Combined Analysis Method Research Articles

Sustainability Assessment of Agricultural Waste Biogas Production System in China Based on Emergy and Carbon Evaluation Methods

Biogas production is widely recognized as an effective solution for addressing agricultural waste treatment in rural areas. However, its development is often hindered by economic and environmental constraints. This study combined emergy evaluation and carbon footprint analysis methods to establish a new environmental radius assessment model for evaluating the ecological performance and optimization direction of an agricultural waste biogas production system, using a biogas production company in China as a case study. Compared with the straw return model and straw power generation model, the results of emergy indicators and carbon accounting showed that the biogas production model had a lower environmental load and higher economic output and level of emergy sustainability. Additionally, the biogas production system was found to reduce 0.47 kg of carbon emissions per 1 kg of agricultural waste utilized. The application of the biogas production model in rural areas had high ecological sustainability and carbon emission reduction benefits. Environmental radius assessment results confirmed that the reasonable changes in resource collection distance could further enhance the ecological sustainability, carbon mitigation ability, and economic benefits of the biogas production system. The environmental radius assessment method offers a new approach to the location planning of agricultural waste biogas utilization companies in rural areas.

Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York

The COVID-19 has led to significant changes in urban travel behaviors, with commuting being one of the most affected travel modes. Commuting cycling by bike-sharing systems (BSS) is regarded as a new transportation mode that is low-carbon and low-cost. However, its dynamic changes and spatiotemporal characteristics in different periods of COVID-19 still lack exploration. Therefore, this study adopts machine learning methods to identify commuter bike-sharing activities and develops a combined analysis method to analyze commuting cycling data via temporal, spatial, and spatiotemporal aggregation. Finally, we select the bike-sharing data in New York City from periods before, during, and after COVID-19 to conduct experiments. It has been found that commuting cycling experienced a “decrease-rebound” trend at the macroscopic level under the pandemic impact. However, at the micro level, urban mobility driven by this travel mode failed to fully recover, as evidenced by significant changes in spatial and temporal mobility patterns. The findings shall not only help traffic operators and managers discover the BSS commuting patterns but also reveal the pandemic impact on the travel behavior of urban residents, promoting the development of intelligent services for urban emergency management and traffic management.

Innovation in cancer pharmacotherapy through integrative consideration of germline and tumor genomes.

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, e.g., somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for more than five decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information about the tumor and the germline, taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to consider PGx data in interdisciplinary molecular tumor boards, where cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. Significance Statement The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, full potential of targeted therapy remains untapped due to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.

Investigation on Vibro-acoustic Characteristics of High-speed Railway Steel-concrete Composite Bridge Based on the Combined FE-BE-SEA Method

This paper presents a combined finite element-boundary element-statistical energy analysis (FE-BE-SEA) method to predict the vibro-acoustic characteristics of high-speed railway steel-concrete composite (SCC) bridge. The train-track coupling model is firstly introduced to accurately determine the forces transmitted to bridge, which are then imported into the established FE-BE model and SEA model. Among them, the former is adopted to calculate the vibration response in full frequency band and acoustic response at 20∼200 Hz, while for the noise part at 200∼2000 Hz, the latter is utilized for calculation. The on-site testing is carried out simultaneously to collect the measured results, which are compared with the predicted ones, validating the reliability and accuracy of the proposed method. Finally, a thorough discussion is conducted on the acoustic contribution and parametric analysis. The results indicate that the dominant frequency bands of deck and web radiated noise are below and above 200 Hz, respectively, ranking first and second in terms of overall acoustic contribution, followed by the diaphragm and flange. Reducing train speed, installing elastic fasteners, as well as increasing web thickness can significantly mitigate the vibro-acoustic responses from SCC bridge, but the effective frequency ranges for vibration and noise reduction are slightly different.

A new combined finite-discrete element method for stability analysis of soil-rock mixture slopes

PurposeThe purpose of this paper is to propose a new combined finite-discrete element method (FDEM) to analyze the mechanical properties, failure behavior and slope stability of soil rock mixtures (SRM), in which the rocks within the SRM model have shape randomness, size randomness and spatial distribution randomness.Design/methodology/approachBased on the modeling method of heterogeneous rocks, the SRM numerical model can be built and by adjusting the boundary between soil and rock, an SRM numerical model with any rock content can be obtained. The reliability and robustness of the new modeling method can be verified by uniaxial compression simulation. In addition, this paper investigates the effects of rock topology, rock content, slope height and slope inclination on the stability of SRM slopes.FindingsInvestigations of the influences of rock content, slope height and slope inclination of SRM slopes showed that the slope height had little effect on the failure mode. The influences of rock content and slope inclination on the slope failure mode were significant. With increasing rock content and slope dip angle, SRM slopes gradually transitioned from a single shear failure mode to a multi-shear fracture failure mode, and shear fractures showed irregular and bifurcated characteristics in which the cut-off values of rock content and slope inclination were 20% and 80°, respectively.Originality/valueThis paper proposed a new modeling method for SRMs based on FDEM, with rocks having random shapes, sizes and spatial distributions.

Simulation and Experimental Design of Magnetic Fluid Seal Safety Valve for Pressure Vessel

This article focuses on the safety valve of pressure vessels, and a new ferrofluid sealing device for pressure vessel safety valves is developed based on a special magnetic circuit. A combined method of numerical calculation and experimental analysis is used to study the relationship between seal clearance, number of seals, pole slot width, pole tooth height, pole tooth width, and the sealing pressure of the ferrofluid sealing device. The research results show that seal clearance and pole tooth width have a significant impact on the sealing performance, and as the dimensions increase, the sealing pressure decreases. As the number of seals, pole tooth height, and slot width increase, the sealing performance initially improves and then decreases. This phenomenon is attributed to the increase in magnetic reluctance in the magnetic circuit. In experimental studies, when the excitation current of the electromagnet is 240 mA and the coil turns number 30, the sealing capacity is 61.22 kPa. When the excitation current is 200 mA and the coil turns number 80, the sealing capacity is 168.24 kPa. The experiments demonstrate the compensating ability of magnetic fluid seals in combination with safety valve seals, confirming that combined seals have higher reliability compared to conventional mechanical seals.

Placing machine learning into the hermeneutic circle: a combined computational-interpretive method for text analysis

Placing machine learning into the hermeneutic circle: a combined computational-interpretive method for text analysis

The combined Lyapunov functionals method for stability analysis of neutral Cohen–Grossberg neural networks with multiple delays

This research article will employ the combined Lyapunov functionals method to deal with stability analysis of a more general type of Cohen–Grossberg neural networks which simultaneously involve constant time and neutral delay parameters. By utilizing some combinations of various Lyapunov functionals, we determine novel criteria ensuring global stability of such a model of neural systems that employ Lipschitz continuous activation functions. These proposed results are totally stated independently of delay terms and they can be completely characterized by the constants parameters involved in the neural system. By making some detailed analytical comparisons between the stability results derived in this research article and the existing corresponding stability criteria obtained in the past literature, we prove that our proposed stability results lead to establishing some sets of stability conditions and these conditions may be evaluated as different alternative results to the previously reported corresponding stability criteria. A numerical example is also presented to show the applicability of the proposed stability results.

Exploring the curing kinetics of Acrolein-Pentaerythritol resin: Impact of molecular weight and molecular weight distribution on cure behavior

Curing kinetics are crucial for designing and optimizing the process parameters of a resin. This study examines the non-isothermal curing kinetics of acrolein-pentaerythritol (APE) resins, focusing on the impact of molecular weight (MW) and molecular weight distribution (MWD) on their cure behavior. Kinetic parameters were determined using isoconversional and combined kinetic analysis methods through microcalorimeter measurements. The findings suggest that the cure process follows the nucleation and growth models (Avrami−Erofeev equation), with an activation energy of 72.2 kJ/mol. A comparison of two APE resins with different molecular weights and molecular weight distributions reveals that higher MW components expedite the initial curing reaction but impede the main curing process, leading to extended curing durations. This study provides valuable insights into the curing kinetics of APE resin and the influence of MW and MWD, contributing to the reliable and reproducible production of composite parts.

Combination of Evidence from Bibliometrics and Bioinformatics Analysis Identifies miR-21 as a Potential Therapeutical Target for Diabetes.

Many microRNAs (miRNAs) have been identified as being involved in diabetes; however, the question of which ones may be the most promising therapeutical targets still needs more investigation. This study aims to understand the overall development tendency and identify a specific miRNA molecule to attenuate diabetes. We developed a combined analysis method based on bibliometrics and bioinformatics to visualize research institutions, authors, cited references, and keywords to identify a promising target for diabetes. Our data showed that diabetes-related miRNA is receiving continuously increasing attention, with a large number of publications, indicating that this is still a hot topic in diabetes research. Scientists from different institutions are collaborating closely in this field. miR-21, miR-146a, miR-155, and miR-34a are frequently mentioned as high-frequency keywords in the related references. Moreover, among all the above miRNAs, bioinformatics analysis further strengthens the argument that miR-21 is the top significantly upregulated molecule in diabetes patients and plays an important role in the pathogenesis of diabetes. Our study may provide a way to identify targets and promote the clinical translation of miRNA-related therapeutical strategies for diabetes, which could also indicate present and future directions for research in this area.

Hydrogeological analysis and remediation strategies for water inrush hazards in highway karst tunnels

Geological hazards, particularly water inrush, pose frequent challenges in tunnel constructions within karst regions. Drawing from the engineering experiences in addressing multiple large-scale water inrush hazards in the Dejiang tunnel, a combined method of field investigation and theoretical analysis was employed for qualitative and quantitative analyses of water inrush mechanisms. The regional geological investigation highlighted the formation of strong and weak aquifers due to alternating weak limestone and mudstone in the synclinal strata, creating favorable conditions for large-scale karst conduit. Atmospheric precipitation water connects karst cavities through groundwater-eroded karst conduit, establishing a recharge system for water inrush hazards. Three empirical formulas are adopted to investigate the influence of rainfall on the tunnel’s actual water inflow. Importantly, it was found that considering only the influence of rainfall or surface water cannot reasonably predict the inflow in this area. The disaster’s primary causes are attributed to the heavy rainfall, karst conduit, underground river, and weak strata, which have contributed to the persistent nature of water inrush events. The principle of “drainage and blockage” was used to address the issue of water inrush. Proposed remediation strategies during construction, including the innovative membrane bag high-pressure grouting, have proved to be effective in addressing the connected karst conduits. The drainage and blockage effects were achieved by installing drainage pipes during the grouting reinforcement process. Finally, the water pressure-resistant structure was employed to enhance the stiffness of the lining. As validated by monitoring data, the measures serve as crucial methods to guarantee the safety of tunnel structures, effectively prevent water inrush and amplify the ecological benefits of water-rich areas.

Abnormal static and dynamic brain network connectivity associated with chronic tinnitus

In order to comprehensively understand the changes of brain networks in patients with chronic tinnitus, this study combined static and dynamic analysis methods to explore the abnormalities of brain networks. Thirty-two patients with chronic tinnitus and 30 age-, sex- and education-matched healthy controls (HC) were recruited. Independent component analysis was used to identify resting-state networks (RSNs). Static and dynamic functional network connectivity (FNC) were performed. The temporal properties of brain network including mean dwell time (MDT), fraction time (FT) and numbers of transitions (NT) were calculated. Two-sample t test and Spearman’s correlation were used for group compares and correlation analysis. Four RSNs showed abnormal FNC including auditory network (AUN), default mode network (DMN), attention network (AN) and sensorimotor network (SMN). For static analysis, tinnitus patients showed significantly decreased FNC in AUN-DMN, AUN-AN, DMN-AN, and DMN-SMN than HC [p < 0.05, false discovery rate (FDR) corrected]. For dynamic analysis, tinnitus patients showed significantly decreased FNC in DMN-AN in state 3 (p < 0.05, FDR corrected). MDT in state 3 was significantly decreased in tinnitus patients (t = 2.039, P = 0.046). In the tinnitus group, the score of tinnitus functional index (TFI) was negatively correlated with MDT and FT in state 4, and the duration of tinnitus was positively correlated with FT in state 1 and NT. Chronic tinnitus causes abnormal brain network connectivity. These abnormal brain networks help to clarify the mechanism of tinnitus generation and chronicity, and provide a potential basis for the treatment of tinnitus.

Genetic and Epigenetic Association of FOXP3 with Papillary Thyroid Cancer Predisposition.

Papillary thyroid cancer (PTC) is the most common type of thyroid malignancy with an increased female incidence ratio. The specific traits of X chromosome inheritance may be implicated in gender differences of PTC predisposition. The aim of this study was to investigate the association of two X-linked genes, Forkhead Box P3 (FOXP3) and Protein Phosphatase 1 Regulatory Subunit 3F (PPP1R3F), with PTC predisposition and gender disparity. One hundred thirty-six patients with PTC and an equal number of matched healthy volunteers were enrolled in the study. Genotyping for rs3761548 (FOXP3) and rs5953283 (PPP1R3F) was performed using polymerase chain reaction-restriction fragment length polymorphism assay (PCR-RFLP). The methylation status of FOXP3 was assessed using the combined bisulfite restriction analysis (COBRA) method. The SPSS software was used for statistical analyses. Gender stratification analysis revealed that the CA and AA genotypes and the A allele of FOXP3 rs3761548 variant are associated with PTC predisposition only in females. Moreover, different methylation status was observed up to the promoter locus of FOXP3 between PTC female patients, carrying the CA and CC genotype, and controls. Both revealed associations may explain the higher PTC incidence in females through reducing FOXP3 expression as reported in immune related blood cells.

A dynamic-simulation based workability and accessibility combined method for systematic analysis of floating wind operations

This work aims at providing a new methodology to assess combined workability and accessibility (W&A) of a floating offshore wind turbine, that is, the capability to transfer technicians to perform maintenance work. The proposed approach accounts for site-specific met-ocean conditions and industry-typical W&A limits by virtue of significant wave height, peak wave period, and wave heading, in contrast to only considering wave height, as currently done to take operation and maintenance decisions. A multi-body model of the VolturnUS-S 15 MW floating platform with a Service Operation Vessel (SOV) is used to compute the accessibility based on time-domain simulations and motion-compensated gangway limits. An iterative algorithm is implemented to find the maximum accessible wave height for each period and wave heading. Then, based on the limiting accessibility, single-body hydro-aero-elastic simulations are used to compute the nacelle accelerations, in order to estimate workability. The accessibility is hereby found to be the most limiting factor, with significant changes depending on the peak wave period and wave heading.

Precursors to rock failure in the laboratory using ultrasonic monitoring methods

Forecasting the occurrence of natural hazards, such as earthquakes or landslides, remain very challenging. These hazards are often caused by stress changes in the subsurface, therefore detecting and monitoring these changes can help the prediction and mitigation. Active ultrasonic transmission experiments were performed on Red Pfaelzer sandstones to investigate the monitoring and forecasting potential of these measurements. The sandstone samples were loaded until failure at different initial confining stress conditions. The forecasting potential to failure of different analysis methods, such as coda wave interferometry or wave attenuation, is investigated and compared. Our results show we can detect the forecast the upcoming failure of the samples from 40 to 70% of its failure point. Small differences between each analysis method are visible, but the trend of the signal is leading and therefore a robust prediction of failure can be made by combining analysis methods. In this paper, we propose a traffic light forecasting system using the precursory signals from ultrasonic monitoring. This system is applicable for monitoring failure at various depths and or stress conditions, for a better prediction of small stress-induced changes in the subsurface and thus mitigation of failure (natural hazards) in the subsurface.

Topology optimization based on combined mechanical analysis and variable density method for casting structure

In this study, a topology optimization method based on combined mechanical analysis and variable density is presented for a large-scale casting structure with complex thin-walled structures. First, the mechanical analysis model of the frame is established, the first-order bending and torsional modes and stiffness values of the all-cast aluminum frame are obtained, and the basic mechanical parameters such as bending/torsional mode and stiffness constraints needed for topology optimization are defined. Using the variable density method, unneeded material that can be removed from the casting structure is identified. According to the design requirements, the optimization objective function and constraint conditions are established, and the variable density method is used to solve the problem. Analysis of the mechanical results before and after topology optimization reveals that the modes and stiffness values are larger than the target values, and the mass of the frame is reduced by 10.2 kg (i.e. 13.9%) compared with the original design. The application of the combined mechanical analysis and variable density method to a casting structure can effectively achieve lightweight design.

Selection and application of building material suppliers based on the 2-tuple linguistic neutrosophic number combined grey relational analysis method

The quality of building materials will affect the implementation effect of construction projects. To ensure the service capacity of building materials, it is necessary to do a good job in selecting suppliers. In the specific evaluation of building material suppliers, after evaluation, suppliers with poor quality are excluded to ensure the quality of material supply, reasonably improve the construction effect of the building project, meet the construction needs of the building project, and improve the quality of the building project. The selection and application of building material suppliers (BMSs) is a multiple-attribute group decision-making (MAGDM) technique. In this study, the 2-tuple linguistic neutrosophic number combined grey relational analysis (2TLNN-CGRA) technique is constructed based on the classical grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Finally, a numerical example for building material supplier selection was constructed and some comparisons is constructed to illustrate the 2TLNN-CGRA technique. The main contribution of this study is constructed: (1) the 2TLNN-CGRA technique is implemented to cope with the MAGDM under 2TLNSs; (2) the 2TLNN-CGRA technique is implemented in line with the 2TLNN Hamming distance (2TLNNHD) and 2TLNN Euclidean distance (2TLNNED) simultaneously under 2TLNSs; (3) the numerical example for building material supplier selection is implemented to show the 2TLNN-CGRA technique; and (4) some efficient comparative studies are constructed with several existing decision techniques.

Methods of Automatic Analysis of Information Presentation Dynamics in Texts Based on Adaptable Dictionaries of Scientific Terms

The paper proposes a method for determining the dynamics of aspectual content of scientific texts. The problems arising in the automatic processing of scientific texts are discussed, approaches to the creation of a combined method of aspect-oriented analysis of scientific texts are proposed. The results of experiments of aspect analysis of scientific publications in the field of mathematics are given. The algorithm of aspect analysis is built on the basis of processing both the semantic domain graph formed as a result of automatic extraction of information from the text of the mathematical encyclopedia and the weighted semantic graph of the publication. Weighting functions for calculating the dependence of links between professional terms of the semantic domain graph are proposed. During the processing the text of a scientific publication, the semantic domain graph is transformed into the semantic graph of the publication, the two-phase processing algorithm of which forms semantic dependencies between text fragments.

Data-driven initial peak crushing force prediction of hybrid tubes

Accurately predicting the initial peak crushing force (IPCF) of the CFRP/metal hybrid structure is of great significance for the high-efficiency energy absorption of energy-absorbing elements and ordered deformation of combined energy-absorbing devices. Due to the significant differences in material properties between CFRP and metal and the high complexity of cross-sectional shape, a combined method of machine learning and mechanism analysis is proposed for predicting the IPCF of CFRP/Al hybrid energy-absorbing tube. First, a high-precision finite element model validated by the axial and oblique experiments is conducted to generate the dataset for predictive analysis. Second, a sample space through the design of experiment (DOE) is established for data fitting between structural parameters and IPCF based on traditional response surface model (RSM). It is found that traditional RSM method can successfully predict the IPCF of the single-cell hybrid tube, but cannot effectively predict the IPCF of the multi-cell tube due to the strong coupling effect between inner and outer plates. To address the issue of the lack of accurate correspondence between structural parameters and IPCFs in multi-cell hybrid tube, the indicator of relative density is proposed to characterize complex cross-sectional features. Surprisingly, there is a strong correlation between relative density and IPCF where multi-cell tubes with the same relative density have almost the same IPCF. Third, different artificial neural networks (ANN) are trained by a pre-simulated IPCF database of CFRP/Al hybrid energy-absorbing tube depending on the parameter characterization scheme based on mechanism analysis. In order to enhance the network's performance, the optimal configuration parameters of the neural network, including the number of layers and nodes, are ascertained through a combination of K-fold cross-validation and the Grid Search method. As a result, compared with the traditional RSM and Kriging model, the IPCF of CFRP/Al hybrid energy-absorbing tube can be predicted in small samples with high accuracy using the optimal ANN model. Finally, Through the comparative analysis of the IPCF of hybrid tubes with reported multi-cell configurations, it is found that the prediction model proposed in this paper has good generalization ability, which can be used to predict more complex types of multi-cell tube configurations. The present study offers important suggestions for the overall control of multi-materials hybrid energy-absorbing tube and the ordered deformation of combined energy-absorbing devices.

Image-based vegetation analysis of desertified area by using a combination of ImageJ and Photoshop software.

Fractional vegetation cover (FVC) is a crucial indicator to estimate degradation and desertification for grasslands. However, traditional small-scale FVC analysis methods, such as visual estimation and point-sampling, are cumbersome and imprecise. Innovative methods like image-based FVC analysis methods, while accurate, face challenges such as complex analytical procedures and the necessary training for operations. Therefore, in this study, a combined application of ImageJ and Photoshop was employed to achieve a more effective analysis of FVC values in desertification areas. Our results showed that the FVC results obtained by combination of Photoshop and ImageJ were dependable and precise (R2 > 0.98), demonstrating equivalency to results obtained through either visual estimation or Photoshop-based methods. Furthermore, even in the face of background interference and varied shooting angles, the combination of ImageJ and Photoshop software was still able to maintain a low error rate when analyzing FVC values (average error rate = - 2.6%). In conclusion, the imaged-based combined FVC analysis method employed in our research was an effective, precise, and efficient technique for analyzing small-scale FVC, promising substantial improvement over conventional methods.