business models when faced with unexpected crises.

Clinic therapy of acute myeloid leukemia (AML) remains unsatisfactory that urges for development of novel strategies. Recent studies identified ANP32A as a novel biomarker of unfavorable outcome of leukemia, which promoted leukemogenesis by increasing H3 acetylation and the expression of lipid metabolism genes. It is of great significance to investigate whether targeting ANP32A is a novel strategy for leukemia therapy. To target ANP32A, we identified a peptide that competed with ANP32A to bind to histone 3 (termed as H3-binding peptide, H3BP). Disrupting ANP32A and H3 interaction by the overexpression of H3BP-GFP fusion protein mimicked the effect of ANP32A knockdown, impaired H3 acetylation on multiple locus of target genes, reduced proliferation, and caused apoptosis in leukemia cells. Furthermore, a synthesized membrane-penetrating peptide TAT-H3BP effectively entered into leukemia cells and phenocopied such effect. In vivo, TAT-H3BP showed potent efficacy against leukemia: Intra-tumor injection of TAT-H3BP significantly reduced the volume of subcutaneous tumors in nude mice and recipient mice engrafted with TAT-H3BP-pretreated 6133/MPL W515L cells exhibited ameliorated leukemia burden and prolonged survival. Noticeably, TAT-H3BP efficiently suppressed proliferation and colony-forming unit of human primary AML cells without affecting normal cord blood cells. Our findings demonstrate that intervening the physical interaction of ANP32A with H3 impairs the oncogenicity of ANP32A and may be a promising therapeutic strategy against AML.

Long noncoding RNAs (lncRNAs) were identified recently as a large class of noncoding RNAs (ncRNAs) with a length ≥200 base pairs (bp). The function and mechanism of lncRNAs have been reported in a growing number of species and tissues. In contrast, the regulatory mechanism of lncRNAs in the goat reproductive system has rarely been reported. In the present study, we sequenced and analyzed the lncRNAs using bioinformatics to identify their expression profiles. As a result, 895 lncRNAs were predicted in the pre-ovulatory ovarian follicles of goats. Eighty-eight lncRNAs were differentially expressed in the Macheng black goat when compared with Boer goat. In addition, the lncRNA XR_311113.2 acted as a sponge of chi-miR-424-5p, as assessed via a luciferase activity assay. Taken together, our findings demonstrate that lncRNAs have potential effects in the ovarian follicles of goats and may represent a promising new research field to understand follicular development.

BackgroundTo compare the efficacy of lipid accumulation product (LAP) and urine glucose excretion (UGE) in predicting diabetes and evaluate whether the combination of LAP and UGE would help to improve the efficacy of using LAP alone or UGE alone in identifying diabetes.MethodsData from 7485 individuals without prior history of diabetes who participated in a cross-sectional survey in Jiangsu, China, were analyzed. Each participant underwent an oral glucose-tolerance test. Operating characteristic curves (ROC) and logistic regression analyses were used to evaluate the performance of LAP and UGE in identification of newly diagnosed diabetes (NDM) and prediabetes (PDM).ResultsFor subjects with NDM, the area under the ROC curve was 0.72 for LAP and 0.85 for UGE, whereas for PDM, these values were 0.62 and 0.61, respectively. Furthermore, LAP exhibited a comparable sensitivity with UGE in detecting NDM (76.4% vs 76.2%, p = 0.31). In predicting PDM, LAP showed a higher sensitivity than UGE (66.4% vs 42.8%, p < 0.05). The combination of LAP and UGE demonstrated a significantly higher sensitivity than that of LAP alone and UGE alone for identification of NDM (93.6%) and PDM (80.1%). Moreover, individuals with both high LAP and high UGE had significantly increased risk of NDM and PDM than those with both low LAP and low UGE.ConclusionsThe combination of LAP and UGE substantially improved the efficacy of using LAP and using UGE alone in detecting diabetes, and may be a novel approach for mass screening in the general population.

Significant research has been done in recent decades in the field of the dispersion of carbon nanotubes in aqueous solutions and the reinforcement of ordinary Portland cement (OPC). However, the cementitious mixture, as an alkaline environment, easily leads to the re-agglomeration of dispersed single-walled carbon nanotubes (SWCNTs) and influences their enhancing effects. Humic acid (HA) is a type of natural organic matter which can assist the stabilization of dispersed single-walled carbon nanotubes in cementitious composites. The present study characterizes the influence of HA in stabilizing the dispersion of SWCNTs by means of ultraviolet spectrophotometer tests. The fluidity of fresh cement slurry and mechanical performance of hardened OPC pastes were measured to better illustrate the dispersion of SWCNTs in real cement composites. The results not only reveal that the addition of an alkaline environment to the SWCNT suspensions results in a rapid decrease of the dispersion, but also suggest that the appropriate content of HA (0.12 wt.%) can play a significant role in stabilizing the dispersion of SWCNTs. When the mixed hybrid of SWCNTs and HA with a concentration of HA/c equal to 0.05 wt.% is used, the fluidity of the fresh cement slurry experiences a maximum decline and this mixture content of materials will dramatically increase the compressive and flexural strength by about 31% and 48%, which indicates that more SWCNTs are in a dispersed state under this concentration. SEM images further prove that a suitable HA/c can inhibit the expansion of cracks in the cementitious composites.

In order to smoothly implement gob-side entry retaining and mining without coal pillar in 3207 working face of Nanyang Coal Mine,Shanxi Province,this paper proposes to carry out advanced deep-hole blasting pre-splitting roof near gob side supporting wall.Through numerical simulation and field industrial test,the influence of deep-hole blasting roof cutting on surrounding rock stability of gob-side retaining roadway is studied.Based on the backdrop of retaining roadway along goaf in 3207 working face of Nanyang coal mine,a numerical simulation model was established,the displacement of roof and floor,wall and coal body of retained roadway,the stress distribution and variation of surrounding rock of retaining roadway were calculated.The numerical calculation shows that the variation of stress and displacement of surrounding rock in gob-side retaining roadway decreases greatly after roof cutting,and the stability of surrounding rock is improved significantly.The field industrial test results show that deep-hole blasting roof cutting can effectively eliminate the rotational extrusion damage of cantilever beam roof to surrounding rock of gob-side retaining roadway,and help to maintain the stability and integrity of gob-side retaining roadway structure.The research results of this paper can be used for reference in the design and construction of gob-side entry retaining project.

In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient.

Objective To study the changes of acid resistance, oxidation resistance and high osmotic pressure resis⁃ tance of Streptococcus mutans after knockout of rnc gene and its possible regulatory mechanism. Methods Through PCR ligation mutagenesis, an rnc knockout mutant (Smurnc) was constructed. Acid tolerance, oxidation tolerance and high osmotic pressure tolerance were compared between Smurnc and the wild strain respectively. Real⁃time RT⁃PCR was used to verify the changes in expression of stress tolerance related genes at the transcriptional level. Results When rnc gene was knocked out, the acid tolerance (c2=13.464, P=0.001) and oxidation tolerance (c2=4.505, P=0.048) of Streptococcus mutans was significantly decreased, but the high osmotic pressure tolerance was significantly increased (c2=11.971, P=0.001). Expression of stress tolerance related genes luxS and ropA (0.64 and 0.51 times expression of the wild strain) had been significantly downregulated (P