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Abstract Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in the automatic classification of heart sounds, but most methods are based on segmentation features and traditional classifiers and do not fully exploit existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time domain, frequency domain and statistical domain. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional space are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2D coordinate representations. Finally, the one-dimensional feature component value and its divergence into categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is sent to a deep network such as ResNet50 for classification. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that for the two-classification/five-classification task of heart sounds, the method in this paper can achieve a classification accuracy of 95.68%/94.53% when combined with the current deep network.

Objective The diverse and complex operating environment of axle box bearings makes it challenging for bearing fault diagnostic methods to achieve satisfactory results with the single sensor. To address this issue, research is conducted on train bearing fault diagnosis based on convolutional temporal-spatial mutual fusion network. Method A multi-sensor fusion approach is adopted, applying the proposed CTS-MFN (a convolutional temporal-spatial mutual fusion network) for bearing fault diagnosis upon horizontal and vertical vibration datasets. The ECA module (efficient channel attention), LSTM (long short-term memory network), and similarity distance constraints are introduced to the convolutional autoencoder, enabling the model to extract temporal-spatial attention features that include inter-modal interaction information. An MLP (multilayer perceptron) is then used to fuse and infer temporal-spatial features from each modality. Result & Conclusion Comparative experiments, ablation studies, and generalization performance analysis demonstrate the effectiveness of the proposed model.

Abstract In this work, the sharp-interface immersed boundary (IB) method proposed by Mittal et al. (J Comput Phys 227(10):4825–4852, 2008) is extended to fluid-structure-interaction (FSI) simulation of parachute inflation by utilizing several open-source tools. The method employs a Cartesian-grid ghost-cell methodology to accurately represent the immersed boundary, and it is suitable for solving moving-boundary flows with arbitrarily complex geometries. The finite-element code CalculiX is employed to solve the structural dynamics of the parachute system. The IB flow solver is coupled with CalculiX in a minimally-invasive manner using the multi-physics coupling library preCICE. The implicit fluid-structure coupling together with the Aitken adaptive under-relaxation scheme is considered to improve the numerical accuracy and stability. The developed approach is validated by a benchmark FSI case. Numerical experiments on the inflation process of several typical parachutes are further conducted. The breathing process, flow structure, canopy displacement and drag coefficient are analyzed to demonstrate the applicability of the present approach for simulating parachute inflation.

Background With the incidence of chronic metabolic diseases rising by year, which has threatened the national health, the study of non-coding RNA and endocrine metabolism-related diseases has become a research hotspot at home and abroad, while lncRNA NEAT1 and miRNA-182-5p in type 2 diabetes mellitus (T2DM) combined with metabolic-related fatty liver disease (MAFLD) has been rarely reported. Objective To investigate the mechanism and clinical significance of lncRNA NEAT1 and miRNA-182-5p in the development of liver fibrosis in T2DM patients with MAFLD. Methods A total of 236 T2DM patients admitted to the endocrinology department of the First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology from October 2021 to June 2022 were included as the study subjects, and 49 healthy people were included as the healthy control group. General information and laboratory test results of the subjects were collected. Visceral fat area (VFA) and subcutaneous fat area (SFA) were measured. Peripheral blood was collected and lncRNA NEAT1, miRNA-182-5p were determined. T2DM patients were divided into the T2DM with non-MAFLD group (n=82) and T2DM with MAFLD group (n=154). T2DM with MAFLD group was further divided into the low-risk subgroup (n=55), medium-risk subgroup (n=69) and high-risk subgroup (n=30) according to the liver fibrosis index (FIB-4). In addition, healthy people were selected as the healthy control group (n=49). Spearman rank correlation analysis was used to explore the correlation of lncRNA NEAT1 and miRNA-182-5p expression levels in the high-risk subgroup of liver fibrosis, and multilevel ordinal Logistic regression was used to explore the influencing factors of liver fibrosis risk in T2DM patients with MAFLD. Results Age, neck circumference (NC), fasting blood glucose (FPG) and glycosylated hemoglobin (HbA1c) in the healthy control group were lower than those in the T2DM with non-MAFLD and T2DM with MAFLD groups, the albumin (Alb) in the healthy control group was higher than that in the T2DM with non-MAFLD and T2DM with MAFLD groups (P

Many parameters and complex boundaries are involved in the spatial arrangement of an underground powerhouse in a hydropower station, necessitating referencing many relevant cases and specifications. However, in practical applications, retrieving such cases or specifications is difficult, and there is a lack of knowledge regarding cascading logic among design parameters. To address this issue, this study proposes a novel methodology for constructing a targeted knowledge graph, in this case, a knowledge graph for building information modeling (BIM) for underground powerhouses in hydropower plants. Subsequently, based on this knowledge graph, this study develops a question-and-answer (Q&A) system to facilitate subsequent applications. First, the ontology skeleton of the spatial arrangement design of an underground powerhouse in a hydropower station, that represents the knowledge organization structure of the knowledge graph, is constructed by carefully analyzing the requirements for intelligent modeling of underground powerhouses. A large volume of unstructured data is identified based on the optical character recognition (OCR) technology; the collected data are divided into words to extract correlation knowledge using THU Lexical Analyzer for Chinese (THULAC). Subsequently, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in Neo4j, a knowledge base, to build a knowledge graph. Finally, the knowledge graph is employed to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of the spatial arrangement of an underground powerhouse in a pumped storage hydropower station.

Eye-tracking technology brings a different human-computer interaction experience to users because of its intuitive, natural, and hands-free operation characteristics. Avoiding the Midas touch problem and improving the accuracy of interaction are among the main goals of the research and development of eye-control systems. This study reviews the methods and limitations of research on avoiding the Midas touch problem. For typical control clicking operations with low fault tolerance, such as mode switching and state selection in an eye-control system, this study proposes Magilock, a more reliable control triggering method with a high success rate in multi-channel eye-control systems. Magilock adds a control pre-locked mechanism between the two interactive steps of eye-control channel positioning control and other interactive channel triggering controls in the multi-channel eye-control system. This effectively avoids incorrect control triggering caused by multi-channel coordination disorder and gaze-point drift. This study also conducted ergonomic experiments to explore the lock and unlock times of the control pre-locked mechanism in Magilock. Taking into account the experimental data and subjective evaluation of the participants, we recommend setting the lock time and the unlock time of Magilock to 200 ms.

ObjectiveTo systematically evaluate the safety of interferon β-1a for treatment of corona virus disease 2019 （COVID-19）， and to provide references for interferon β-1a's clinical application.MethodsThis study was conducted with the database from US Food and Drug Administration adverse event reporting system （FAERS） from January 1， 2015 to March 31， 2021. Information component （IC） and reporting odds ratio （ROR） methods were applied for signal mining.ResultsA total of 463 700 records of COVID-19 were selected for analysis， and 45 positive drug adverse event signals were detected. Headache （IC025=1.09， ROR025=2.28）， pyrexia （IC025=0.51， ROR025=1.51） and multiple sclerosis relapse （IC025=3.67， ROR025=14.71） were positive adverse events with higher frequency. Autoimmune disorder （IC025=4.42， ROR025=24.03）， streptococcal infection （IC025=4.12， ROR025=19.82）， and multiple sclerosis relapse （IC025=3.67， ROR025=14.71） were positive adverse events. Acute lung injury， cardio-respiratory arrest and metabolic acidosis were associated with a higher proportion and frequency of death.ConclusionThere are certain safety issues with interferon β-1a in the treatment of COVID-19， and some adverse events with high frequency and high death rate deserve further attention by medical staffs.

Abstract Introduction Culturally validated neurocognitive measures for children in Low- and Middle-Income Countries are important in the timely and correct identification of neurocognitive impairments. Such measures can inform development of interventions for children exposed to additional vulnerabilities like HIV infection. The Battery for Neuropsychological Evaluation of Children (BENCI) is an openly available, computerized neuropsychological battery specifically developed to evaluate neurocognitive impairment. This study adapted the BENCI and evaluated its reliability and validity in Kenya. Methodology The BENCI was adapted using translation and back-translation from Spanish to English. The psychometric properties were evaluated in a case–control study of 328 children (aged 6 – 14 years) living with HIV and 260 children not living with HIV in Kenya. We assessed reliability, factor structure, and measurement invariance with respect to HIV. Additionally, we examined convergent validity of the BENCI using tests from the Kilifi Toolkit. Results Internal consistencies (0.49

At present, the key technical researches are carried out for the future exploration missions to dense atmosphere planets such as Jupiter. As a necessary aerodynamic decelerator for planetary exploration missions with dense atmosphere, parachutes face several big challenges, such as complex transonic/supersonic unsteady aerodynamic characteristics. Thus, it is necessary to study deeply and make clear the transonic/supersonic unsteady flow mechanisms around the parachute systems and its aerodynamic characteristics; however, these are still unknown to date. In this study, parachute structure systems are designed based on the Galileo exploration mission. Using the parachute models, the numerical simulations were conducted to investigate the complex flow phenomena and unsteady aerodynamics of parachute systems with different diameter ratios of capsule to canopy, under different freestream Mach numbers. As a result, it is found that when a parachute 2-body model with a bigger diameter ratio of capsule to canopy is placed in transonic flows, the wake of the forebody (capsule) is strong, and the shock wave in front of the canopy is weak. When the canopy shock moves upstream, the pressure fluctuation inside the canopy surface exhibits 3 stages of changes: high-frequency large-amplitude, low-frequency small-amplitude, and high-frequency small-amplitude. It is also found that in the supersonic flow cases, the amplitude and frequency of pressure fluctuations inside the canopy increase significantly, especially at Mach 2.0. This is due to the strong interactions between the shedding vortex of the wake and the shock wave in front of the canopy. However, for the parachute model with a small diameter ratio, the important shock waves in front of the canopy is not observed, and high-frequency and small-amplitude pressure variations remain, which is affected directly by the frequency and intensity of wake vortex shedding. In summary, as the Mach number increases, the amplitude and frequency of pressure fluctuations increase, and the average pressure distribution also increases, and the law of its influence becomes more complex.

The new-generation manned spacecraft employs a new recovery system of parachutes and airbags. In order to realize the effective cushion of the return capsule airbag, the conversion of the single-point hanging to the double points for the spacecraft is necessary. In the conversion process, the long hanging rope and the height of the return capsule will cause a large impact and disturbance to the cabin, but the traditional dynamic modeling method is not suitable for those with complex boundary conditions. In order to solve these problems, a finite element model is proposed to analyze the suspension conversion process and simplify the parachute model based on the aerodynamic load dynamic matching control method to analyze the dynamic response characteristics, such as overload, speed, and force of the cabin, quantifying the conditions of the rope in the process. Results are generally consistent with experimental data. This study provides guidance for future optimization work and will greatly support the safe-return technology of the new-generation manned spacecraft.

Pansharpening is crucial for obtaining high-resolution multispectral images. Existing deep learning-based pansharpening networks rely on supervised learning with external reference labels. Due to the lack of actual fusion results for labeling, simulated degraded data is used with the original multispectral image as the fusion result label. These process steps are cumbersome, which also leads to the problem of scale degradation, and the fusion relationship between data before and after the degradation cannot represent the real fusion relationship. To address these limitations, we propose a self-supervised interactive dual-stream network for pansharpening using real training datasets. Our approach incorporates a dual-stream network architecture, comprising a spatial scale enhancement stream and a spectral channel attention stream. Spatial and spectral features essential for fusion are extracted from the original panchromatic and multispectral images, respectively. Through interconnection at different levels, the network expands the search range in the feature space, enabling continuous interaction between spatial and spectral information during feature extraction and transmission. This ensures the injection of spatial features of varying scales into corresponding-scale spectral features, enhancing complementarity between features. Moreover, we introduce a novel joint spatial-spectral loss function, leveraging the original panchromatic and multispectral images themselves as self-supervised labels. Experimental results on diverse satellite datasets demonstrate the outstanding fusion performance of our method, as assessed through both subjective qualitative evaluation and objective quantitative evaluation. Furthermore, our proposed method exhibits exceptional generalization performance for full-scale remote sensing images, showcasing its practical value.

Objective To evaluate the safety and clinical value of laparoscopic jejunostomy in minimal invasive McKeown esophagectomy. Methods The clinical data of the patients undergoing minimally invasive McKeown esophagectomy in the Department of Thoracic Surgery, Peking Union Medical College Hospital from January 2013 to June 2020 were retrospectively included, and according to postoperative nutritional support they were divided into oral intake group (January 2013 to October 2017) and jejunostomy group (November 2017 to June 2020). The operation time, postoperative hospital stay, complications, perioperative nutritional status and quality of life scores were compared between the two groups. Results A total of 190 patients with esophageal cancer who met the inclusion and exclusion criteria were enrolled. There were 128 cases in jejunostomy group and 62 cases in oral intake group. Compared with the patients in oral intake group, those in jejunostomy group had shorter postoperative hospitalization time [11(9, 13)d vs. 14(13, 20)d, P < 0.001], the completion rate of postoperative adjuvant chemotherapy in the jejunostomy group was higher(95.16% vs. 75.00%, P=0.005). The operation time [335(300, 374)min vs. 330(310, 370)min, P=0.750] and the incidence of surgery-related complications(28.13% vs. 35.48%, P=0.748) showed no significant difference between the two groups. The body mass index in jejunostomy group was higher 1 month [(23.3 ± 3.5)kg/m2 vs. (21.7±3.9)kg/m2, P=0.006] and 3 months[(22.6±3.5)kg/m2 vs. (20.6±4.0)kg/m2, P < 0.001] after surgery, and the body weight decreasing rate was lower 1 month [(3.9 ± 2.2)% vs. (10.3±3.5)%, P < 0.001] and 3 months[(6.5±3.1)% vs. (15.7 ± 4.8)%, P < 0.001] after surgery than those in oral intake group. The quality of life symptom scores in both groups were increased and the physical functioning and summary scores were sharply decreased at day 7 after operation, and all scores were improved in different degrees 1 month after operation. Compared with the measurements in oral intake group, the symptom scores (except pain score 1 month after operation) were decreased, and the physical functioning and summary scores were increased in the jejunostomy group 7 days and 1 month after operation (all P < 0.05). Conclusion Compared with oral nutrition, total laparoscopic jejunostomy has greater advantages in postoperative nutritional support and quality of life recovery in patients with esophageal cancer. It is also safe and unlikely to increase surgery-related complications.

ObjectiveTo analyze the characteristics of thoracic aorta injury in road traffic accidents, to provide data reference for forensic identification.MethodsThe data of 27 traffic accident death cases with thoracic aorta injury were analyzed according to relevant parameters including sex, age, mode of transportation, and thoracic aorta injury.ResultsAortic injury in traffic accidents was significantly more in males than females, and 74.1% cases were in the age range of 31-70 years. The most common mode of transportation was the motorcycle, followed by electric bike, most of which crashed with trucks. Most cases were accompanied by rib fractures and lung injuries. Thoracic aorta injury was the most common in ascending aorta, followed by aortic arch and thoracic aorta. Ascending aorta injury was most likely to occur in the range of 0-

With the rapid development of modern industry, the foundation soil and the underground water in some areas are severely polluted by the corrosive medium affecting the durability of foundation concrete. Sulfate corrosion is one of the major factors leading to the destruction of foundation concrete. The sulfate corrosion resistance of foundation concrete with nano-particles (nano-SiO2 and nano-CaCO3) under the action of dry-wet cycle is experimentally studied, and compared with that of plain foundation concrete. The test results indicate that, under the action of dry-wet cycle, the sulfate corrosion resistance of foundation concrete is significantly improved with the addition of nano-particles. With the increasing content of nano-particles, the sulfate corrosion resistance of foundation concrete gradually rises to its peak and then drops step by step. The sulfate corrosion resistance of foundation concrete with nano-SiO2 is superior to that of foundation concrete with the same amount of nano-CaCO3. When the content of nano-SiO2 and nano-CaCO3 is respectively 2.0 % and 1.0 % by weight of binder, the sulfate corrosion resistance of foundation concrete is the best. Because the pore structure of concrete is improved with the addition of nano-particles, as nano-SiO2 can react with Ca(OH)2 and nano-CaCO3 can react with C3A, nano-particles can significantly enhance the sulfate corrosion resistance of foundation concrete.

The performance of RAP material directly affects the engineering effect of pavement recycling technology. In this study, the performance of high content RAP recycled mixture was analyzed. Through the extraction and screening of RAP materials, it was found that the viscosity of old asphalt increased significantly, the softening point decreased, and the coarse aggregate was refined seriously. The high, low temperature and water stability of high content plant mixed recycled ATB-25 mixture were studied through laboratory tests with different regenerants. The results showed that, compared with the control group, the dynamic stability, freeze-thaw splitting strength ratio and the maximum flexural tensile strain increase rate were: Type A regenerant: 27.48%, 29.45%, 35.71%; Type B regenerant: 16.76%, 21.28%, 38.96%. Comparing the effects of the two kinds of regenerants, after adding Type A regenerant, the mixture has the best high-temperature and water stability, and Type B regenerant is better than Type A regenerant in improving the low-temperature performance. The high, low temperature and water stability of the mixture after adding the regenerant can meet the specification requirements.

The application of construction waste in road engineering is an effective means to improve the level of resource recycling. Due to the lack of specialized sorting equipment for different types of construction waste and the high cost of manual sorting, resource utilization enterprises mainly rely on mixed crushing and regeneration in production, while there is a lack of systematic research on the application technical indicators of recycled aggregates for brick concrete mixed construction waste. This article proposes a quality evaluation model for construction waste coarse aggregates based on the theory of matter element extension, providing new ideas for quantitative evaluation of the quality status of coarse aggregates, and also providing reference for the rational selection of recycled coarse aggregates and the determination of mechanical parameters of coarse aggregates in the construction process, which is conducive to the evaluation and analysis of the quality status of recycled coarse aggregates.

In the field of computer vision,3D face reconstruction is a valuable research direction.High quality reconstruction of 3D faces can find applications in face recognition,anti-proofing,animation and medical cosmetology.In the last two decades,although great progress has been made 3D face reconstruction based on a single image,the results of reconstruction using traditionalalgorithms are still facing the challenge of facial expression,occlusion and ambient light,and there will be problems such as poor reconstruction accuracy and robustness.With the rapid development of deep learning in 3D face reconstruction,various methods which are superior to traditional reconstruction algorithms have emerged.Firstly,this paper focuses on deep-learning-based reconstruction algorithms.The algorithms are divided into four categories according to different network architecture,and the most popular methods are described in detail.Then commonly used 3D face data sets are introduced,and performance of representative methods are evaluated.Finally,conclusions and prospects of the single-image-based 3D face reconstruction are given.

Silicon is regarded as one of the most promising anode candidates for next-generation Li-ion batteries because of its high theoretical capacity (4200 mAh g−1). However, the main challenge for the practical application of Si anodes is the huge volume change during (de)alloying with lithium, which leads to the pulverization of the active material and severe loss of electrical contact after cycling. Here, we develop hollow porous silicon nanospheres with three-dimensional carbon coating and SiC transition interlayer (C@SiC@Si@SiC@C) via a simple and straightforward polymer-directed strategy in order to tackle the challenges met with Si anodes. The accordingly synthesized C@SiC@Si@SiC@C anode shows high utilization of the active substance, high measured capacity (3200 mAh g−1) with almost 100% Coulombic efficiency and stable cycling performance (0.7‰ per cycle decay rate at 0.2C). Such superior performances are related to the uniquely designed structure. Firstly, the 3D carbon coating provides high electronic conductivity, and extremely small size of silicon shortens the diffusion distance for Li ions and electrons. Secondly, the stable cyclability originates from the nanoscale silicon particles reinforced by a SiC transition interlayer which effectively prevents fracture and provides robust outer layers, respectively.

We are devoted to the study of a semilinear time fractional Rayleigh-Stokes problem on ℝN, which is derived from a non-Newtonain fluid for a generalized second grade fluid with Riemann-Liouville fractional derivative. We show that a solution operator involving the Laplacian operator is very effective to discuss the proposed problem. In this paper, we are concerned with the global/local well-posedness of the problem, the approaches rely on the Gagliardo-Nirenberg inequalities, operator theory, standard fixed point technique and harmonic analysis methods. We also present several results on the continuation, a blow-up alternative with a blow-up rate and the integrability in Lebesgue spaces.

The supersonic parachute plays an important role in the descent and landing of Mars missions. Next-generation supersonic parachutes, such as disksail parachutes, are alternatives to disk-gap-band (DGB) parachutes. Disksail parachutes have larger porous gaps and smaller porous seams on the canopy surface than DGB parachutes. To date, the influence mechanism of porous seams or gaps and their locations on the performance of supersonic parachute systems in Martian atmospheric conditions remains unclear. In this study, different canopy models with seams and gaps based on NASA’s supersonic disksail parachutes were designed, and the aerodynamic characteristics of such geometric porosity models were studied numerically. For seam-only models, the drag coefficient of the parachute decreases when the position of the seam is close to the middle of the canopy. When the seam is close to the mouth of the canopy, the pressure difference between the inner and outer surface of the canopy becomes small, reducing the risk of tearing the canopy. For gap-only models, the drag coefficient of the middle gap model is higher, while the lateral force stability of the top gap model is better. The results show that the addition of a seam can improve the drag performance of the top gap model and improve the lateral stability of the canopy with the middle gap. This study provides some theoretical references for designing the porosity of parachutes under different requirements for Mars exploration missions in the future.

A fully 3D-printed soft pneumatic robotic arm based on two types of gas-structure coupling actuators is designed for on-orbit servicing. The path planning algorithm and trajectory tracking control strategy of the arm are developed. A model-free closed-loop control system with a PID controller and an iterative learning controller is built to improve the performance speed for repeated tasks. An experiential knowledge database for an iterative learning controller is described. The effectiveness of the iterative learning controller is verified by comparative experiments. The obstacle avoidance path planning algorithm based on the A* algorithm is presented. The validity of the path planning algorithm and trajectory tracking control strategy are verified by obstacle avoidance path planning experiments. The experimental results show that intelligent motion control and obstacle avoidance of the fully 3D-printed soft robotic arms are realized within an acceptable error.

BACKGROUND Mineral identification is the basis of all types of geological work, and its appraisal level and quality directly affect the depth and degree of research of a study. Conventional identification methods are significantly influenced by experience level, optical microscope resolution, and other factors. It is difficult to accurately identify fine rare minerals and clay minerals that need to be studied. Additionally, most of the technical methods relying on high-precision large-scale instruments have special requirements for sample preparation, which is not conducive to the reuse of the samples. It is also inconvenient to explore and observe specific fine transparent minerals under high multiple reflected lights, such as scanning electron microscopy and electron microprobe. OBJECTIVES To develop a more rapid and accurate method for identifying fine minerals. METHODS The laser Raman high-resolution large-area fast imaging method (StreamLineHR) was applied to the whole-area large-area scanning spectrum of two standard rock slices. RESULTS The transparent minerals were identified as alkali feldspar, plagioclase, quartz, amphibole, biotite, calcite, sphene, apatite, zircon, and epidote. The opaque mineral was identified as magnetite. Some of the minerals were closely associated (e.g., quartz and feldspar as well as sphene and hornblende), and some minerals showed secondary alterations (e.g., feldspar was transformed to calcite). Based on the content statistics, the two thin sections were named fine-grained amphibolite monzonite and fine-grained biotite plagioclase amphibolite. CONCLUSIONS Experimental results showed that this method was more accurate than the conventional methods used for the identification of fine minerals with very low content. However, the interference caused by the fluorescence effect, similarity in peak positions of similar minerals (feldspar, amphibole), and shift of the peak position of altered minerals during mineral identification and spectral fitting were solved by combining the optical characteristics under the mineral objective lens when necessary. In addition, the smaller the setting of the surface sweep step size, the more accurate the analysis, and the time cost correspondingly increased. This method realized the rapid identification of fine minerals over a large range, which was convenient, intuitive, and accurate. It compensated for the shortcomings of conventional rock and ore identification and other technical methods and expanded the application scope of Raman spectroscopy in geological studies.

With the development of the engineering construction industry, knowledge became an important strategic resource for construction enterprises, and knowledge graphs are an effective method for knowledge management. In the context of peak carbon dioxide emissions and carbon neutrality, low carbon emission became one of the important indicators for the selection of construction schemes, and knowledge management research related to low carbon construction must be performed. This study investigated a method of incorporating low-carbon construction knowledge into the bridge construction scheme knowledge graph construction process and proposed a bridge construction scheme recommendation method that considers carbon emission constraints based on the knowledge graph and similarity calculation. First, to solve the problem of the poor fitting effect of model parameters caused by less annotation of the corpus in the bridge construction field, an improved entity recognition model was proposed for low-resource conditions with limited data. A knowledge graph of low carbon construction schemes for bridges was constructed using a small sample dataset. Then, based on the construction of this knowledge graph, the entities and relationships related to construction schemes were obtained, and the comprehensive similarity of bridge construction schemes was calculated by combining the similarity calculation principle to realize the recommendation of bridge construction schemes under different constraints. Experiments on the constructed bridge low carbon construction scheme dataset showed that the proposed model achieved good accuracy with named entity recognition tasks. The comparative analysis with the construction scheme of the project verified the validity of the proposed construction scheme considering carbon emission constraints, which can provide support for the decision of the low-carbon construction scheme of bridges.

Objective: This study aimed to explore the preservation conditions and quality changes of sweet potato leaf sauerkraut fermented by lactic acid bacteria. Methods: After vacuum packaging, the pH value, lactic acid content, nitrite content, colony number, color difference and sensory score of sweet potato leaves were compared under four different kinds of storage conditions, including 25 ℃ at room temperature, 4 ℃ at low temperature, 4 ℃ after pasteurization at 80 ℃-15 min, and 4 ℃ after pasteurization at 85 ℃-10 min. Results: When stored at 25 ℃, the luminance decreased the fastest, and the color became darker at 60 days. The number of yeasts was the most after 30 days at 4 ℃, and the number of lactic acid bacteria was the most after 45 days; After pasteurization for 90 days at 80 ℃-15 min, the number of Escherichia coli was the least, and the sensory score reached the edible threshold. After pasteurization for 75 days at 85 ℃-10 min, the total number of bacteria was the least and the sensory score was qualified; pH value was negatively correlated with nitrite, and positively correlated with L value and sensory score; Lactic acid bacteria were positively correlated with yeast; E. coli and total bacteria, and the sensory score was the largest in the first principal component. Conclusion: After pasteurization at 80 ℃-15 min and storage at 4 ℃, the main microorganisms were lactic acid bacteria, with other indicators met the safety standards, and the sensory quality was stable. This condition is more suitable for the storage of pickled cabbage with lasting for 90 days.

The porous material adsorbent X zeolite was prepared by using agricultural waste rice hull ash (RHA) and modified by ion-exchange into the rare earth metal zeolite of La and Ce. A series of characterization results, including X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, N2 adsorption–desorption and pore size analysis, illustrated the effects of ion exchange modification on crystal, pore structure and morphology. The CO2 adsorption experiment was operated, and the adsorption amount of NaX and LaLiX was 6.14 and 4.36 mmol/g at atmospheric pressure, respectively. Three isotherm models were used to fit the pressure swing adsorption data. The Toth and DSL models were more accurate due to their ability to express heterogeneity. The isosteric heat and selectivity were obtained by the corresponding models and calculations. The results explained the mechanism of heat and gas uptake during adsorption and desorption. The CO2/N2 selectivity of LaNaX has enhanced more than three times. All modified zeolite samples maintained above 96.5% of initial adsorption after 20 adsorption–desorption cycles. The adsorption heat releases properties and long-term stability of the modified zeolite are superior. It demonstrates that such adsorbents can be used for long-term capture and separation of CO2 from the industrial exhaust gas.

PurposeImmune checkpoint inhibitors (ICIs) have recently emerged as an important option for treating patients with advanced non-small cell lung cancer (NSCLC). Neoantigens are important biomarkers and potential immunotherapy targets that play important roles in the prognosis and treatment of patients with NSCLC. This study aimed to evaluate and characterize the relationships between somatic mutations and potential neoantigens in specimens from patients who underwent surgical treatment for NSCLC.Patients and MethodsThis prospective study evaluated specimens from patients with NSCLC who underwent surgical treatment at the Peking Union Medical College, China, from June 2019 to September 2019. Whole-exome sequencing was performed for tumor tissues and corresponding normal tissues. Candidate neoantigens were predicted using generative software, and the relationships between various mutation characteristics and number of neoantigens were evaluated.ResultsNeoantigen-related gene mutations were less frequent than mutations affecting the whole genome. Genes with high neoantigen burden had more types and higher frequencies of mutations. The number of candidate neoantigens was positively correlated with missense mutations, code shift insertions/deletions, split-site variations, and nonsense mutations. However, in the multiple linear regression analysis, only missense mutations were positively correlated with the number of neoantigens. The number of neoantigens was also positively correlated with base transversions (A>C/C>A, T>G/G>T, and C>G/G>C) and negatively correlated with base transitions (A>G/G>A and C>T/T>C).ConclusionThe number of candidate neoantigens in NSCLC specimens was associated with mutation frequency, type of mutation, and type of base substitution.

The supersonic flows around rigid parachute-like two-body configurations are numerically simulated at Mach number of 1.978 by solving three-dimensional compressible Navier-Stokes equations, where the two-body model consists of a capsule and a canopy, and a geometric structure (i.e., gap) is located on the canopy surface. The objective of this study is to investigate the effects of gaps with different porosities and positions on the aerodynamic performance of supersonic parachute. The complicated periodic aerodynamic interactions between the capsule wake and canopy shock occur around these two-body models. From the formation of canopy shock and drag coefficient variation, the cycled flow structures can be divided into three types:(1) narrow wake period, (2) open wake period, and (3) middle wake period. In addition, it was found that the geometric gaps have no obvious influences on the flow modes. However, compared with models with different gap positions, the two-body model with an upper gap (gap is close to the canopy vent, UG model) has a smaller drag coefficient fluctuation and better lateral stability. On the other side, the increase of porosity has a more significant impact on UG models.

The three methods, agent-based model (ABM), product life cycle management (PLM), and discrete firefly optimization algorithm (DFOA), used herein rely on local infrastructure functions after reviewing the local and global functions. Then, a resolution of the multi-layered neural network is proposed. A resolution has been saved at all levels of the structure. A global approximation function that keeps learning samples stored is employed. The local map is converted using a set having a respective free rotation. Then, the translation is reflected by a global map of each local map using the affine transformation. The differences of the conversion that the optimal global map uses by minimizing the common sensor nodes are shared by the discovery of different local maps. The optimal conversion is found by running a discrete firefly optimization algorithm (DFOA). Thus, local map registration can resolve the merged map-based approach for each of several pairs and can achieve better performance. Therefore, it provides a systematic approach to building a global map from a local map. A computer simulation was conducted to verify the performance and efficiency of the algorithm.

The angular momentum of galaxies (galaxy spin ) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ∼30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ∼7 σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to

On December 17, 2020, the Chang’e-5 reentry spacecraft landed safely and brought back the lunar sample without damage. This paper describes the recovery system that has critically contributed to the scientific success of the Chang’e-5 missions and presents the technical requirements and constraints of the recovery system for the Chang’e-5 reentry spacecraft and discusses the design process of the recovery system, including the system composition, working procedure, and some other key aspects. Finally, the ground cover rejection tests and air drop and flight tests were carried out to confirm the design configuration. The results showed that the Chang’e-5 reentry spacecraft recovery system was designed correctly, and its functions and performances met the design requirements. A breakthrough in the recovery technology of the reentry spacecraft was achieved for Chinese first lunar sample-return mission.