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Ultra-high-performance concrete (UHPC) has been regarded as promising alternative to provide reliable connections between difference segments (e.g., columns and pier footing/cap) during accelerated bridge construction (ABC) procedures. This paper proposes an innovative layered-UHPC connection for the pre-fabricated segmental (PFS) pier, whose seismic performance was validated through quasi-static experiment. The corresponding design procedure for PFS pier with this type of connections is presented based on the test results. The layered-UHPC connection ensures the emulative performance of pre-fabricated bridge as cast-in-place (CIP) ones, as well as provides greater economic efficiency than traditional UHPC connections. Based on experimental results, key issues concerning this connection, including the tensile behavior of UHPC, height of connection region, thickness of UHPC layer and steel bars in grouting bed, are presented and discussed. Then a seismic design procedure is proposed utilizing the capacity protection philosophy widely adopted in design specifications. The layered-UHPC connection is expected as capacity-protected component without damage, since it provides anchorage for steels extended from columns and pier cap/footing. While the pre-fabricated region is designed as ductile component undergoing nonlinearity during strong earthquakes. Following the detailed elaborations about the design philosophy, requirement and implementation steps, this procedure is further presented through illustration examples using PFS piers with various heights. The results show that PFS piers designed according to this procedure could meet the requirement under both frequent and rare earthquakes. Note that the PFS piers with this layered-UHPC connections could be designed similar to and emulative as CIP ones, which is believed friendly to designers in engineering practice.

Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and the generated molecules possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. The code is available at https://github.com/clinfo/SBMolGen.

Deoxyribonucleic acid (DNA) has evolved to be a naturally selected, robust biomacromolecule for gene information storage, and biological evolution and various diseases can find their origin in uncertainties in DNA-related processes (e.g. replication and expression). Recently, synthetic DNA has emerged as a compelling molecular media for digital data storage, and it is superior to the conventional electronic memory devices in theoretical retention time, power consumption, storage density, and so forth. However, uncertainties in the in vitro DNA synthesis and sequencing, along with its conjugation chemistry and preservation conditions can lead to severe errors and data loss, which limit its practical application. To maintain data integrity, complicated error correction algorithms and substantial data redundancy are usually required, which can significantly limit the efficiency and scale-up of the technology. Herein, we summarize the general procedures of the state-of-the-art DNA-based digital data storage methods (e.g. write, read, and preservation), highlighting the uncertainties involved in each step as well as potential approaches to correct them. We also discuss challenges yet to overcome and research trends in the promising field of DNA-based data storage.

The diffusion of e-commerce has played a significant role in recent rural economic development in China. E-commerce is also considered as an efficient channel to alleviate poverty in rural China. Voluminous studies have investigated the contribution of e-commerce to agricultural development, yet it is lacking empirical evidence as to the effects of e-commerce on rural poverty alleviation. Since the year of 2014, in order to develop rural e-commerce, Chinese government launched the National Rural E-commerce Comprehensive Demonstration Project. This gradual involvement policy offered a natural experiment for evaluation of e-commerce. Based on village-level survey data from rural China and Heckit method, our study finds that rural e-commerce has a significantly positive effect on rural income. Moreover, the effect is inverted U-shaped for the relative-poverty villages. The estimation of the propensity scores matching model confirms that the results are robust. The following policy recommendations are proposed: (1) policy support to rural e-commerce should prioritize the poverty-stricken villages. By doing so, the marginal income effects of e-commerce will be maximized. (2) Investment in internet infrastructure and establishment of human resources for e-commerce in rural areas will have spillover effects, increasing rural income through the “digital dividend”.

Liquid metal (LM) has shown potential values in different areas. Attempts to implement LM are tending to develop new functions and make it versatile to improve its performance for practical applications. Here, we present an unprecedented LM-integrated ultra-elastic microfiber with distinctive features for wearable electronics. The microfiber with a polyurethane shell and an LM core was continuously generated by using a sequenced microfluidic spinning and injection method. Due to the precise fluid manipulation of microfluidics, the resultant microfiber could be tailored with tunable morphologies and responsive conductivities. We have demonstrated that the microfiber could act as dynamic force sensor and motion indicator when it was embedded into elastic films. In addition, the values of the LM-integrated ultra-elastic microfiber on energy conversions such as electro-magnetic or electro-thermal conversions have also been realized. These features indicate that LM-integrated microfiber will open up new frontiers in LM-integrated materials and the wearable electronics field.

An accurate determination of the system failure threshold is an essential requirement in achieving an appropriate system residual life prediction and a reasonable planned maintenance strategy optimization afterward for degradation systems. This paper proposes a failure threshold determination method based on quantitative measurement of the similarity between the operating system and the historical systems. The similarity is formulated by a weighted average function and then calculated by a convex quadratic formulation to minimizing the variance between the operating system and the historical systems. With an accurate determination of the system failure threshold in real-time, a better prediction of the residual life for the operating system is achieved. Finally, a real case study for several power-shift steering transmission systems monitored using oil spectral analysis is adopted to illustrate and numerically compare the improved performance of the proposed method.

Purpose Ferulic acid (FA) is a natural compound which is used to treat insomnia. However, its use is limited because of its poor oral bioavailability caused by extremely rapid elimination. The current study aimed to develop a self-microemulsifying drug delivery system (SMEDDS) to improve the oral delivery of FA and to enhance its hypnotic efficacy. Methods FA-SMEDDS was prepared, and its morphology and storage stability were characterized. The formulation was also subjected to pharmacokinetic and tissue distribution studies in rats. The hypnotic efficacy of FA-SMEDDS was evaluated in p-chlorophenylalanine-induced insomnia mice. Results FA-loaded SMEDDS exhibited a small droplet size (15.24 nm) and good stability. Oral administration of FA-SMEDDS yielded relative bioavailability of 185.96%. In the kidney, SMEDDS decreased the distribution percentage of FA from 76.1% to 59.4% and significantly reduced its metabolic conversion, indicating a reduction in renal elimination. Interestingly, FA-SMEDDS showed a higher distribution in the brain and enhanced serotonin levels in the brain, which extended the sleep time by 2-fold in insomnia mice. Conclusion This is the first study to show that FA-loaded SMEDDS decreased renal elimination, enhanced oral bioavailability, increased brain distribution, and improved hypnotic efficacy. Thus, we have demonstrated that SMEDDS is a promising carrier which can be employed to improve the oral delivery of FA and facilitate product development for the therapy of insomnia.

Regular inspection of pavement conditions is important to guarantee the safety of transportation. However, current approaches are time-consuming and subjective, which requires the technician to annotate each training image exactly pixel by pixel. To ease the workload of the inspector and lower the cost of acquiring the high-quality training dataset, a semi-supervised method for the pavement crack detection is proposed. Firstly, unlabeled pavement images can be used for the model training in our proposed algorithm, our model can generate a supervisory signal for unlabeled pavement images, which makes up for the deficiency of image annotation. Secondly, an adversarial learning method and a full convolution discriminator are adopted, which can learn to distinguish the ground truth from segmentation predictions. To improve the accuracy of pavement crack detection, the adversarial loss is coupled with the cross-entropy loss in discriminator. Thus, the quality of the training model is no longer dependent on the quantity of the labeled dataset and the accuracy of the labeled. Compared with existing methods that can only employ labeled images, our method utilizes unlabeled images to improve the pavement crack detection accuracy. Moreover, our model is validated on the CFD dataset and AigleRN dataset, the experimental results show that the proposed algorithm is effective. Compared with existing methods, not only can our method detect different types of cracks, but also be particularly effective when only a few labeled are available: when using 118 crack images with a resolution of 480 × 320, using only 50% of the labeled data, the detection accuracy of our model can reach 95.91%.

Background:Breast cancer (BC) is one of the most common tumors in women. Recent years, immune checkpoint inhibitors (ICIs) have brought good news to BC patients. Although significant achievements have been made through treatment with ICIs, some people who experience serious immune-related adverse events (IrAEs) are still insensitive to this approach. The response to ICI treatment depends on the type of tumor microenvironment (TME). Methods:WGCNA (weighted gene co-expression network analysis), ESTIMATE algorithm, LASSO regression analysis, survival analysis, functional enrichment analysis are conducted to analyze the BC data in the TCGA database. Immunohistochemistry was used to verify the expression of CD52 in BC.Results:WGCNA and ESTIMATE algorithm found that the CD52 is closely related to the immune microenvironment. CD52 highly expressed in various breast cancer subtypes, and patients with high expression of CD52 have longer survival time. Compared with the low-CD52 group, the high-CD52 group had more immune cell infiltration. TIMER database verification results showed that CD8+ T cells, activated memory CD4 T cells, memory B cells, γδ T cells, and Tregs were positively correlated with CD52 expression, while M2 macrophages were negatively correlated. CD52 can change the trend of TIC (CD8+ T) and tumor-associated macrophage (TAM) infiltration with respect to the survival time of breast cancer patients. Based on the expression of CD52, we explored the relationship between CD52 and the adaptive immune response (AIR). CD52 is a marker of AIR stratification in breast cancer patients. We constructed a CD52-related adaptive immune response gene signature (CD52rAIRGsig) which is an independent prognostic factor for breast cancer and related to genome instability and the immune cells infiltration in the TME. CD52 and CD52rAIRGsig were associated with PD-1 signaling and immune checkpoint inhibitor markers, which proves that patients with high CD52 expression and low risk of CD52rAIRGsig are more suitable for ICI treatment. We then screened chemotherapeutics for personalized medicine based on CD52rAIRGsig. Conclusion:Therefore, we have discovered a new marker to guide the treatment and prognosis of breast cancer patients with ICIs. This provides a combined treatment strategy including different combinations of ICIs combined with chemotherapeutic drugs to treat breast cancer.

Colon adenocarcinoma (COAD) is one of the diseases with the highest morbidity and mortality in the world. At present, immunotherapy has become a valuable method for the treatment of COAD. Tumor mutational burden (TMB) is considered to be the most common biomarker for predicting immunotherapy. According to reports, the mutation rate of COAD ranks third. However, whether these gene mutations are related to TMB and immune response is still unknown. Here, COAD somatic mutation data were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Bioinformatics methods were used to study the relationships among gene mutations, COAD survival prognosis, and tumor immune response. A total of 22 of the top 40 mutations in TCGA and ICGC databases were the same. Among them, the USH2A mutation was associated with high TMB and poor clinical prognosis. According to Gene Set Enrichment Analysis (GSEA) and the CIBERSORT algorithm, we determined that the USH2A mutation upregulates signaling pathways involved in the immune system and the antitumor immune response. In cases with a USH2A mutation, the immune score and MSI score of TCGA samples increased, the expression of immune checkpoint genes decreased significantly, and the TIDE score decreased significantly. Dependent on the presence or absence of a USH2A mutation, TCGA COAD samples were analyzed for differentially expressed genes, 522 of which were identified. Using a univariate Cox analysis and LASSO COX analysis of these differential genes, a prediction model was established, which established significant differences in the infiltration of immune cells, immune checkpoint gene expression, immune score, MSI score, TMB, and TIDE in patients in high- and low-risk groups. In conclusion, mutation of USH2A is frequent in COAD and is related to an increase in TMB and the antitumor immunity. The differential genes screened by USH2A mutation allowed the construction of a risk model for predicting the survival and prognosis of cancer patients, in addition to providing new ideas for COAD immunotherapy.

Long non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

Herein, we have developed a strategy of sequential C-H activations of indole to construct novel 2-alkynyl aza-spiro[4,5]indole scaffolds, which incorporated both alkyne and spiro-units into indole. Gram-scale synthesis and a one-pot, three-step synthesis demonstrated the utility of this protocol. Hybrid conjugates with an oseltamivir derivative further offered a powerful tool for the construction of a versatile spiroindole-containing library via click chemistry.

Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

With the advent of the information age, the Internet of Things (IoTs) technology has been favored by many industries and became another in-depth revolution of information technology industry. Under such an era, the application of the IoTs technology to the construction of intelligent buildings can broaden the practicality of intelligent systems, optimize resource allocation, and enhance the management and service capabilities of intelligent buildings, thereby improving people’s quality of life. On the basis of summarizing and analyzing previous research works, this paper elaborates the development status and future challenges of the IoTs technology, analyzes the impact of IoTs technology on intelligent buildings and intelligent manufacturing, and expounds the integrated system framework of intelligent building and building information model (BIM) technology, and proposes the system design of intelligent building and the architecture model of intelligent manufacturing based on the IoTs technology. The model verification results show that the intelligent manufacturing model of construction industry can realize the integration of human society and physical system and achieve the purpose of real-time management and control of personnel, machines, equipment, and infrastructure within the whole network. The study results of this paper provide a reference for the further research on the intelligent manufacturing model of construction industry based on the IoTs technology.

Shared control structure is beneficial to steering controller design of intelligence vehicles, and human-machine goal consistency is a key prerequisite for shared control. However, the goal consistency is usually given and cannot be changed, and the steering controller in low goal consistency, which directly affect the vehicle performance in case of emergency, has not been sufficiently investigated. This paper proposes a shared steering controller for path-following task based on Nash game strategy and steer-by-wire system considering different human-machine goal consistency. The driver-automation interactive path-following task is modeled by non-cooperative MPC, and authority weight of lateral displacement is used to balance the control objectives of the driver and automation. Human-machine goal consistency is determined by the driver and the automation controller steering angle. Aimed at different goal consistencies, a continuous authority weight adjustment algorithm is designed to ensure correct path following. This is especially true in low consistency in this study, when four driving modes are given to meet the different demand for control power. Simulations and hard-in-loop tests are conducted to verify the proposed control algorithm and the results show that it can perform the path-following task irrespective of human-machine goal consistency.

Background: Alectinib has shown a greater efficacy to ALK-rearranged NSCLCs in first-line setting; however, most patients relapse due to secondary mutations in ALK such as I1171N and G1202R. Although ceritinib or lorlatinib was shown to be effective to these resistant mutations positive tumor, several cases of ALK-compound mutation-mediated resistance are reported in relapsed patients. However, the overcoming drug has not been clarified yet. Methods: We established lorlatinib-resistant ALK-G1202R-positive tumor in vivo. ENU mutagenesis screening was performed using Ba/F3 cells expressing ALK-I1171N and -G1202R. We performed drug screening to overcome the ALK compound mutations. To evaluate these resistances in silico, we developed a modified computational molecular dynamic simulation (MP-CAFEE). Findings: We identified 14 lorlatinib-resistant ALK-compound mutants, including several mutants recently discovered in lorlatinib-resistant patients. Most of these compound mutants were found to be sensitive to early-generation ALK-TKIs and several BCR-ABL inhibitors. Using our original computational simulation, we succeeded in demonstrating a clear linear correlation between binding free energy and in vitro experimental IC50 value of several ALK-TKIs to single-mutant EML4-ALK expressing Ba/F3 cells and in recapitulating the tendency of binding affinity reduction by the double mutations found in this study. Through the computational simulation, we found that ALK-L1256F single mutant conferred resistance to lorlatinib but increased sensitivity to alectinib. Interpretation: We discovered lorlatinib resistant multiple ALK compound mutations and an L1256F mutation, and found the potential therapeutic strategies to those ALK mutations. Our original computational simulation calculating binding affinity might be applicable for predicting resistance mutations and overcoming drugs in silico. Funding: This study was supported in part by MEXT/JSPS KAKENHI grant number 15H02368 and 17H06327 (to N. Fujita), 16H04715 (to R. Katayama), the grant from the MHLW/AMED grant number 17cm0106203h0002 and 17ck0106231h0002 (to R. Katayama), and the grant from the Vehicle Racing Commemorative Foundation (to R. Katayama). MEXT as “Priority Issue on Post-K computer (Building Innovative Drug Discovery Infrastructure Through Functional Control of Biomolecular Systems)” (to Y. Okuno), and FOCUS Establishing Supercomputing Center of Excellence (to Y. Okuno). This research used the computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science through the HPCI System Research Project (Project IDs: hp150272, hp160213, hp170275, and ra000018). Declaration of Interest: R Katayama received research grants from Toppan Printing, Taiho Pharmaceutical, Fujifilm, TAKEDA. Ethical Approval: All in vivo studies were conducted through the Institutional Animal Care and Use Committee-approved animal protocols according to the institutional guidelines.

This study developed discrete element models to simulate the dynamic repetitive load test using precision unbound material analyzer (PUMA). The simulated aggregate was created with the clump method...

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze &amp, excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

Background: Equipoise exists regarding the implementation of evidence-based guidelines for nutritional therapy in critical illness because some previous cluster-randomised controlled trials (cRCTs) demonstrate patient benefits whilst others do not. We aimed to resolve this issue by initiating an appropriately powered cRCT to assess whether active implementation of an evidence-based guideline for nutrition therapy in critical illness could reduce mortality. Methods: We did a multicentre, cluster-randomised, parallel-controlled trial in 97 intensive care units (ICUs) throughout China. We developed an up-to-date, evidence-based nutrition guideline. ICUs randomly allocated to implement the guideline formed a local "intervention team", which actively implemented the guideline using standardized educational materials. ICUs assigned to the control group remained unaware of the guideline. All ICUs enroled critically ill patients who were expected to stay longer than seven days. The primary outcome was all-cause mortality within 28 days of enrolment . Findings Between March, 2018 and July, 2019, we enrolled 1,373 patients at 48 guideline ICUs and 1,399 patients at 49 control ICUs. Implementation of the nutrition guideline resulted in significantly earlier enteral nutrition (EN) initiation (1.20 vs. 1.55 mean days to initiation of EN; difference -0.40 [95% CI, -0.71 to -0.09]; P=0.01) and delayed parenteral nutrition (PN) initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI, 0.44 to 1.67]; P=0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference -1.6% [95% CI -4.3% to 1.2%]; p=0.42) between study groups. Interpretation: Active implementation of an evidence-based guideline can change practice. Our active implementation strategy improved the provision of nutrition therapy to critically ill patients. However, in our cRCT, this improvement in care did not translate to a reduction in mortality. We did not identify any safety concerns associated with the active implementation of our evidence-based guideline for nutrition therapy. Trial Registration: The study was registered in the ISRCTN registry before enrolment commenced (No. ISRCTN12233792). Funding Statement: The study was funded by the Key Research and Development Program Foundation of Jiangsu Province of China (no. BE2015685) and Nutricia, Wuxi, China. Declaration of Interests: Dr. GSD reported receiving academic research grants related to nutrition in critical illness from the Australian National Health and Medical Research Council, Fresenius Kabi Deutschland GmbH and Baxter Healthcare Pty Ltd and speakers honoraria from Fresenius Kabi Deutschland GmbH, Baxter Healthcare Australia, Pty Ltd, Nestle Healthcare, Vevy, Switzerland and Nutricia Pharmaceutical (Wuxi) Co., Ltd. China. Dr. AvZ reports personal fees from Baxter, personal fees from Nestle, personal fees from Fresenius Kabi, grants and personal fees from Nutricia, grants from Cardinal Health grants from Mermaid grants from Lyric, outside the submitted work. Dr. WqL reported receiving speakers honoraria from Nutricia Pharmaceutical (Wuxi) Co., Ltd. China. No other disclosures were reported. Ethics Approval Statement: The study was approved by the local hospital ethics committees of all the participating ICUs. At each site, informed consent was obtained from the patients or their next of kin before enrolment.

Under strong winds and at low temperatures, heat loss of hot-mix asphalt mixtures is likely to occur, which leads to temperature segregation. Temperature segregation affects the forming quality and the performance of asphalt pavements. In this study, a phase-change thermal-insulation agent (PCTIA) was prepared for reducing the temperature dissipation. A cooling simulation experiment was performed to test the temperature-dissipation process for an ordinary asphalt mixture and the asphalt mixture mixed with PCTIA (AM-PCTIA). The thermal-insulation effect was analyzed according to the temperature difference and the thermal-insulation extension time. Moreover, two indices&mdash, the thermal-insulation accumulated time difference value (IATDV) and thermal-insulation accumulated time difference index (IATDI)&mdash, were proposed for evaluating the thermal-insulation ability and efficiency. The results indicated that the temperature at the center of the AM-PATIA was 4 &deg, C higher than that for the ordinary asphalt mixture. The insulation time was prolonged by 29.8 min at the ambient temperature of 15 &deg, C. As the ambient temperature increased, the thermal-insulation effect of the PCTIA improved.

BackgroundThe gut microbiota participates in the metabolism of substances and energy, promotes the development and maturation of the immune system, forms the mucosal barrier, and protects the host from pathogen attacks. Although the pathogenesis of cholesterol gallstones is still not clear, studies have suggested that gut microbiota dysbiosis plays an important role in their formation.MethodsMicrobial DNA from faeces of normal control patients and those of patients with calculi was subjected to 16S rRNA gene sequencing to detect gene expression changes in intestinal microbes. ELISA kits were used to measure free bile acids, secondary bile acids and coprostanol according to the manufacturer’s instructions. The relationship between flora and their metabolites was then analysed.ResultsIn the gallstone group, the diversity of intestinal bacteria and the abundances of certain phylogroups were significantly decreased (p Firmicutes(p p p p p Ruminococcus gnavusgroup could be used as a biomarker to distinguish the gallstone group from the control group.ConclusionWe conclude that intestinal flora imbalance affects bile acid and cholesterol metabolism and is associated with gallstone formation.

With the rapid development of artificial intelligence, related technologies and applications come into being, and industries based on artificial intelligence are booming, among which image recognition and target tracking technologies are widely used in various fields, especially in the fields of security monitoring and augmented reality. In this paper, combined with the characteristics of athletes, based on mobile artificial intelligence terminal technology, the C/S mode of athlete training process monitoring system is developed and designed, which uses GPS to obtain the real-time position information of athletes and provide real-time guidance for athletes. In order to reveal the changing rules of various indexes of athletes in training state, the author makes synchronous tracking analysis from the aspects of individual sports function characteristics of athletes, training plan arrangement of coaches, brain function state, routine physiological and biochemical indexes, nutrition regulation, and injury conditions.

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 &times, 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder&ndash, decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it&rsquo, s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from &minus, 11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

Background There is a lack of large-scale epidemiological data on the clinical practice of enteral nutrition (EN) feeding in China. This study aimed to provide such data on Chinese hospitals and to investigate factors associated with EN delivery. Methods This cross-sectional study was launched in 118 intensive care units (ICUs) of 116 mainland hospitals and conducted on April 26, 2017. At 00:00 on April 26, all patients in these ICUs were included. Demographic and clinical variables of patients on April 25 were obtained. The dates of hospitalization, ICU admission and nutrition initiation were reviewed. The outcome status 28 days after the day of investigation was obtained. Results A total of 1953 patients were included for analysis, including 1483 survivors and 312 nonsurvivors. The median study day was day 7 (IQR 2–19 days) after ICU entry. The proportions of subjects starting EN within 24, 48 and 72 h after ICU entry was 24.8% (84/352), 32.7% (150/459) and 40.0% (200/541), respectively. The proportion of subjects receiving > 80% estimated energy target within 24, 48, 72 h and 7 days after ICU entry was 10.5% (37/352), 10.9% (50/459), 11.8% (64/541) and 17.8% (162/910), respectively. Using acute gastrointestinal injury (AGI) 1 as the reference in a Cox model, patients with AGI 2–3 were associated with reduced likelihood of EN initiation (HR 0.46, 95% CI 0.353–0.599; p

Temperature changes of pavement structure have significant influence on the internal stress, strain, and deformation characteristics of the pavement structure. For example, rutting occurs in high temperature range and pavement cracks when its temperature is low. This yields failure and reduces the service life of the pavement structure. Phase change material, PCM, which is a new type of additive for pavement engineering, has been studied for regulating the working temperature of pavement structure. By storing or releasing thermal energy via phase change process, the PCM is able to maintain the temperature of pavement structure in a relatively narrow range. In the present study, outdoor asphalt mixture model constructed with and without PCM were established. The outdoor model was directly exposed to the environment and the temperature distribution inside the outdoor model was measured by using temperature sensors. The specific heat capacities of asphalt mixture mixed with and without PCM were determined by calorimetric method, which is simple and reliable. Based on the outdoor asphalt mixture model, the corresponding finite element model was established by using ANSYS, and the temperature distribution inside the finite element model was determined according to the measured specific heat capacity, density, thermal conductivity of the asphalt mixture, and the meteorological data which were used for determining the boundary conditions. Comparison between the calculated and actual measured temperature values was made. The results show that the calculated temperature values had good agreement with the measure ones, based on which the accuracy of material parameters and boundary conditions were validated. The results of the present study can lay the foundation for calculating the temperature field of pavement structure constructed with PCM and investigating the application of PCM in road engineering.

Here we report a bio-inspired photonic nitrocellulose array for ultrasensitive nucleic-acid detection. The patterned photonic nitrocellulose array is inspired by the Stenocara beetle living in the desert, which can collect water on its bumpy back surface from early morning fogs so that spontaneous generation of separated reaction droplets for loop-mediated isothermal amplification (LAMP)-based detection is enabled. Owing to the slow-photon effect of the photonic nitrocellulose, the fluorescence signal of calcein produced during the LAMP reaction can be effectively enhanced (up to 32 fold), which results in dramatically improved sensitivity for the detection of single nucleic acids in 40 min. We demonstrate that Staphylococcus aureus (SA) DNA can be quantitatively detected with a limit-of-detection of 0.60 copy per μL. The consumption of reagents and sample is also remarkably reduced owing to the highly decreased dead volume of the nitrocellulose substrate. Therefore, this bio-inspired photonic nitrocellulose array is promising for carrying out inexpensive, ultrasensitive, and high-throughput nucleic-acid detection under resource-limited settings.

The constitutive model is the crucial part for the finite element analyses. To study the elasto-plastic properties of unbound granular materials (UGMs) under repeated vehicular loads, an elasto-plastic constitutive model called revised spatially mobilized plane (SMP) was proposed and validated. In this study, the revised SMP model was used for the plastic strain analyses of a typical three-layer pavement structure. To make comparisons, the Mohr-Coulomb and Druck-Prager models were employed for the numerical computation. The results show that plastic tensile and compressive strains in the horizontal and vertical directions appear on the top surface of UGM using the revised SMP model, but no plastic strains are produced by the Mohr-Coulomb and Druck-Prager models. The distribution of plastic strains in the revised SMP model had a good relationship with the actual loading areas under the vehicular loading, which related to the rutting. With the Mohr-Coulomb and Druck-Prage models, a great plastic strain was produced during the first several loading cycles and hardly increased in the following loading cycles, while the plastic strain in the revised SMP model presented an obvious increasing tendency with increased loading cycles. The predicted permanent deformations of the revised SMP, Mohr-Coulomb and Druck-Prage models were 0.557 mm, 0.78 mm and 0.155 mm, respectively. Our work reveals that the Mohr-Coulomb model may over-predict and Druck-Prage model may under-predict the rutting of pavement in early loading stage and the results proved that the revised SMP model had advantages in the description of the plastic strain of UMG under repeated loads.

The lack of seismic capacity of piers leads to bridge collapse in recent earthquakes, such as the 1994 Northridge (USA), 1995 Kobe (Japan) and 2008 Wenchuan (China) earthquakes. For precast bridge piers, the performance of pier connection determines the seismic capacity of the whole bridge. Therefore, a new connection between precast bridge column and footing using ultra-high performance concrete (UHPC) is developed in this paper. The precast column and footing are connected by longitudinal rebars of the column and the footing anchored into the UHPC-filled cage. The connection allows force to be transferred from the column rebars to UHPC, then to the footing rebars by bond effects. To investigate the seismic behavior of precast bridge piers with this new UHPC connection, cyclic tests with two large-scale (1:2.5) models are carried out. One is the precast reinforced concrete (RC) specimen using the proposed new connection, and the other is the cast-in-place (CIP) RC reference. The results show that the failure modes of the specimens are similar, both denoted by crushing of the core concrete above the connection segment. No obvious damage is observed in the connection region, which demonstrates that the precast bridge pier with the proposed connection is emulative of the CIP pier. Furthermore, a finite element model is developed to simulate the cyclic response of the precast column with proposed connection. The deviation between the calculated and measured force–displacement relationships is about 5%, which indicates that the numerical model used herein can be effective to capture the performance of the precast pier with new UHPC connection.

Although photocatalysts are enormously explored for solar energy utilization, it remains a challenge to construct a highly efficient photocatalytic system by successfully steering the charge separation, transportation and consumption. Here we designed a novel photocatalyst by rationally constructing the photoelectron-trapped/accumulated site and electron transportation path via assembling TiO 2 , Ag and SrSO 4 nanoparticle together, where TiO 2 nanoparticles work as a solar energy harvester to generate electron-hole pairs, Ag nanoparticles conduct as electrons trapped and accumulated sites, and SrSO 4 nanoparticles are used to build coherent electron transportation paths and consumption sites. Our work provides a general bottom-up route for designing and preparing novel photocatalysts with ultrafast charge separation and highly catalytic activates.

Accelerated bridge construction (ABC) has attracted substantial attentions around the world owing to advantages of minimizing construction delivery time, improving component material quality and promoting overall-economy performance. An essential factor in widespread and successful implementation of the ABC lies in the reliable connections between precast units. In this study, the distinction of seismic performance between the high and short precast reinforced concrete pier columns with a new column-to-footing (or cap beam) connection filled with ultra-high performance concrete (UHPC) were experimentally investigated through the quasi-static testing. Based on the observations of test results, although the phenomena of sliding and uplifting around the joints were obvious, the failure modes of high and short columns were the flexural failure and the shear failure on the non-joint zone, respectively. Moreover, the damage process, the ductility capacity, the resilience behavior and the hysteresis energy response of the two large-scaled prefabricated piers were described and discussed.

As one of the most important crops, maize not only has been a source of the food, feed, and industrial feedstock for biofuel and bioproducts, but also became a model plant system for addressing fundamental questions in genetics. Male sterility is a very useful trait for hybrid vigor utilization and hybrid seed production. The identification and characterization of genic male-sterility (GMS) genes in maize and other plants have deepened our understanding of the molecular mechanisms controlling anther and pollen development, and enabled the development and efficient use of many biotechnology-based male-sterility (BMS) systems for crop hybrid breeding. In this review, we summarize main advances on the identification and characterization of GMS genes in maize, and construct a putative regulatory network controlling maize anther and pollen development by comparative genomic analysis of GMS genes in maize, Arabidopsis, and rice. Furthermore, we discuss and appraise the features of more than a dozen BMS systems for propagating male-sterile lines and producing hybrid seeds in maize and other plants. Finally, we provide our perspectives on the studies of GMS genes and the development of novel BMS systems in maize and other plants. The continuous exploration of GMS genes and BMS systems will enhance our understanding of molecular regulatory networks controlling male fertility and greatly facilitate hybrid vigor utilization in breeding and field production of maize and other crops.

Video stability is a significant problem for videos captured by handheld or body-worn cameras. An accurate video stability estimation that is consistent with human perception is the basis of effective video stabilization algorithms. It is also useful for comparing different video stabilization algorithms and constructing a benchmark. In this paper, we present a perception-inspired video stability estimator based on 2D image motions. It calculates the fraction of information in each frame that can be perceived by human eyes. Experimental results show that our stability estimator can accurately estimate subjective video stability scores. It requires less time to compute and is more accurate and robust under different scene structures than methods based on 3D motions.

Accelerated bridge construction (ABC) has been increasingly popular in recent decades, due to various potential advantages compared with traditional cast-in-place (CIP) construction. Grouted splice (GS) sleeve is one of the most widely adopted techniques connecting the pre-fabricated segmental bridge columns and pier footings/caps. Therefore, the behavior of segmental piers with GS should be investigated carefully, especially under earthquake events. This paper investigates the seismic performance of GS piers, considering the influence of columns heights that is scarcely incorporated in previous numerical and experimental studies. The results show that the distribution of plastic regions and hysteretic responses of GS piers are similar to the CIP ones, except for GS regions remaining elastic. The ultimate lateral forces of GS piers are increased by about 10%, since the implementation of GS improves the corresponding section stiffness. While the yield displacement of GS piers accounts over 90% of CIP ones, the displacement capacity and ductility are 40–60% lower, caused by the strain penetration of steel bars and shifting of plastic deformation into grouted beds, as well as shorter plastic regions. Additionally, investigation on coupler rigid length factor ( β ) proposes for the first time that this parameter could be affected by the pier height, H, and might be related to other factors. Suggestions concerning the seismic design of GS piers are finally proposed according to the above results.

Pavement crack detection is a key task to ensure driving safety. Manual crack detection is subjective and inefficient. Therefore, it is very important to develop an automatic crack recognition system. However, asphalt pavement crack detection from images is a challenging problem due to the interference of complex noises. Meanwhile, although deep learning-based methods have recently made great progress in crack classification and recognition, there are still difficulties, such as large parameters and low detection efficiency. For this purpose, we develop a novel crack recognition and analysis system. Firstly, crack images are cut by using the overlapping sliding window to establish crack datasets. Then a crack classification algorithm based on interleaved low-rank group convolution hybrid deep network (ILGCHDN) is proposed to recognize cracks and non-cracks. Next, we propose a crack image binarization architecture called SegNet-DCRF, which fuses the SegNet and the dense condition random field (DCRF). Finally, calculate the unidirectional crack width and the web crack area. Moreover, an interactive crack detection software is developed to further display various information on results. Experimental results show that our model is superior to other state-of-the-art algorithms in terms of accuracy, parameters, speed, and anti-interference ability. Also, for cracks with width of 3 mm or more, the relative error is less than 0.02, which better achieves the actual engineering requirements. Besides, the area of web cracks is calculated to comprehensively evaluate the pavement damage level.

Vehicle sideslip angle is one of the important indicators to determine whether vehicles are stable and is also a significant parameter for vehicle stability control. However, it is almost impossible to be measured directly without complex and expensive sensors or devices. Soft measurements are therefore generally adopted to estimate vehicle sideslip angle on the basis of easily observed physical quantities. This paper proposes an estimation method of sideslip angle based on steering torque instead of steering angle, for the fact that steering torque signal has more rapid and direct response compared with that obtained from steering angle. The frequency is analyzed between steering torque-sideslip angle and steering angle-sideslip angle transfer functions, and the extended Kalman filter (EKF) for vehicle sideslip angle based on steering torque is proposed. Both simulation results and vehicle tests prove the validity of our estimating method, indicating that the proposed method based on steering torque exhibits better accuracy and more rapid response in extreme region than the one based on steering angle.

The main objective of this paper is to analysis the influence of mixing approaches on the properties of the conventional concrete (CC) and interlocking-dense concrete (IDC). Compared with the normal mixing approach (NMA), the two-stage mixing approaches (TSMA, including mortar mixed first (MMF), coarse aggregate wrapped with paste (CAWP), aggregate wrapped with cement (AWC)) were employed. The mixing homogeneity tests were conducted to evaluate the uniform of the fresh concrete with different mixing approaches. Moreover, compressive strength, flexible-tensile strength, flexible modulus and abrasion resistance tests were designed to evaluate the performance of the hardened concrete. Pearson correlation analysis and analysis of variance (ANOVA) was also involved to illustrate the correlation and differences between NMA and TSMA. The repercussion in the mixing homogeneity test shows that Δ G is a reasonable index to evaluate the uniformity of fresh concrete. This also reveals that the uniformity can be improved by TSMA for CC, while the MMF is the most effective approach to enhance the uniformity for IDC. The results help to find out that the most appropriate mixing approaches for CC and IDC are CAWP and MMF, respectively.

A simplified three-degree-of-freedom dynamic model with nonlinear friction torque and engine torque excitation, capable of identifying the effect of the engine excitation on clutch judder, is presented. The analysis of harmonic order is performed and a sinusoidal contact pressure between friction surfaces is considered, along with an analytical solution for the relative angular velocity of the clutch plates. The average fluctuation amplitude of the clutch relative angular velocity is used to evaluate the judder. Numerical calculations indicate that the clutch judder increases significantly when the angular velocity of the crankshaft, corresponding to the harmonic orders of the engine, is equal or close to the natural frequency of the driveline. An identical frequency of the engine excitation and the oil pressure fluctuation contributes little to the clutch judder, unless the excitation is at or near the resonance frequency. The amplitudes of oscillations due to the engine excitation grow when the pulsating torque of the engine increases. The mean torque of the engine has little influence on the judder, although it governs the clutch engagement time. The results further show that clutch judder attenuates as the torsional stiffness of the system increases.

This paper presents an investigation of the hot judder phenomenon of multidisc clutches, which takes place during the engagement process. Depending on the results of finite element analysis, a pressure distribution function is defined and a contact pressure equation is established to demonstrate the non-uniformity of the contact pressure distribution on the friction interfaces due to frictional heat. The relationship between the coefficient of friction and the temperature is analyzed. A 4 degrees of freedom power-train model is developed to evaluate the clutch judder behavior. The paper indicates that the clutch judder is influenced by the non-uniformity of the interface contact pressure distribution, which is excited by frictionally induced thermal load. The non-uniform contact pressure distributions along the radial direction have a slight influence on the clutch judder, while the uneven contact pressure distributions along the circumference contribute to the judder substantially. Furthermore, the results in this work can be used to study the operation instability and the thermal failure of clutches.

The transient thermal and thermoelastic analysis of an automotive multidisc clutch is performed to investigate the thermal deformation and the contact pressure distribution of the friction surfaces. An alternating finite volume method (FVM) and finite element method (FEM) for temperature distribution and contact pressure distribution, including the coupling between temperature, displacement, and contact pressure, is employed in MATLAB. A penalty function method is used to impose the constraints on the contact boundary. The computational results indicate that the contact pressure distributions on the surfaces near the pressure plate are more uniform than those near the opposite plate, and the nonuniformity is enlarged with the temperature rise. The contact pressure and temperature distributions on the two friction surfaces of the separate plate are dissimilar and asymmetric, which is in good agreement with the test results. The results further show that the contact pressure peaks diminish and the n...

Based on the rigid-plastic finite element method (FEM), the shear stress field of deformation region for high manganese austenite steel during hot asymmetrical rolling process was analyzed. The influences of rolling parameters, such as the velocity ratio of upper to lower rolls, the initial temperature of workpiece and the reduction rate, on the shear deformation of three nodes in the upper, center and lower layers were discussed. As the rolling parameters change, distinct shear deformation appears in the upper and lower layers, but the shear deformation in the center layer appears only when the velocity ratio is more than 1.00, and the absolute value of the shear stress in this layer is changed with rolling parameters. A mathematical model which reflected the change of the maximal absolute shear stress for the center layer was established, by which the maximal absolute shear stress for the center layer can be easily calculated and the appropriate rolling technology can be designed.

To overcome the adverse effects of conventional chemotherapy for cancers, various nanoparticles based drug delivery systems have been developed. However, nanoparticles delivering drugs directly to kill tumor cells still faced with challenges, because tumors possessed adopt complex mechanism to resist damages, which compromised the therapeutic efficacy. TMEM16A/CaCCs (Calcium activates chloride channels) has been identified to be overexpressed in lung adenocarcinoma which can serve as a novel tumor specific drug target in our previous work. Here, we developed a novel dual-targeted antitumor strategy via designing a novel nano-assembled, pH-sensitive drug-delivery system loading with specific inhibitors of TMEM16A against lung adenocarcinoma. For validation, we assayed the novel dual-targeting therapy on xenograft mouse model which exhibited significant antitumor activity and not affect mouse body weight. The dual targeting therapy accomplished in this study will shed light on the development of advanced antitumor strategy.

This study aims to establish an elasto-plastic constitutive model under repeated loads for unbound granular materials (UGM) used in the pavement. Shakedown theory was employed to analyze the deformation behavior of UGM at different stages. The prediction models of plastic strain rate and resilient strain were fitted with the loading repetitions, axial stress, and other factors. Based on the typical stress-strain curves and the models of plastic strain rate and resilient strain, the yield point model was established using the criterion of spatially mobilized plane (SMP). Using this model, the yield stress was obtained conveniently at certain loading conditions, which provided the movement path of yield surface under repeated loads based on kinematic hardening law. Under single loading, the plastic strain hardening was treated as the isotropic hardening. This simplified method made it feasible to establish the elasto-plastic constitutive model under repeated loads. The proposed model was verified with the measured deformation of mixed-size aggregates using precision unbound material analyzer (PUMA) tests. The comparsions of permanent strain and plastic strain rate obtained from laboratory tests and numerical simulation indicated that the proposed elasto-plastic constitutive model can characterize the deformation behavior of UGM.

Phase change material, PCM, which is a new type of additive for pavement engineering, has been studied for regulating the working temperature of pavement structure. By storing and releasing thermal energy via phase change, PCM is able to maintain the temperature of pavement structure in a relatively narrow range. Current researches on PCM mainly focus on its preparation, mechanism, and testing, while few researches have been conducted on the evaluation of PCM’s temperature regulation effect. This research however, established an asphalt pavement model with and without PCM respectively by applying finite element method (FEM), and compared the two patterns’ thermal regulation ability in a detailed way. Influence factors of asphalt pavement temperature field were classified into external and internal factors. The boundary conditions were determined based on in-situ meteorological data. The effects of PCM on temperature of asphalt pavement were evaluated. Latent heat accumulated temperature value (LHATV) and latent heat thermoregulation index (LHTI) were selected to evaluate the latent heat effect. Results showed that different layers of surface course constructed with PCM had different thermal regulation ability. Other factors like temperature difference in a day and whether to add PCM can also make a difference. The proposed LHATV and LHTI were good indices and assessment for PCM’s function. The results of the present study can lay the foundation for calculating the temperature field of pavement structure constructed with PCM and for investigating the application of PCM in pavement engineering.

Accurate monitoring of electrical equipment temperature can effectively ensure the safety of electrical equipment operation. It is worth further study to use limited temperature monitoring equipment to obtain a comprehensive temperature distribution of electrical equipment. Based on the study of nearest neighbor interpolation bilinear interpolation and bicubic interpolation these three methods are applied to image processing of temperature distribution in 10kV switchgear cabinets. Compared with the three methods, bilinear interpolation and bicubic interpolation can accurately reflect the non-measured temperature, and can be used in image processing of temperature distribution in high-voltage switchgear temperature measurement system.

The multi-disc wet clutch is widely used in high-power vehicles and large construction machinery due to its capacity for high torque transfer and compact efficiency. However, the early-stage failure such as the buckling of the friction disk is hard to detect since its external performance is not obvious, which can impair the reliability of the system largely. This paper presents an online fault diagnosis method for wet clutch based on vibration signal analysis to detect the deformation of the friction components. A bench test is designed to acquire the vibration signal of the clutch pack with four different fault levels. Then an online monitoring method for clutch early fault based on wavelet analysis and EMD algorithm is proposed. The analysis shows that the method can extract the abnormal vibration characteristics and shock signals caused by the local deformation of the clutch pair, which can be feasible in the engineering practice of online fault diagnosis for multi-disc clutch early failure.

Aiming at the difficulty of feature extraction for gear fault diagnosis and the problem of traditional classification methods cannot diagnose the faults in wind turbine gearboxes adaptively, a new fault diagnosis method based on ensemble empirical mode decomposition (EEMD) energy entropy and SOM neural networks (SOM-NN) is proposed. Firstly, the EEMD method is used to decompose the original vibration signal of the gear under all kinds of condition into several intrinsic mode functions (IMF) and calculate the energy value of each IMF and the energy entropy of the signal. Then the IMF energy proportion and the signal energy entropy are selected to form a set of features which can reflect the fault vibration signal. The values of these features are inputted to SOM neural network for classification. The numerical simulation results show that the accuracy of the method is 100% in the fault diagnosis of wind turbine gearbox.

The objective of this study is to optimize the diesel injection parameters, operating parameters and the combustion system of a diesel/natural gas fueled dual fuel engine simultaneously by applying the genetic algorithm. The indicated specific fuel consumption, NOx and CH4 emissions are selected as the optimization objectives. It is shown that almost all the solutions can meet the EURO VI emission limit of the Soot. However, only partial solutions satisfy the EURO V NOx emission limit. More open combustion chambers are chosen than the re-entrant combustion chamber on the Pareto front. A wide range distribution of the combustion bowl radius is found on the Pareto front. However, the diesel fuel spray angle, swirl ratio, EGR (Exhaust Gas Recirculation) rate and compression ratio are gradually concentrated near several optimal points. The response surfaces of ISFC, CH4 and NOx emissions for injection timing vs. EGR rate and spray angle vs. swirl ratio for all the calculated Pareto citizens are achieved.