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Статті в журналах з теми "EPBS machine"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Wan, Chaodong, and Zhiyi Jin. "Adaptability of the Cutter-Head of the Earth Pressure Balance (EPB) Shield Machine in Water-Rich Sandy and Cobble Strata: A Case Study." Advances in Civil Engineering 2020 (November 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/8847982.

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Earth Pressure Balance (EPB) shield machines are considered to be the most efficient tunneling method for Metro tunnels due to their adaptability to a great variety of ground conditions, higher construction efficiency, and providing a safer working environment. There are many guidelines available for EPB shield machine selection. However, these guidelines are very general and cannot be used directly for an upcoming project. This paper takes Chengdu Metro Line 6 in China as the engineering background; the studied area is typical of a water-rich sandy and cobble stratum with high content of cobble. Three types of EPBs in the two continuous intervals exhibit significant differences in performance and encounter many difficulties such as wear of the cutter disc and tools, clogging, and severe surface settlement during the operation. These difficulties prevent the construction efficiency, increase the cost of the project, and cause delays in construction period. The causes of these difficulties are summarized by recording and comparing the operational parameters of the three types of EPBs. These parameters that are summarized include the advance rate, total thrust, torque, and the rate of rotation of the cutter-head. In addition, the surface settlements are also compared. The results indicate that the opening rate, maximum opening size, and the opening position of the cutter-head are key factors that affected the geological adaptability of the shield machine in water-rich sandy and cobble strata. Among the three factors, the maximum opening size and opening position are the most important factors influencing the strata adaptability of the cutter-head. To avoid frequent jams of the cutter-head, the maximum torque should be not less than 6,500 kNm. The maximum opening size should not be less than 420 mm × 420 mm. The effect of increasing the central opening of the cutter-head is that large cobbles and boulders can be discharged through the central opening when they cannot be discharged through the opening near the original position of the cobbles and boulders. This paper provides specific guidance on the selection of cutter-head for shield machines in water-rich sandy and cobble strata.

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2

Koohsari, Ali, Roohollah Kalatehjari, Sayfoddin Moosazadeh, Mohsen Hajihassani, and Bao Van. "A Critical Investigation on the Reliability, Availability, and Maintainability of EPB Machines: A Case Study." Applied Sciences 12, no. 21 (November 6, 2022): 11245. http://dx.doi.org/10.3390/app122111245.

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Tunnelling is a vital geotechnical engineering feature of underground transportation systems that is potentially hazardous if not properly investigated, studied, planned, and executed. A reliability, availability, and maintainability (RAM) analysis is one of the main practical techniques in machinery-based projects to recognize the failure and repair rates of machines during or after their operations. RAM analysis of mechanized tunneling can help to manage the project safety and cost, and improve the availability and performance of the machine. There are several methods to obtain and predict the RAM of a system, including the Markov chain simulation and other statistical methods; however, the result of the analysis can be affected by the selected method. This paper presents the results of a critical investigation on the RAM of the Earth pressure balance machines (EPBMs) used in developing an urban metro project in Isfahan, Iran. The five kilometer length of the first line of the Isfahan metro project was excavated using EPBMs over four years. After overhauling the EPBMs and making some minor changes, excavation of the second line started, and to date, about 1.2 km has been excavated by the refurbished machines. In the present study, a RAM analysis has been applied to electrical, mechanical, and cutter head subsystems of the EPBMs in Lines 1 and 2 of the Isfahan metro project over an 18- and 7-month period of machine operation, respectively. The results show that the estimated availability, A(t), determined by the Markov method, is closer to reality but cannot be propagated to reliability R(t) and maintainability M(t) analysis. It was also revealed that by predicting the required maintenance and proper planning, the overall availability of the EPBM was improved from 45% in Line 1 to 61% in Line 2. The outcomes of this study can be used in the future planning of urban tunneling projects to estimate machine, staff, and logistic performance with the least possible error, and appropriately arrange the factors involved in the system.

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3

Wan, M. S. P., J. R. Standing, D. M. Potts, and J. B. Burland. "Measured short-term subsurface ground displacements from EPBM tunnelling in London Clay." Géotechnique 67, no. 9 (September 2017): 748–79. http://dx.doi.org/10.1680/jgeot.sip17.p.148.

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Subsurface ground displacements from the construction of the twin-bore Crossrail tunnels in London Clay by earth pressure balance machines (EPBMs) are presented and discussed, complementing a companion paper by the authors that focused on the surface response. Both papers report vertical and horizontal displacements, in this case measured using comprehensive arrays of instruments installed within boreholes in Hyde Park, London. The Crossrail tunnels are deeper than those cited in most UK case histories concerning tunnelling in stiff clay. Clear insights were gained into subsurface displacement mechanisms: an ‘inward’ displacement field was observed around the Crossrail tunnel construction, in contrast to the ‘outward’ displacement field that developed around the shallower Channel Tunnel Rail Link tunnels constructed east of London using similar EPBMs in London Clay. This has important implications when estimating subsurface displacements using currently available empirical methods. Appraisal of the EPBM operational variables suggests that the relative magnitude of face and tail grout pressures to overburden stress is the key factor contributing to the opposing senses of the observed displacement fields. Earlier tunnelling-induced strain softening of the London Clay is evident from greater subsurface incremental volume losses and settlement trough width parameters relating to subsequent tunnel construction.

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4

Zhou, Jianguo, Renyang Liu, Zifeng Wu, Jintao Zhang, and Junhui Liu. "Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions." E3S Web of Conferences 218 (2020): 03046. http://dx.doi.org/10.1051/e3sconf/202021803046.

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How to discriminate distal regulatory elements to a gene target is challenging in understanding gene regulation and illustrating causes of complex diseases. Among known distal regulatory elements, enhancers interact with a target gene’s promoter to regulate its expression. Although the emergence of many machine learning approaches has been able to predict enhancer-promoter interactions (EPIs), global and precise prediction of EPIs at the genomic level still requires further exploration.In this paper, we develop an integrated EPIs prediction method, called EpPredictor with improved performance. By using various features of histone modifications, transcription factor binding sites, and DNA sequences among the human genome, a robust supervised machine learning algorithm, named LightGBM, is introduced to predict enhancer-promoter interactions (EPIs). Among six different cell lines, our method effectively predicts the enhancer-promoter interactions (EPIs) and achieves better performance in F1-score and AUC compared to other methods, such as TargetFinder and PEP.

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5

Wei, Yingjie, Duli Wang, Jianguang Li, and Yuxin Jie. "Effects of Soil Conditioning on Characteristics of a Clay-Sand-Gravel Mixed Soil Based on Laboratory Test." Applied Sciences 10, no. 9 (May 9, 2020): 3300. http://dx.doi.org/10.3390/app10093300.

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Soil conditioning is of great significance for tunneling in soft ground with earth pressure balance shield (EPBS) machines, which leads to safe, highly efficient, and high-quality tunneling. To study the effects of soil conditioning on properties of a clay-sand-gravel mixed soil encountered in an EPBS tunneling project in Beijing, a series of geotechnical test methods was carried out based on laboratory test in this paper. The decay behaviors of foam particles generated by the Waring-Blender method were studied first using the image particle analysis system, and then the feasibility of soil conditioning on the mixed soil was qualitatively and quantitatively assessed through the mixing test, slump test, and friction coefficient test. The preliminary test results indicate that drainage of water in liquid film plays an important role in the decay of foam microstructure. The viscosity and flowability of the conditioned soil were modified dramatically by using various amount of water and foam, and a suitable state meeting the requirement of EPBS machines was obtained. The net power and mixing time, which reflects the interaction between the blending rods and tested soils, as well as the slump value, providing the overall indication for liquidity of the conditioned soils, and friction coefficient, reflecting the friction between steel and tested soils, were used to provide insight into the variation in viscosity and flowability of the tested soils.

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6

Mariño, José B., Rafael E. Banchs, Josep M. Crego, Adrià de Gispert, Patrik Lambert, José A. R. Fonollosa, and Marta R. Costa-jussà. "N-gram-based Machine Translation." Computational Linguistics 32, no. 4 (December 2006): 527–49. http://dx.doi.org/10.1162/coli.2006.32.4.527.

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This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS).

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7

Jung. "Correlation between the EPB shield TBM machine data and the ground condition." Journal of Korean Tunnelling and Underground Space Association 16, no. 6 (2014): 543. http://dx.doi.org/10.9711/ktaj.2014.16.6.543.

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8

Zhang, Qian, Kaihong Yang, Lihui Wang, and Siyang Zhou. "Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines." Mathematical Problems in Engineering 2020 (April 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/3057893.

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Анотація:

At present, many large-scale engineering equipment can obtain massive in-situ data at runtime. In-depth data mining is conducive to the real-time understanding of equipment operation status or recognition of service environment. This paper proposes a geological type recognition system by the analysis of in-situ data recorded during TBM tunneling to address geological information acquisition during TBM construction. Owing to high dimensionality and nonlinear coupling between parameters of TBM in-situ data, the dimensionality reduction feature engineering and machine learning methods are introduced into TBM in-situ data analysis. The chi-square test is used to screen for sensitive features due to the disobedience to common distributions of TBM parameters. Considering complex relationships, ANN, SVM, KNN, and CART algorithms are used to construct a geology recognition classifier. A case study of a subway tunnel project constructed using an earth pressure balance tunnel boring machine (EPB-TBM) in China is used to verify the effectiveness of the proposed geological recognition method. The result shows that the recognition accuracy gradually increases to a stable level with the increase of input features, and the accuracy of all algorithms is higher than 97%. Seven features are considered as the best selection strategy among SVM, KNN, and ANN, while feature selection is an inherent part of the CART method which shows a good recognition performance. This work provides an intelligent path for obtaining geological information for underground excavation TBM projects and a possibility for solving the problem of engineering recognition of more complex geological conditions.

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9

Ceballos, Gerardo A., Luis F. Hernandez, Daniel Paredes, Luis R. Betancourt, and Midhat H. Abdulreda. "A machine learning approach to predict pancreatic islet grafts rejection versus tolerance." PLOS ONE 15, no. 11 (November 5, 2020): e0241925. http://dx.doi.org/10.1371/journal.pone.0241925.

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The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

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10

Morón, Carlos, Enrique Tremps, Puerto Ramirez, Alfonso Garcia, and Jose Andrés Somolinos. "Performance Optimization in Switched Reluctance Motor Drives." Key Engineering Materials 495 (November 2011): 67–70. http://dx.doi.org/10.4028/www.scientific.net/kem.495.67.

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In this paper, switched reluctance motors (SRM) are proposed as an alternative for electric power assisted steering (EPAS) applications. A prototype machine has been developed as very attractive design for a steering electric motor, both from a cost and size perspective. A four-phase 8/6 SRM drive is designed for a rack type EPAS which should provide a maximum force of 10 kN. Two-dimension finite element analysis is used to validate the design.

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11

Selemetas, D., and J. R. Standing. "Response of full-scale piles to EPBM tunnelling in London Clay." Géotechnique 67, no. 9 (September 2017): 823–36. http://dx.doi.org/10.1680/jgeot.sip17.p.126.

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The installation and working test performance of four full-scale instrumented driven piles and their subsequent response to twin tunnels constructed below the pile bases are described. One pair was designed to be largely friction piles and the other pair end-bearing. Their locations relative to the new tunnels were carefully chosen to optimise understanding of pile responses at varying offsets from the centre-lines. The site conditions and the greenfield response to earth pressure balance machine tunnelling at the site were described in a companion paper that reported an expanding displacement field around the tunnels rather than the contracting fields usually observed. The field monitoring results indicated that, during construction, zones of influence existed around tunnels, where the ground and piles were subjected to different degrees and senses of relative vertical displacement. Redistributions of load along the pile lengths occurred as the tunnel boring machines approached, passed beneath and continued beyond the pile bases; lateral pile deflections and bending moments were also induced. Based on the results from this field study, implications for the capacity of existing piles (and design of new piles) subjected to tunnelling-induced movements are assessed for cases of expanding and contracting displacement fields.

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12

Bárcena, Elena, and Tim Read. "Principles of sublanguage theory and their relevance to machine translation." Epos : Revista de filología, no. 16 (January 1, 2000): 351. http://dx.doi.org/10.5944/epos.16.2000.10157.

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13

Oliveira, Daniela Garroux G., Markus Thewes, and Mark S. Diederichs. "EPB machine excavation of mixed soils – Laboratory characterisation." Geomechanics and Tunnelling 12, no. 4 (August 2019): 373–85. http://dx.doi.org/10.1002/geot.201900014.

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14

Seo, Junghwan, and Daehie Hong. "3D Cutting Machine of EPS Foam for Manufacturing Free-Formed Concrete Mold." Journal of the Korean Society for Precision Engineering 34, no. 1 (January 1, 2017): 35–39. http://dx.doi.org/10.7736/kspe.2017.34.1.35.

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15

Zhuang, Zhong, Xiaotong Shen, and Wei Pan. "A simple convolutional neural network for prediction of enhancer–promoter interactions with DNA sequence data." Bioinformatics 35, no. 17 (January 14, 2019): 2899–906. http://dx.doi.org/10.1093/bioinformatics/bty1050.

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Abstract Motivation Enhancer–promoter interactions (EPIs) in the genome play an important role in transcriptional regulation. EPIs can be useful in boosting statistical power and enhancing mechanistic interpretation for disease- or trait-associated genetic variants in genome-wide association studies. Instead of expensive and time-consuming biological experiments, computational prediction of EPIs with DNA sequence and other genomic data is a fast and viable alternative. In particular, deep learning and other machine learning methods have been demonstrated with promising performance. Results First, using a published human cell line dataset, we demonstrate that a simple convolutional neural network (CNN) performs as well as, if no better than, a more complicated and state-of-the-art architecture, a hybrid of a CNN and a recurrent neural network. More importantly, in spite of the well-known cell line-specific EPIs (and corresponding gene expression), in contrast to the standard practice of training and predicting for each cell line separately, we propose two transfer learning approaches to training a model using all cell lines to various extents, leading to substantially improved predictive performance. Availability and implementation Computer code is available at https://github.com/zzUMN/Combine-CNN-Enhancer-and-Promoters. Supplementary information Supplementary data are available at Bioinformatics online.

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16

Kilic, Kursat, Hisatoshi Toriya, Yoshino Kosugi, Tsuyoshi Adachi, and Youhei Kawamura. "One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction." Applied Sciences 12, no. 5 (February 25, 2022): 2410. http://dx.doi.org/10.3390/app12052410.

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An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s.

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17

Xiao, Mengli, Zhong Zhuang, and Wei Pan. "Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks." Genes 11, no. 1 (December 29, 2019): 41. http://dx.doi.org/10.3390/genes11010041.

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Enhancer-promoter interactions (EPIs) are crucial for transcriptional regulation. Mapping such interactions proves useful for understanding disease regulations and discovering risk genes in genome-wide association studies. Some previous studies showed that machine learning methods, as computational alternatives to costly experimental approaches, performed well in predicting EPIs from local sequence and/or local epigenomic data. In particular, deep learning methods were demonstrated to outperform traditional machine learning methods, and using DNA sequence data alone could perform either better than or almost as well as only utilizing epigenomic data. However, most, if not all, of these previous studies were based on randomly splitting enhancer-promoter pairs as training, tuning, and test data, which has recently been pointed out to be problematic; due to multiple and duplicating/overlapping enhancers (and promoters) in enhancer-promoter pairs in EPI data, such random splitting does not lead to independent training, tuning, and test data, thus resulting in model over-fitting and over-estimating predictive performance. Here, after correcting this design issue, we extensively studied the performance of various deep learning models with local sequence and epigenomic data around enhancer-promoter pairs. Our results confirmed much lower performance using either sequence or epigenomic data alone, or both, than reported previously. We also demonstrated that local epigenomic features were more informative than local sequence data. Our results were based on an extensive exploration of many convolutional neural network (CNN) and feed-forward neural network (FNN) structures, and of gradient boosting as a representative of traditional machine learning.

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18

Majumder, Mainak, and Priyanka Ghosh. "Active screening for axi-symmetric machine loading using EPS geofoam." Japanese Geotechnical Society Special Publication 2, no. 66 (2016): 2238–43. http://dx.doi.org/10.3208/jgssp.ind-04.

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19

Chen, Li Feng, Xiao Ling Wu, and Da Tong Qin. "Strength Analysis of Planetary Trains’ Carrier of EPB Shield Machine." Advanced Materials Research 97-101 (March 2010): 3303–7. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3303.

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The method to analysis the strength of planetary trains’ carriers of EPB(earth pressure balance) shield machine is presented in this paper. The structure of the shield machine trains is analyzed and the 3-D solid model of the carrier is built with Pro/E. After the load on the carrier has been dealt with, the strength of carrier is calculated by means of finite element method. The results via ANSYS show that the max stress and strain on the carriers are increasing as the increasing load on it. The max stress is lying on the joint point of the carrier and planetary gear shaft because of the bending deformation of the shaft.

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20

Shi, Hu, Huayong Yang, Guofang Gong, and Lintao Wang. "Determination of the cutterhead torque for EPB shield tunneling machine." Automation in Construction 20, no. 8 (December 2011): 1087–95. http://dx.doi.org/10.1016/j.autcon.2011.04.010.

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21

Feng, Yixiong, Yong Wang, Hao Zheng, Shanghua Mi, and Jianrong Tan. "A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks." Sensors 18, no. 8 (August 7, 2018): 2591. http://dx.doi.org/10.3390/s18082591.

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Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

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22

Elger, Kirsten, Geertje ter Maat, Rita Caldeira, Corrado Cimarelli, Fabio Corbi, Stephane Dominguez, Martin Drury, et al. "The EPOS Multi-Scale Laboratories: A FAIR Framework for stimulating Open Science practice across European Earth Sciences Laboratories." Annals of Geophysics 65, no. 3 (July 12, 2022): DM318. http://dx.doi.org/10.4401/ag-8790.

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The Multi-scale Laboratories (MSL) are a network of European laboratories bringing together the scientific fields of analogue modeling, paleomagnetism, experimental rock and melt physics, geo- chemistry and microscopy. MSL is one of nine (see below) Thematic Core Services (TCS) of the European Plate Observing System (EPOS). The overarching goal of EPOS is to establish a compre- hensive multidisciplinary research platform for the Earth sciences in Europe. It aims at facilitating the integrated use of data, models, and facilities, from both existing and new distributed pan European Research Infrastructures, allowing open access and transparent use of data. The TCS MSL network allows researchers to collaborate with other labs and scientists. By becoming part of the rapidly growing TCS MSL network, new laboratories are offered a platform to showcase their research data output, laboratory equipment and information, and the opportunity to open laboratories to guest researchers through the Transnational Access (TNA) program. The EPOS Multi-scale laboratories offer researchers a fully operational data publication chain tailored to the specific needs of laboratory research, from a bespoke metadata editor, through dedi cated, (domainspecific) data repositories, to the MSL Portal showcasing these citable data publica- tions. During this process the data publications are assigned with digital object identidiers (DOI), published with open licenses (e.g. CC BY 4.0) and described with standardized and machine-read- able rich metadata (following the FAIR Principles to make research data Findable, Accessible, Interoperable and Reusable. The TCS MSL is currently working on linking these data publications to the EPOS Central Portal1, the main discovery and access point for European multi-disciplinary data, and on increasing the number of connected data repositories.

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23

Attanasio, Antonio, Marco Piscitelli, Silvia Chiusano, Alfonso Capozzoli, and Tania Cerquitelli. "Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates." Energies 12, no. 7 (April 2, 2019): 1273. http://dx.doi.org/10.3390/en12071273.

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Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

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Song, Shuguang, Ruizhong Tian, Liping Li, Jie Hu, Chunsheng Shen, and Shaozhong Peng. "Adaptability Study of EPB Shield Machine in Clay Stratum in Xuzhou." Geotechnical and Geological Engineering 37, no. 4 (November 23, 2018): 2335–41. http://dx.doi.org/10.1007/s10706-018-00759-z.

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25

Wang, Lintao, Guofang Gong, Hu Shi, and Huayong Yang. "Modeling and analysis of thrust force for EPB shield tunneling machine." Automation in Construction 27 (November 2012): 138–46. http://dx.doi.org/10.1016/j.autcon.2012.02.004.

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26

Wang, Weizheng, Huiya Tang, Jinping Li, and Yukun Hou. "Spatial-Temporal Variations of Extreme Precipitation Characteristics and Its Correlation with El Niño-Southern Oscillation during 1960–2019 in Hubei Province, China." Atmosphere 13, no. 11 (November 18, 2022): 1922. http://dx.doi.org/10.3390/atmos13111922.

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Extreme precipitation could result in many disasters, such as floods, drought, and soil erosion, further bringing severe economic loss. Based on the daily precipitation records during 1960–2019 of 26 stations obtained from the National Meteorological Science Data Center of China, 10 extreme precipitation indices (EPIs: annual total precipitation (PRCPTOT), max-1-day precipitation amount (RX1day), max-5-day precipitation amount (RX5day), number of heavy rain days (R10), number of very heavy rain days (R10), simple daily intensity index (SDII), consecutive dry days (CDD), continued wet days (CWD), very wet days (R95p) and extremely wet days (R99p)) were chosen and used to analyze the spatial-temporal variation of extreme precipitation within Hubei province, China, which is an important industrial and agricultural base in China. Finally, the correlation between El Niño-Southern Oscillation and EPIs was analyzed by cross-wavelet analysis. Results showed that the annual EPIs varied obviously during 1960–2019, and CWD decreased significantly (p < 0.05). The chosen EPIs were higher in eastern and southwestern Hubei compared to other regions, and RX1day, RX5day, R95p, and R99p were increased in most regions. The spatial-temporal variations of spring and summer EPIs were more obvious than those on an annual scale. In summer, all EPIs except CDD should increase in the near future. More attention should be paid to Wuhan, Enshi, and Macheng, where the RX1day, RX5day, R95p, and R99p will increase in these regions. Finally, the RX1day and R10 were positively correlated with MEI (p < 0.05), while the RX5day, CDD, CWD, and R99p were negatively correlated with MEI (p < 0.05). The extreme precipitation events within Hubei were affected by the El Niño-Southern Oscillation. The results could provide a possible driving factor for precipitation prediction and natural hazard prevention within Hubei province, China.

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27

Alves Torrano, José Antonio. "O jogo de aparências e de opiniões na tragédia Helena de Eurípides." Epos : Revista de filología, no. 26 (January 1, 2010): 13. http://dx.doi.org/10.5944/epos.26.2010.10632.

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A análise hermenêutica permite-nos ver como, na tragédia Helena de Eurípides, o desenvolvimento temático se dá mediante o jogo de aparências e de opiniões, como o segundo estásimo se integra motivada e perfeitamente a essa tragédia, e como a dialética trágica prepara e integra a cena da epifania do Deus ex machina e os versos finais do coro – comuns a outras quatro tragédias de Eurípides

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28

Fu, Jie, Yimin Xia, Ling Zhang, Laikuang lin, Xiaoyun Zhang, and Shifang Zhao. "Failure analysis on the fracture of disc cutter in EPB shield machine." Engineering Failure Analysis 108 (January 2020): 104341. http://dx.doi.org/10.1016/j.engfailanal.2019.104341.

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29

Comito, Tiziana, Colm Clancy, Conor Daly, and Alan Hally. "Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools." Advances in Science and Research 18 (September 17, 2021): 145–56. http://dx.doi.org/10.5194/asr-18-145-2021.

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Abstract. Convection-permitting weather forecasting models allow for prediction of rainfall events with increasing levels of detail. However, the high resolutions used can create problems and introduce the so-called “double penalty” problem when attempting to verify the forecast accuracy. Post-processing within an ensemble prediction system can help to overcome these issues. In this paper, two new up-scaling algorithms based on Machine Learning and Statistical approaches are proposed and tested. The aim of these tools is to enhance the skill and value of the forecasts and to provide a better tool for forecasters to predict severe weather.

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30

Latune, Camille L., Ilya Sinayskiy, and Francesco Petruccione. "Roles of quantum coherences in thermal machines." European Physical Journal Special Topics 230, no. 4 (April 12, 2021): 841–50. http://dx.doi.org/10.1140/epjs/s11734-021-00085-1.

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31

Lin, Song-Shun, Shui-Long Shen, Ning Zhang, and Annan Zhou. "Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms." Geoscience Frontiers 12, no. 5 (September 2021): 101177. http://dx.doi.org/10.1016/j.gsf.2021.101177.

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32

Hong, Kairong, Fengyuan Li, Zhenjian Zhou, Feng Li, and Xunlin Zhu. "A Data-Driven Method for Predicting the Cutterhead Torque of EPB Shield Machine." Discrete Dynamics in Nature and Society 2021 (October 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/5980081.

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The prediction of cutterhead torque of earth pressure balance (EPB) shield machine is mainly studied. First, the idea of shield tunneling stage division is proposed. The process of shield tunneling from start to stop (or pause) is divided into start-up and stationary driving stages. Using the change point detection method based on linear regression, the separation points between start-up stage and stationary driving stage are identified from the original construction data, and the datasets of the two stages are extracted, respectively. Then, for the start-up stage, the linear regression method is suggested for the cutterhead torque prediction, since there is a strong linear correlation between the key parameters such as the cutterhead torque and the thrust force. Meanwhile, for the stationary driving stage, considering the fact that the key parameters vary smoothly and show obvious inertia, the long short-term memory (LSTM) network method can be used to establish the relationship model between cutterhead torque and other key parameters, such as the thrust force. Through the test experiments of construction data in Zhengzhou, Luoyang, and Dalian shield projects, the results show that the proposed segmented modeling method possesses good adaptability and the cutterhead torque prediction model has high prediction accuracy.

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Brodey, B. B., R. R. Girgis, O. V. Favorov, J. Addington, D. O. Perkins, C. E. Bearden, S. W. Woods, et al. "The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning." Schizophrenia Research 197 (July 2018): 516–21. http://dx.doi.org/10.1016/j.schres.2017.11.030.

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34

Wu, Li, Tianmin Guan, and Lei Lei. "Discrete element model for performance analysis of cutterhead excavation system of EPB machine." Tunnelling and Underground Space Technology 37 (August 2013): 37–44. http://dx.doi.org/10.1016/j.tust.2013.03.003.

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35

刘, 发展. "Failure Analyse and Improvement Measures for Centre Rotation Component of EPB Sheild Machine." Mechanical Engineering and Technology 06, no. 03 (2017): 250–58. http://dx.doi.org/10.12677/met.2017.63031.

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36

Tang, Xiaoqiang, Kongshu Deng, Liping Wang, and Xu Chen. "Research on natural frequency characteristics of thrust system for EPB machines." Automation in Construction 22 (March 2012): 491–97. http://dx.doi.org/10.1016/j.autcon.2011.11.008.

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37

Gopalakrishnan, Abinaya, Revathi Venkataraman, Raj Gururajan, Xujuan Zhou, and Guohun Zhu. "Predicting Women with Postpartum Depression Symptoms Using Machine Learning Techniques." Mathematics 10, no. 23 (December 2, 2022): 4570. http://dx.doi.org/10.3390/math10234570.

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Being pregnant and giving birth are big life stages that occur for women. The physical and mental effects of pregnancy and childbirth, like those of many other fleeting life experiences, have the significant potential to influence a mother’s overall health and well-being. They have also been known to trigger Postpartum Depression (PPD) in many cases. PPD can be exhausting for the mother and it may have a negative impact on her capacity to care for herself and her kid if it is not treated. For this reason, in this study, initially, physiological questionnaire Edinburgh Postnatal Depression Scale (EPDS) data were collected from delivered mothers for one week, the score was evaluated by medical experts, and participants with PDD symptoms were identified. As a part of multistage progress, further, follow-up was carried out by collecting the Patient Health Questionnaire-9 (PHQ-9), Postpartum Depression Screening Scale (PDSS) questionnaires for the above-predicted participants until six weeks. As the second step, correlated risk factors with PPD symptoms were identified using statistical analysis. Finally, data were analyzed and used to train and test machine learning algorithms in order to predict postpartum depression from one to six weeks. The extremely Randomized Trees (XRT) algorithm with (Background Information + PHQ-9 + PDSS) data offers the most accurate and efficient prediction. Pregnant women with these features could be identified and treated properly. Moreover, it reduces prolonged complications and remains cost-effective in future clinical models.

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38

Saha, Snehanshu, and Nithin Nagaraj. "Measure or infer? Role of modeling and machine learning in modern astronomy." European Physical Journal Special Topics 230, no. 10 (May 2021): 2173–75. http://dx.doi.org/10.1140/epjs/s11734-021-00275-x.

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39

Bhardwaj, Rashmi, Shivam Bhardwaj, and Mohammad Sajid. "Fractal analysis and machine-learned decision system for precision and smart farming." European Physical Journal Special Topics 230, no. 21-22 (December 2021): 3955–69. http://dx.doi.org/10.1140/epjs/s11734-021-00333-4.

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40

Władysiak, R., W. Bogus, and T. Pacyniak. "Heat Balance Analysis of EPS Products Shaping Process." Archives of Foundry Engineering 13, no. 3 (September 1, 2013): 107–12. http://dx.doi.org/10.2478/afe-2013-0070.

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Abstract The work is a part of research into the reduction of energy consumption in the production of EPSthrough the modernization of technological equipment used. This paper presents the results of research and analysis of heat transfer process between the water vapor that was provided to machine, the mold, the product and the environment. The paper shows the calculation of the heat balance of the production cycle for two types of mold: standard and modernized. The performance tests used an infrared imaging camera. The results were used to develop a computer image analysis and statistical analysis. This paper presents the main stages of the production process and the construction of technological equipment used, changing the mold surface temperature field during the production cycle and the structure of the heat balance for the mold and its instrumentation. It has been shown that the modernization of construction of technological equipment has reduced the temperature field and as a consequence of decreased of demand for process steam production cycle.

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41

CHEN, LIFENG, XIAOLING WU, DATONG QIN, and ZEJUN WEN. "AN EXPERIMENTAL STUDY ON THE TRANSIENT TEMPERATURE TEST OF THE PLANETARY GEAR REDUCER OF THE EPBM." Journal of Advanced Manufacturing Systems 10, no. 01 (June 2011): 37–43. http://dx.doi.org/10.1142/s0219686711001953.

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A temperature test apparatus has been designed to measure the temperature rise of the planetary gear train. The experimental platform integrated the temperature test system, siemens operating system and industrial personal computer (IPC). The temperatures of various locations have been investigated under rated condition and the oil temperature-rise time under different speed was estimated. The experimental results indicated that the temperatures of the super-speed planetary gear train (PGT) of the earth pressure balance shield machine (EPBM) rise quickly than the low-speed PGT and the speed has significant influence on the temperature of the PGT. Through comparison the oil temperatures under reverse direction and forward direction, the direction of the motor has little influence on the temperature of the PGT.

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42

Wang, Xuanyao, and Yi Cheng. "Lane departure avoidance by man-machine cooperative control based on EPS and ESP systems." Journal of Mechanical Science and Technology 33, no. 6 (June 2019): 2929–40. http://dx.doi.org/10.1007/s12206-019-0540-6.

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43

Kongshu, Deng, Tang Xiaoqiang, Wang Liping, and Chen Xu. "Research on characteristics of deformation in thrust system for EPB shield machines." Tunnelling and Underground Space Technology 26, no. 1 (January 2011): 15–21. http://dx.doi.org/10.1016/j.tust.2010.06.008.

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44

Cui, Jian, Gongyun Xu, Yong Fang, Zhongtian Chen, Zhigang Yao, Liming Tao, and Liming Qu. "Experimental assessment of Soil/metal interface adhesion behaviours of EPB shield Machines." Tunnelling and Underground Space Technology 131 (January 2023): 104835. http://dx.doi.org/10.1016/j.tust.2022.104835.

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45

Lin, Lin, Hao Guo, Yancheng Lv, Jie Liu, Changsheng Tong, and Shuqin Yang. "A machine learning method for soil conditioning automated decision-making of EPBM: hybrid GBDT and Random Forest Algorithm." Eksploatacja i Niezawodnosc - Maintenance and Reliability 24, no. 2 (March 7, 2022): 237–47. http://dx.doi.org/10.17531/ein.2022.2.5.

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There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.

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46

Hu, Hui, and Wen Ge Qiu. "Study on Gas Permeability Test in Water Sand and Gravel Stratum." Applied Mechanics and Materials 71-78 (July 2011): 3407–10. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.3407.

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Water sand and gravel stratum is characterized by high gravel content, big grain size and high strength, easy permeation for water and air, brittleness and hysteresis quality in stratum failure, causing great difficulties in EPBM hyperbaric intervention and changing cutter tools working under air pressure. Due to the special geological conditions and no useful reference or criterion about gas permeability in such stratum all over the world, gas permeability test is done to fetch air loss data in natural state and bentonite-added state.Based on the practical situation of ChengDu Metro Line 1-Lot 3 project, by using methods of site test and theoretical analysis,the air loss before/after bentonite is added are given: At the air pressure of 1.0 bar, the air loss with bentonite added is 27% of that without bentonite. The acreage of pressure air loss around excavation face is from 35.2 m2 (In case of half chamber muck and bentonite slurry is 100% in effect around shield machine) to 129.6m2 (In case of empty chamber and bentonite slurry is 50% in effect around shield machine), providing reference in choosing air compressor, also for other project in this stratum and similar project.

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47

Babendererde, Siegmund. "Tunnelling machines in soft ground: a comparison of slurry and EPB shield systems." Tunnelling and Underground Space Technology 6, no. 2 (January 1991): 169–74. http://dx.doi.org/10.1016/0886-7798(91)90063-a.

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48

Babendererde, S. "Tunnelling machines in soft ground: a comparison of slurry and EPB shield systems." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 28, no. 6 (November 1991): A394. http://dx.doi.org/10.1016/0148-9062(91)91634-4.

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49

Bilal, A., D. Bathula, E. Bränn, E. Fransson, J. Virk, F. Papadopoulos, and A. Skalkidou. "Mom2B: a study of perinatal health via smartphone application and machine learning methods." European Psychiatry 65, S1 (June 2022): S574—S575. http://dx.doi.org/10.1192/j.eurpsy.2022.1472.

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Introduction Peripartum depression (PPD) impacts around 12% of women globally and is a leading cause of maternal mortality. However, there are currently no accurate methods in use to identify women at high risk for depressive symptoms on an individual level. An initial study was done to assess the value of deep learning models to predict perinatal depression from women at six weeks postpartum. Clinical, demographic, and psychometric questionnaire data was obtained from the “Biology, Affect, Stress, Imaging and Cognition during Pregnancy and the Puerperium” (BASIC) cohort, collected from 2009-2018 in Uppsala, Sweden. An ensemble of artificial neural networks and decision trees-based classifiers with majority voting gave the best and balanced results, with nearly 75% accuracy. Predictive variables identified in this study were used to inform the development of the ongoing Swedish Mom2B study. Objectives The aim of the Mom2be study is to use digital phenotyping data collected via the Mom2B mobile app to evaluate predictive models of the risk of perinatal depression. Methods In the Mom2B app, clinical, sociodemographic and psychometric information is collected through questionnaires, including the Edinburgh Postnatal Depression Scale (EPDS). Audio recordings are recurrently obtained upon prompts, and passive data from smartphone sensors and activity logs, reflecting social-media activity and mobility patterns. Subsequently, we will implement and evaluate advanced machine learning and deep learning models to predict the risk of PPD in the third pregnancy trimester, as well as during the early and late postpartum period, and identify variables with the strongest predictive value. Results Analyses are ongoing. Conclusions Pending results. Disclosure No significant relationships.

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Victor Chukwunweike EHIEDU, Anastasia Chi-Chi ONUORAH, and Josephine Ofure, CHIEJINA. "E-PAYMENT SYSTEM (EPS) AND EFFICIENCY OF BANKS IN NIGERIA." International Journal of Applied Research in Social Sciences 5, no. 1 (February 5, 2023): 1–13. http://dx.doi.org/10.51594/ijarss.v5i1.440.

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This study examines the effect of e-payment system (EPS) on the efficiency of banks in Nigeria. Specific objectives are to determine the implication of mobile payment, Automated Teller Machine (ATM) and POS on the efficiency of Nigerian banks, by collating data Central Bank of Nigeria (CBN), from the year 2012 to 2016. A linear regression analysis was used via SPSS to carry out the analysis, at a significance level of 0.05. The result of the analysis obtained a P-Value significance of 0.333 which connotes that there is no discernible effect of EPS on the efficiency of banking in Nigeria. The study recommended that Banks and other financial institutions should intensity efforts in mounting other e-payment channels to promote trade and commerce in Nigeria and the CBN should embark on intensive campaign for complete adoption of e- payments products especially at the grassroots. Keywords: Electronic, Payment, System, Mobile and Efficiency.

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