Actual Process Research Articles

A new type of bridge structure reinforcement effect evaluation algorithm based on time series

A supplement method based on reusing the original bridge structure is suggested to expedite construction and reduce costs. Furthermore, theoretical analysis of the single pile bearing capacity is conducted, primarily involving the calculation of the bridge pile foundation settlement, horizontal resistance and axial bearing capacity. At the same time, considering that the time series data generated in the actual process often have characteristics of imbalance between classes, a cost-sensitive hybrid network model is also constructed. Its significance is the analysis of bridge reinforcement performance data. The experimental results show that compared with the cross-entropy loss function, the introduction of a cost-sensitive loss function can significantly improve the monitoring accuracy of abnormal data sets. The ACC– score, G-means and F-measures are higher than those of the specific cross-entropy loss function. The permissible bearing capacity of muddy clay determined using a network model complies with engineering standards in the evaluation of bridge reinforcement performance. As a result, the reliability of this form of reinforcement is improved.

Prediction of control temperature and emergency temperature of monadic/binary aromatic nitro compounds by quantitative structure-property relationship: correlation study of self-accelerating decomposition temperature in thermal hazard assessment.

The thermal hazard of reactions caused by the structural instability of aromatic nitro compounds is a major concern in the field of chemical process safety and one of the main causes of major thermal runaway (TR) accidents such as fire and explosion. Among them, the self-accelerating decomposition temperature (SADT), as an important parameter, has been widely used to evaluate the thermal hazards of aromatic nitro compounds in actual storage and transportation processes. However, the control temperature (CT) and emergency temperature (ET), which depend on and are associated with SADT, have been rarely reported in previous studies. In this work, multiple linear regression (MLR) and artificial neural network (ANN) models for CT and ET were constructed based on the molecular descriptors corresponding to the stable structures of 27 monadic/binary aromatic nitro compounds, combined with advanced adiabatic accelerating calorimetric experiments and quantitative structure-property relationship (QSPR). The optimal subset of descriptors with significant contributions was screened out while the fit, predictive ability, and robustness of the four types of models were evaluated with internal and external validation parameters, and finally, two types of parameters (R2 and ARE) were selected as the main indicators for a comprehensive comparative analysis. The results show that the four models fit the experimental data well. During this period, the accuracy of ANN models is slightly higher than that of MLR models, and the QSPR models under the two modes (linear and nonlinear) are more inclined toward ET in prediction ability. Based on simplifying the calculation process and realizing rapid parameter prediction, this study is expected to provide technical support for engineering applications such as safe operation, safe storage and transportation of substances, and emergency response in the chemical industry. In this work, we tested and calculated the thermal safety parameters of 27 monadic/binary aromatic nitro compounds by ARC and AKTS and further used the PubChem database and Gaussian 09 software program to obtain and optimize their corresponding molecular structures. The geometric optimization process adopts DFT on the B3LYP level and the 6-31 + G(d, p) basis set, while the same functional and basis set was used for vibration analysis. The OpenBabel toolbox and ChemDES platform were used for transformation coding and descriptor calculation. Finally, IBM SPSS Statistics 24 and MATLAB software were used to construct MLR models and ANN models, respectively.

Distributed activation energy model of pyrolysis kinetics of starch and lignin with blast furnace slag as heat carriers

Molten blast furnace slag (BFS) can be used as heat carrier for biomass pyrolysis, which has high industrial value. Lignin (LG) and starch (ST) were chosen as model compounds of lignocellulosic biomass, which were mixed with different proportions of BFS, respectively. Thermogravimetry-process mass spectrometry studied the characteristics of LG-BFS and ST-BFS pyrolysis. The activation energies were obtained by Kissinger-Akahira-Sunose (KAS) method and four kinds of Distributed Activation Energy Model (DAEM) methods. The results showed BFS has little effect on the temperature of LG and ST pyrolysis. The average activation energy of LG-BFS-30% and ST-BFS-50% calculated by KAS method was 167.74 and 155.57 kJ∙mol−1 respectively, which meant the addition of 30% and 50% BFS promoted the pyrolysis of LG and ST, respectively. The activation energy distribution (AED) obtained by the four kinds of DAEM methods could reflect the actual pyrolysis process more completely. The results showed that the Double Gaussian distribution (GAUSS-2)and the distribution-free method (DAEM-NM) were more closer to the pyrolysis process of LG-BFS and ST-BFS. Compared with the pure LG, the CO2 content in pyrolysis gas decreased by 9.61%, and the CO content increased by 22.14% in the LG-BFS-30% pyrolysis. Compared with ST-BFS and LG-BFS, LG-BFS-30% was more conducive to the generation of CO.

Numerical investigation of the tensile behaviors of needle-punched nonwoven geotextiles

A numerical modeling method to reproduce the structures of needle-punched nonwoven geotextile based on the actual manufacturing process (web formation and web bonding) was developed. The geotextile model was built based on the statistical characteristics of the computed tomography (CT) observations, physical and mechanical properties of fibers, and physical parameters of the geotextile. A combination of the finite element model and pore network model method was proposed to obtain the pore size characteristics. The appearance and pore size characteristics before and under uniaxial tensile strains were investigated. It is shown that the method can simulate the geotextile manufacturing process satisfactorily. The pore size distribution (PSD) curves obtained from the model show good agreement with the CT-based results. Moreover, the method can reasonably simulate the tensile behaviors of geotextile under uniaxial tensile strain. The specimen necking phenomenon during the uniaxial tensile test was well reproduced. The PSD curves moved towards the direction of larger pore size with increasing uniaxial strain, and characteristic pore sizes (O98, O95, O50, O30, O10) all show a relatively steady increasing trend.

Polynesian Agency and the Establishment of the French Centre for Pacific Tests

ABSTRACT Since the 1960s, the installation of the Centre for Pacific Tests (Centre d’expérimentation du Pacifique or CEP) in French Polynesia has given rise to important debates concerning the agency of the populations in relationship to a decision on which they were not consulted. Until now, these debates have often focused on the reactions of the political class. In this article, we propose another approach to these issues by focusing on economic and social questions, with particular attention paid to the multiple strategies implemented by Polynesian workers. As this article demonstrates, while local actors had no say in the decision to set up the CEP in French Polynesia, the actual process of infrastructure construction, the interactions induced by these activities and the transformation of local power relations constituted opportunities for Islanders to develop their own strategies based on their interests and desires, their opinions, and the constraints that weighed unequally on them.

Dialogic investigations: Motivation in Japanese language learning

Over the past 15 years, the L2 motivational self system (Dörnyei, 2005, 2009) has been a dominant framework in the field of second language (L2) motivation. Yet, since the model’s introduction, there has been a theoretical discordance between the two components associated with future self-images (the ideal L2 self and the ought-to L2 self) and the third component related to the actual process of learning (the L2 learning experience). To remedy this shortcoming, this study draws on Bakhtin’s (1981, 1986) theory of dialog. Bakhtin claims that any use of language is a dialog between self and others and that language is learned through the assimilation of speech genres used by others for similar purposes in typical situations of social communication. The analysis of interview data obtained from three Japanese-as-a-second-language learners shows how situation-specific and future-oriented motivations are related to each other. The learners’ engagement in dialog with imagined Japanese speakers is developed through their engagement in dialog with actual Japanese speakers, and regardless of whether the dialog is actual or imagined, the participants orient themselves to speech genres of the language that correspond to particular social contexts.

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis.

Validation of orthopedic allograft preparation process: a new application of media fill test.

Musculoskeletal allografts represent an important practice in orthopedic surgeries and the demand for them has been growing. For this reason, in order to reduce clinical risk and to more efficiently manage the increase of allograft usage and also to optimize timing of the surgeries, the thawing and washing processes with aseptic technique were centralized in the department of Hospital Pharmacy. This study describe the design and execution of an adapted Media Fill Test (MFT) to demonstrate aseptic thawing and washing of allografts. For this specific and innovative setting, to better simulate the actual processing steps, a surrogate system was developed to simulate the tendon allograft. The aseptic technique of four operators was assessed and an initial performance validation and the first revalidation were described. All MFT were completed successfully, with no observation of turbidity. The readapted MFT shown in this study can provide insight into this innovative and growing field to other health professionals who want to implement this service.

A novel deep wavelet convolutional neural network for actual ECG signal denoising

Recently, more than 80% of sudden cardiac death is caused by arrhythmia, whose incidence has increased rapidly. In the actual wearable device acquisition process, ECG recordings are polluted by many different types of noise, mainly including baseline drift (BW), muscle artifact (MA) and electrode motion (EM). Therefore, the effective de-noising of ECG signals plays an important role in the subsequent accurate arrhythmia diagnosis. Nowadays, traditional filtering methods often only focus on one or two kinds of noise. When encountering different noises, different filtering algorithms need to be used, resulting in insufficient generalization ability of the algorithms. Aiming to effectively denoising ECG signals, this paper proposes a novel signal denoising method based on deep wavelet convolutional neural network. Inspired by the structure of the de-noising self-encoder, the convolution layers are used to replace the simple full-connected layers to build the encoder and decoder. Based on automatic feature extraction in convolution layer, the discrete wavelet transform is used to convert the signal into high-frequency and low-frequency components for replacing pooling layers to compress the input data and fully preserve the effective information. Finally, based on the dataset under the real noise conditions, compared with the existing methods, the proposed denoising model has better denoising performance, which reduces 0.194 RMSE and improves 5.99 SNR on average. Thus, the proposed denoising model has good potential in wearable devices.

Vibration Suppression of an Input-Constrained Wind Turbine Blade System

During the actual wind power generation process, wind turbines are often affected by side effects such as blade vibrations, input constraints, and actuator faults. This can lead to a reduction in power generation efficiency and even result in unforeseen losses. This study discusses a robust adaptive fault-tolerant boundary control approach to address the issues of input-constrained and actuator-fault problems in wind turbine blade vibration control. By employing projection mapping techniques and hyperbolic tangent functions, a novel robust adaptive controller based on online dynamic updates is constructed to constrain vibrations, compensate for unknown disturbance upper bounds, and ensure the robustness of the coupled system. Additionally, considering the possibility of actuator faults during the control process, a fault-tolerant controller is proposed to effectively suppress elastic vibrations in the wind turbine blade system even in the presence of actuator faults. The effectiveness of the proposed controller is validated through numerical simulations.

A study on the optimization of English writing teaching system in medical colleges and universities based on the perspective of curriculum ideology and politics

By adopting a theoretical framework based on constructivism and Content and Language Integrated Learning (CLIL), this study uses case study and action research methods to explore how to enhance medical students’ language skills, humanistic literacy, and research awareness under the perspective of curriculum ideology and politics. Observations and in-depth interviews revealed teachers’ and students’ views on English writing teaching, while action research explored and tested strategies for teaching reform. The results indicated that integrating the cultivation of language skills, humanistic literacy, and research awareness into the actual teaching process, along with providing ample practical opportunities and specific guidance, are key to improving medical students’ abilities. Continuous evaluation and adjustment are required in teaching reform to ensure its effectiveness and adaptability. This study provides empirical evidence for understanding how to optimize teaching activities under the perspective of ideological and political education in the curriculum to enhance the language skills, humanistic literacy, and research awareness of medical students, and provides guidance for future teaching reforms.

A Wireless Self-Powered Sensor Network Based on Dual-Model Convolutional Neural Network Algorithm for Tennis Sports

More and more scholars turn their attention to the use of digital and systematic management methods to further improve the safety and effectiveness of tennis. At present, most of these systems are based on video monitoring technology, and their actual operation process is limited by the deployment environment, and the cost is very high. In order to obtain the track of tennis players, this paper uses the sensor array in the intelligent insole to collect the original data. A Dual-model Convolutional Neural Network (DMCNN) structure is designed to further improve the computational efficiency and accuracy of tennis players’ gait recognition. Combined with quaternion and complementary filtering algorithm, the rotation matrix is established to convert the data in the sensor coordinate system to the reference coordinate system. Use multiple sensors in the smart insole to collect linear and curve track data from tennis players. After the unit conversion and normalization of the original data, the acceleration and angular velocity data in the sensor coordinate system are converted to the reference coordinate system by using quaternions to represent the rotation matrix and using complementary filtering algorithm to update the quaternions. The experimental results show that the displacement error of the scheme is small and the performance is good when the track tracking of tennis players is realized. The error between the estimated distance and the actual distance is within 6%, which proves the accuracy of the linear trajectory experiment.

SIA-net: Structural information awareness network based on normal samples for surface defect detection

Surface defect detection is a challenging task in industrial manufacturing, and the detection method based on deep learning has become the mainstream trend in the industry. However, this method requires a large number of labeled datasets for network training, and it cannot detect new types of defects randomly generated in the actual production process, making it difficult to transfer to actual production. To address this problem, we propose a structural information awareness network (SIA-Net). It is constructed by the adaptive generative adjunctive network (AGAN) module, which can simulate the background style of defect-free samples to randomly embed more reasonable defects. Then, the model is trained to recover the defect embedded area and the defect area is inferred according to the difference between before and after image restoration. Furthermore, in order to avoid blurring the image structure information during feature extraction, the self-attention encoder (SE) and spatial awareness decoder (SD) modules are designed to aggregate the image structure information to generate the final prediction results. We selected four public datasets and specially developed a box defect dataset to verify its detection effect. Experimental results (mIoU/mPA) (Kolektor: 88.91%/89.55%, AITEX defect: 89.61%/91.46%, RSDDs: 86.89%/87.88%, MT defect: 88.36%/90.32%, box defect: 89.71%/90.57%) show that our proposed method clearly outperforms the current unsupervised detection methods.

A nonlinear fatigue damage accumulation model under variable amplitude loading considering the loading sequence effect

Damage accumulation is nonlinear in the actual fatigue process, and the loading sequence is an important factor under variable amplitude loading. In this paper, a nonlinear fatigue damage accumulation model under variable amplitude loading considering the loading sequence effect is proposed. First, the cumulative rule of fatigue damage under variable amplitude loading is analyzed. It is suggested that the exponential function based on the load cycle ratio can be used to characterize the load sequence effect. Then, a nonlinear fatigue damage evolution equation of materials under constant amplitude load is proposed by taking the static toughness of the material as the damage parameter. Furthermore, the exponent of the load sequence influence parameter is determined as a function of the cycle life and elastic modulus. Then, a fatigue damage accumulation model under variable amplitude loading that considers the effect of the loading sequence is obtained based on the damage equivalence principle. Finally, the model is validated and compared through fatigue test data of five metal materials. It is found that the prediction accuracy of the proposed model is significantly higher than the other two models. In contrast, the difficulty of using the proposed model is lower than that of similar models.

Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning

Abstract The accurate control of the endpoint in converter steelmaking is of great significance and value for energy saving, emission reduction, and steel quality improvement. The key to endpoint control lies in accurately predicting the carbon content and temperature. Converter steelmaking is a dynamic process with a large fluctuation of samples, and traditional ensemble learning methods ignore the differences among the query samples and use all the sub-models to predict. The different performances of each sub-model lead to the performance degradation of ensemble learning. To address this issue, we propose a soft sensor method based on multi-cluster dynamic adaptive selection (MC-DAS) ensemble learning for converter steelmaking endpoint carbon content and temperature prediction. First, to ensure the diversity of the ensemble learning base model, we propose a clustering algorithm with different data partition characteristics to construct a pool of diverse base models. Second, a model adaptive selection strategy is proposed, which involves constructing diverse similarity regions for individual query samples and assessing the model’s performance in these regions to identify the most suitable model and weight combination for each respective query sample. Compared with the traditional ensemble learning method, the simulation results of actual converter steelmaking process data show that the prediction accuracy of carbon content within ±0.02% error range reaches 92.8%, and temperature within ±10°C error range reaches 91.6%.

The characterization of RNA-binding proteins and RNA metabolism-related proteins in fungal extracellular vesicles.

RNA-binding proteins (RBPs) are essential for regulating RNA metabolism, stability, and translation within cells. Recent studies have shown that RBPs are not restricted to intracellular functions and can be found in extracellular vesicles (EVs) in different mammalian cells. EVs released by fungi contain a variety of proteins involved in RNA metabolism. These include RNA helicases, which play essential roles in RNA synthesis, folding, and degradation. Aminoacyl-tRNA synthetases, responsible for acetylating tRNA molecules, are also enriched in EVs, suggesting a possible link between these enzymes and tRNA fragments detected in EVs. Proteins with canonical RNA-binding domains interact with proteins and RNA, such as the RNA Recognition Motif (RRM), Zinc finger, and hnRNP K-homology (KH) domains. Polyadenylate-binding protein (PABP) plays a critical role in the regulation of gene expression by binding the poly(A) tail of messenger RNA (mRNA) and facilitating its translation, stability, and localization, making it a key factor in post-transcriptional control of gene expression. The presence of proteins related to the RNA life cycle in EVs from different fungal species suggests a conserved mechanism of EV cargo packing. Various models have been proposed for selecting RNA molecules for release into EVs. Still, the actual loading processes are unknown, and further molecular characterization of these proteins may provide insight into the mechanism of RNA sorting into EVs. This work reviews the current knowledge of RBPs and proteins related to RNA metabolism in EVs derived from distinct fungi species, and presents an analysis of proteomic datasets through GO term and orthology analysis, Our investigation identified orthologous proteins in fungal EVs on different fungal species.

Acceptance rate of clinical pharmacists' recommendations-an ongoing journey for integration.

Introduction: Multidisciplinary expert team collaboration in the clinical setting, which includes clinical pharmacist involvement can facilitate significant improvements in outcomes and optimize patient management by preventing drug-related problems (DRP). This type of collaboration is particularly valuable in patients with multi-morbidity and polypharmacy such as diabetic foot patients. Evidence regarding the successful integration of a new clinical pharmacist, without previous experience into a unit is still scarce. Therefore, this study aimed to describe and evaluate the actual successful integration process of the clinical pharmacist into a diabetic foot unit by measuring the change in recommendation acceptance rate over time. Methods: A prospective, exploratory treatment effectiveness study based on the recommendation acceptance rate of a new clinical pharmacist introduced into the diabetic foot unit was conducted over a 9- month period. The clinical pharmacist identified medical and drug-related problems (DRP) or any discrepancies in the prescribing and administration of medications. Each identified DRP was documented and formulated as a recommendation by the clinical pharmacist. The main outcome measure was the acceptance rate of recommendations over time. Results: A total of 86 patients, of which 67% were men, averagely aged 66.5 (SD 11.8) years were evaluated. Calculated BMI was 30.2 (SD 6.2). The average number of medical diagnoses was 8.9 (SD3.2), and 11.1 (SD 3.7) prescribed drugs for each patient. Cardiovascular disease was presented by 95% (n = 82) of the patients and 33% of them (n = 28) had uncontrolled hyperglycemia. Averagely, 3.3 (SD 1.9) DRPs were identified pre patient. The efficacy-related DRP recommendation acceptance rate increased over the study period from 37.8% in the first 4months to 79.4% after a period of 4.75months. Safety-related DRP recommendation acceptance rate increased from 56% to 67.6%. Conclusion: Improved clinical outcomes and optimized pharmacologic patient management may be achieved by the successful integration of a clinical pharmacist into the team. This study provides evidence of the increasing recommendation acceptance rate of integrated, pharmacist-driven comprehensive medication management in an unexperienced unit. To overcome challenges, team members should collaborate to fully integrate the clinical pharmacist into the team-based structure and utilize proper strategies to minimize and transcend barriers.

Enhancing Methane Production through Anaerobic Co-Digestion of Sewage Sludge: A Modified ADM1 Model Approach

The International Water Association’s (IWA) established Anaerobic Digestion Model No. 1 (ADM1) was created to serve as a backup for experimental findings regarding the actual anaerobic digestion process. The previous model idea was adjusted and used to simulate an anaerobic digestion process in this study. Testing procedures, such as benchmark tests and balance checks, were performed in order to verify the accuracy of the implementation. These measures worked in tandem to ensure that the model was implemented flawlessly and without inconsistencies. The primary objective of this article is to construct a method that is based on the ADM1 for evaluating co-digestion and predicting the performance of the digestion process or methane yield based on the analyzed substrates’ physicochemical properties. Additional equations and simulations have been added to the standard model to create tools for evaluating the feasibility of anaerobic co-digestion. The study’s two most intriguing aspects are the optimal mixture and parameter dependence. The adjusted ADM1 is accurate in predicting the measured values of effluent COD, pH, methane, and produced biogas flows with a reasonable degree of accuracy, according to the validation results. This research shows how to use ADM1 in a wastewater treatment plant and other settings where anaerobic digestion is of interest.

Risk Control Analysis in Electric Power Engineering Project Management

With the development of social economy, the number and scale of electric power construction projects are increasing. Their contents are changing from "single" in the past to "diversified" at present. And the contents of project management are constantly being improved and updated. The function and structure of the electric power construction project in China are relatively complex, involving many factors. There are certain risks in safety, quality, investment, construction period and operating benefit. How to better solve the problems existing in the actual operation process and how to better manage power companies and improve the efficiency and economy of power company projects are the key issues that need to be considered. In order to ensure the economic and social benefits of electric power projects, improve their engineering quality, the project managers must analyze various possible risk factors in time during the construction of electric power projects, and take effective measures to minimize the risks.

Estimating resilience of manufacturing plants to physical disruptions: Model and application

The concept of Resilience is gaining an increasing importance in industrial systems, service networks and supply chains, as it extends traditional risk assessment methods by capturing both shortterm and long-term effects of accidents and disruptions. Resilience is, in fact, both a qualitative property and a performance indicator that measures the system's ability to survive disruptive events, and rapidly restore its functionality after the disruptive event has occurred. However, while literature about systems resilience is ample, comparatively scarce attention has been devoted to the evaluation of production systems resilience. As a contribution to fill this gap, in this paper a quantitative method for resilience estimation specifically aimed at manufacturing plants is developed. The method focuses on computing resilience of the manufacturing process, according to its structure, when resources are subjected to physical damage resulting from disruptive events such as sabotage, technical accidents or natural disasters. Overall, the paper allows to assess the economic loss and duration of production interruption in any kind of manufacturing plant when subjected to damage produced by the above causes. In particular, the method enables direct assessment of the initial capacity loss following a prescribed disruptive event, and explicit estimation of the time-dependent capacity recovery path based on the actual process structure and sequence of resources restoration tasks, also allowing the quantification of resulting economic losses. In the paper, after model development, an application example is provided to show model capabilities. Facility designers and emergency managers can use this model as a decision support tool for risk assessment, and to plan emergency actions and plant refurbishment aimed at resilience enhancement.