Characteristic Index Research Articles

Identifying damage in long-span self-anchored suspension bridges using characteristic indices

This study presents a methodology for detecting damage in bridge structures using three damage identification indices: the deviation of displacement influence line (DDIL), the deviation curvature of displacement influence line (DCDIL), and the relative rate of change of wavelet packet energy (RES). The proposed approach is based on the principles of displacement influence line and wavelet packet transform. To demonstrate the effectiveness of the three metrics in determining the location of structural damage, a finite element model of the main bridge in Zhengzhou Taohuayu Yellow River Bridge, a three-span twin-tower all-steel girder self-anchored suspension bridge, is evaluated. The study investigates the impact of various factors, such as damage degree, damage location, signal noise, variable speed vehicles, and random traffic flow, on the usefulness of the metrics. The results indicate that all three indices are effective in detecting damage in bridge structures. The DCDIL demonstrates a distinct, abrupt change at the location of damage, while the DDIL is somewhat less effective. Additionally, the RES curve is particularly sensitive to local damage and can identify damage locations far from the bridge span. However, the study notes that the accuracy and reliability of the RES identification indicators decrease non-linearly in the presence of signal noise. The highest values of the RES curve are positively correlated with acceleration and less closely related to uniform acceleration. A vehicle speed of 20 m/s is recommended for identifying the location of bridge structure damage. Finally, the study suggests that random, unevenly spaced traffic enhances vibration and can more precisely localize the damage location and degree of the bridge structure.

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory

BackgroundObesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments. ObjectivesWe aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods. MethodsThe OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome. ResultsThe OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%. DiscussionThe index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

Regional short-term load forecasting method based on power load characteristics of different industries

With the gradual increase in the proportion of end-user electricity consumption, the electrical load has become a crucial issue in carbon emission studies. Precise load forecasting will benefit the treatment, calculations, and markets of carbon emission. To explore the characteristics of industrial power load in depth and improve the accuracy of regional short-term load forecasting, this paper proposes a regional short-term load forecasting method based on the power load characteristics of different industries. First, a power load characteristic index system is established to obtain a fine industry classification. Considering the influence of external factors on the power load, the industries are then further classified depending on their sensitivity to external factors using a combination of grey correlation analyses. For the non-external factor-sensitive industries, an integrated model based on the Shapley value method is adopted; for the external factor-sensitive industries, a factor analysis-bidirectional gated recurrent unit-attention mechanism model is proposed. The final combination of the two is the output of the total regional load forecasting results. The combined forecasting method is tested using power load data from a city in Liaoning, China. The results show that the proposed method has apparent advantages over the traditional single forecasting model in terms of forecasting accuracy.

Study on the Selection of Power Cable Rejuvenation Fluids Based on Relative Diffusion Coefficients

The present work presents and formulates a method for calculating the relative diffusion coefficient of rejuvenation fluid in injection cable rejuvenation technology, and the permeability characteristics of rejuvenation fluid in polyethylene insulation materials are calculated and compared. The main components of the rejuvenation fluid such as (Phenylmethyl-dimethoxysilane) PMDMS, (Diphenyldimethoxysilane) DPDMS and (Trimethylmethoxysilane) TMMS, and the catalysts such as (Tetra-isopropyl titanate) TIPT and (Tetra-isobutyl titanate) TIBT were analysed in depth. The article uses theoretical calculation method, combined with Helmroth and Limm-Hollifield models, to calculate the diffusion coefficients and relative diffusion coefficients of these compounds in polyethylene. Through comparative analysis of theoretical calculations, the optimal parameters for evaluating the permeability characteristics of rejuvenation fluids were determined. The research results show that TMMS has the best permeability characteristics in polyethylene, followed by PMDMS and DPDMS. In the catalyst, TIPT has better permeability characteristics than TIBT. And when selecting the composite formula for cable rejuvenation fluid, it is more convenient to use the relative diffusion coefficient as an index of diffusion characteristics. This research provides important theoretical basis and practical guidance for the formulation selection of cable rejuvenation fluids.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

Analysis of characteristic index and prediction of river bottom tearing scour in the Yellow River

Abstract River bottom tearing scour (RBTS) has a strong effect on the scouring and moulding of channel in the Yellow River. Due to the special forming conditions, complex influencing factors, and limited observed data, it is difficult to predict whether RBTS will occur accurately. By collecting and disposing of the hydrodynamic, sediment, and initial boundary data of 246 flood events related to RBTS in three typical reaches of the Yellow River basin, the correlation between different characteristic influencing factors and the occurrence and absence of RBTS were analysed, and prediction models based on machine learning algorithms were constructed. The results showed that under the existing data conditions, the maximum sediment concentration Sm, average sediment concentration Sp, flood growth rate ν, and shape coefficient δ were the four key indices to more easily distinguish whether RBTS will occur. The support vector machine algorithm model had the best performance results and exhibited higher accuracy and precision in predicting its occurrence compared with other models under given water and sediment conditions. The method proposed in this study provides a new method for accurately predicting RBTS in the Yellow River.

Research and Application of Carbon Capture, Utilization, and Storage–Enhanced Oil Recovery Reservoir Screening Criteria and Method for Continental Reservoirs in China

CCUS-EOR is a crucial technology for reducing carbon emissions and enhancing reservoir recovery. It enables the achievement of dual objectives: improving economic efficiency and protecting the environment. To explore a set of CCUS-EOR reservoir screening criteria suitable for continental reservoirs in China, this study investigated and compared the CCUS-EOR reservoir screening criteria outside and in China, sorted out the main reservoir parameters that affect CO2 flooding, and optimized the indices and scope of CCUS-EOR reservoir screening criteria in China. The weights of parameters with respect to their influences on CCUS-EOR were determined through principal component analysis. The results show that there are 14 key parameters affecting CO2 flooding, which can be categorized into four levels. For the first level, the crude oil-CO2 miscibility index holds the greatest weight of 0.479. It encompasses seven parameters: initial formation pressure, current formation pressure, temperature, depth, C2–C15 molar content, residual oil saturation, and minimum miscibility pressure. The second level consists of the crude oil mobility index, which has a weight of 0.249. This index includes four parameters: porosity, permeability, density, and viscosity. The third level pertains to the index of reservoir tectonic characteristics, with a weight of 0.141. It comprises two parameters: permeability variation coefficient and average effective thickness. Lastly, the fourth level focuses on the index of reservoir property change, with a weight of 0.131, which solely considers the pressure maintenance level. Based on the CCUS-EOR reservoir screening criteria and index weights established in this study, comprehensive scores for CCUS-EOR were calculated for six blocks in China. Among these, five blocks are deemed suitable for CCUS-EOR. Based on the comprehensive scoring results, a planning for field application of CCUS-EOR is proposed. The study provides a rational method to evaluate the CCUS-EOR reservoir screening and field application in continental reservoirs in China.

Urbanization-induced soil organic carbon loss and microbial-enzymatic drivers: insights from aggregate size classes in Nanchang city, China.

Soil microorganisms and enzymes play crucial roles in soil organic carbon (SOC) sequestration by promoting soil aggregate formation and stability and by participating in SOC cycling and accumulation. However, the effects by which soil microorganisms and enzymes act as mediators driving dynamic changes in SOC during rapid urbanization remain unclear. Therefore, this study selected the built-up area of Nanchang City, China (505 km2), as the study area. Sampling surveys were conducted using 184 sample plots stratified based on the proportion of impermeable surface area to distinguish different urbanization levels. The driving factors of dynamic changes in SOC of different aggregates during the process of urbanization were analyzed using the soil microbial community and enzyme activities. The results demonstrated that with an increase in urbanization intensity, both SOC content and stock exhibited a significant decline (p < 0.05). The highest SOC stock and contribution rate were observed in the 0.25-1 mm aggregates, and they were significantly influenced by urbanization (p < 0.05). In addition, the biomass of gram-positive bacteria (G+) and actinomycetota, and the activities of N-acetylglucosaminidase and acid phosphatase (AP) were significantly higher in low-urbanization areas than in high-urbanization areas (p < 0.05). SOC of each aggregate was positively correlated with fungi, arbuscular mycorrhizal fungi, G+, gram-negative bacteria, actinomycetota, protozoa, β-1,4-glucosidase, N-acetylglucosaminidase, AP, urease, and catalase. Compared to soil enzymes, soil microorganisms exhibited a greater role in SOC sequestration (22.7%). Additionally, a structural equation model indicated that urbanization can directly or indirectly lead to a decrease in SOC of aggregates by altering soil physicochemical properties and affecting microbial and enzyme dynamics. However, the larger vegetation characteristics index mitigate the negative impacts of urbanization on SOC. Overall, urbanization had a negative impact on soil carbon storage. In the future, it is important to consider strategies that focus on improving soil nutrients, maintaining soil structure, protecting existing urban trees, and enhancing plant diversity during the urbanization process. These measures can help increase soil microbial biomass and enzyme activity, thereby improving soil and aggregate-related SOC content. The study could contribute to enhancing carbon sequestration in urban greenspaces.

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit.

A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.

G-4 inhibits triple negative breast cancer by inducing cell apoptosis and promoting LCN2-dependent ferroptosis

Compound G-4 is a derivate of cyclin-dependent kinase inhibitor Rocovitine and showed strong sensitivity to triple negative breast cancer (TNBC) cells. In this study, the antitumor activity, mechanism and possible targets of G-4 in TNBC were investigated. Flow cytometry and immunoblotting showed that G-4 not only arrested the S phase of the cell cycle, but also induced apoptosis in TNBC cells via the mitochondrial pathway through inhibiting epidermal growth factor receptor (EGFR), AKT and MAPK pathways. In addition, G-4 induced the iron-mutagenesis process in TNBC cells and down-regulated differentially expressed gene lipid carrier protein 2 (LCN2) by RNA-seq. Moreover, G-4 elevated levels of cytosolic reactive oxygen species (ROS), lipid ROS, Fe and malondialdehyde (MDA), but decreased levels of superoxide dismutase (SOD) and glutathione (GSH), consistent with the effects of iron-mutagenic agonists Erastin and RSL3, which were inhibited by the iron inhibitor ferrostatin-1 (Fer-1). Furthermore, a LCN2 knockdown cell model was established by siRNA transfection, the IC50 of G-4 was increased nearly 100-fold, accompanied by a trend of no ferroptosis characteristic index. The results indicated that G-4 suppressed the malignant phenotype of TNBC, induced apoptosis by inhibiting EGFR pathway and promoted LCN2-dependent ferroptosis.

Impact of modifiers on soil–water characteristics of graphite tailings

To achieve integrated resource utilization of graphite tailings to improve their water-holding capacity, river silt and cow dung powder were added to graphite tailings as organic matter improvers. Improver ratios were designed using 4 g cow dung powder and 20, 30, and 50 g river silt. Soil–water characterization tests were performed using a combined tensiometer and filter paper method based on optimum density measurements. Analysis of the influence of river silt dosing on the soil–water characteristic curves of improved graphite tailing specimens was performed with data fitting using the Van Genuchten model. Here, we investigated the effect of river silt dosing on the internal pore structure and water-holding capacity of modified graphite tailing samples and verified the applicability of the model to graphite tailings. Our results demonstrate that the organic matter improver incorporated into graphite tailings can improve the internal structural compactness of graphite tailings, improving the water holding capacity. With an increase in river silt dosage, the saturated water content is larger, and the residual water content increases and then decreases. When river silt dosage is 30 g, the residual water content is the highest at a value of 3.32%. The van Genuchten model was highly accurate for assessing the graphite tailings. With an increase in river silt doping, the internal pore space first decreased and then increased, and the internal structure gradually became compact and loosened. The internal structure was in the optimal state in the experimental study when the dosage of cow dung powder was 4 g and the dosage of river silt was 30 g. The water holding capacity was optimal at this time. The results of this study provide a theoretical foundation for graphite-tailing-based mine reclamation and play a guiding role in exploring the value of the hydraulic characteristic index parameters when applying graphite tailings engineering.

DMA Characteristic Identification for Efficient Water Loss Management: Case Study of MWA Pipe Network, Thailand

This study is designed to categorize essential characteristics of District Metered Area (DMA), focusing on aspects related to pipe management such as real water loss, pipe density, and customer density. Our objective is to delineate the influence of each characteristic, with the ultimate goal of developing a practical index that aids planners in making informed decisions about water loss management. To achieve this, we propose the Area Characteristic Index (ACI), calculated using a fuzzy logic system, which amalgamates these characteristics to provide a comprehensive indicator of DMA performance. The results of this study elucidate the distinctive advantages of considering various characteristics when evaluating DMAs with diverse features. Specifically, real losses- a key element within the water balance framework-exert a more substantial influence on the ACI compared to the other characteristics assessed. Furthermore, to determine the number of deteriorated pipes in each DMA, we computed a Risk Index (RI) and an Infrastructure Value Index (IVI). The findings suggest that the extended length of deteriorated pipes, attributed to RI pipes, influences the ACI value and real losses significantly. Concluding, this study introduces a prospective matrix for pipe network management, serving as a guideline to enhance the efficiency of water loss management practices.

Spatial-temporal Variation in Water Quality of Rain-source Rivers in Shenzhen from 2015 to 2021 and Its Response to Rainfall

Rain-source urban rivers have the characteristics of small water capacity， lack of dynamic water supply， and being easily polluted. This study analyzed the spatial and temporal distribution characteristics of river water quality and the response of characteristic pollutants to rainfall based on daily rainfall data and 21 water quality indicators of nine major river basins in Shenzhen （excluding Shenzhen-Shantou） from 2015 to 2021 by using the single-factor assessment method， comprehensive pollution index method， hierarchical cluster analysis， and Pearson correlation. The results showed that： ① in 2015， the water quality of most sections in the whole region was inferior Class V water. After October 2018， the overall water quality of rivers was greatly improved， which was consistent with the background of Shenzhen's special water control activities in 2018. By 2021， the water quality of approximately 62% of sections reached Class Ⅰ-Ⅲ water standards. ② The water pollution in the densely populated western part of Shenzhen was more serious than that in the eastern part， and the water pollution in the lower reaches of the estuaries and tributaries was more serious than that in the upper reaches. ③ The water quality of the Pingshan River， Guanlan River， Longgang River， and Maozhou River was significantly affected by rainfall. ④ The main characteristic pollution indexes of the Shenzhen River were DO， permanganate index， COD， BOD5， NH4+-N， TP， petroleum， and anionic surfactant. For the Pingshan River and Longgang River， rainfall increased the concentrations of TP and NH4+-N. For the Maozhou River， rainfall increased the concentrations of TP and COD. For the Shenzhen River， rainfall increased the concentrations of COD， TP， and NH4+-N. The above results reveal the spatio-temporal variation in rain-source river water quality in Shenzhen and its response to non-point source pollution caused by rainfall events and provide a scientific reference for building a higher quality water environment in Shenzhen.

Investigating the causal impact of polycystic ovary syndrome on gestational diabetes mellitus: a two-sample Mendelian randomization study.

Determining the causal relationship between polycystic ovary syndrome (PCOS) and gestational diabetes mellitus (GDM) holds significant implications for GDM prevention and treatment. Despite numerous observational studies suggesting an association between PCOS and GDM, it remains unclear whether a definitive causal relationship exists between these two conditions and which specific features of PCOS contribute to increased incidence of GDM. The causal relationship between polycystic ovary syndrome (PCOS), its characteristic indices, and gestational diabetes mellitus (GDM) was investigated using a two-sample Mendelian randomization study based on publicly available statistics from genome-wide association studies (GWAS). The inverse-variance weighted method was employed as the primary analytical approach to examine the association between PCOS, its characteristic indices, and GDM. MR Egger intercept was used to assess pleiotropy, while Q values and their corresponding P values were utilized to evaluate heterogeneity. It is important to note that this study adopts a two-sample MR design where PCOS and its characteristic indices are considered as exposures, while GDM is treated as an outcome. The study results indicate that there is no causal relationship between PCOS and GDM (all methods P > 0.05, 95% CI of OR values passed 1). The IVW OR value was 1.007 with a 95% CI of 0.906 to 1.119 and a P value of 0.904. Moreover, the MR Egger Q value was 8.141 with a P value of 0.701, while the IVW Q value was also 8.141 with a P value of 0.774, indicating no significant heterogeneity. Additionally, the MR Egger intercept was 0.0004, which was close to zero with a P value of 0.988, suggesting no pleiotropy. However, the study did find a causal relationship between several other factors such as testosterone, high-density lipoprotein, sex hormone-binding globulin, body mass index, waist-hip ratio, apolipoprotein A-I, number of children, diabetes illnesses of mother, father and siblings, hemoglobin A1c, fasting insulin, fasting blood glucose, years of schooling, and GDM based on the IVW method. We observed no association between genetically predicted PCOS and the risk of GDM, implying that PCOS itself does not confer an increased susceptibility to GDM. The presence of other PCOS-related factors such as testosterone, high-density lipoprotein, and sex hormone-binding globulin may elucidate the link between PCOS and GDM. Based on these findings, efforts aimed at preventing GDM in individuals with PCOS should prioritize those exhibiting high-risk features rather than encompassing all women with PCOS.

Diagnostic value comparison of the combination of prostate-specific membrane antigen-body PET/MR and the prostate health index with each alone in early diagnosis of prostate cancer

ObjectiveThis study aimed to figure out whether the combination of the prostate health index (PHI) and prostate-specific membrane antigen (PSMA)-PET/MR could improve the diagnostic accuracy for prostate cancer (PCa) than that of each individual method used alone. MethodsIn this prospective, observational study, 41 patients who underwent the systematic prostate biopsy between June 2019 and September 2022 were enrolled. Both the PHI test and 18F-PSMA-1007-PET/MR were performed prior to biopsies. The diagnostic accuracy of different models was compared by logistic regression, areas under the curve (AUCs) of the receiver operating characteristic and net reclassification index (NRI). ResultsAmong the 41 patients, 14 (34.1%) were pathologically diagnosed with PCa. The PHI in the PCa group was significantly higher than that in the benign group (44.4 vs. 35.0, p=0.048). Similarly, all the patients in the PCa group received positive results of 18F-PSMA-1007-PET/MR, of which the positive rate was significantly higher than that in benign group (100% vs. 62.96%, p=0.025). The 18F-PSMA-1007-PET/MR provided additional diagnostic values to the PHI (AUC: 0.802 vs. 0.692, p=0.025). However, there was no significant difference between the combination model and the 18F-PSMA-1007-PET/MR alone (AUC 0.802 vs. 0.685, p=0.071). The optimal PHI cutoff of the combination model is 32, with which the model could significantly reduce unnecessary biopsies (NRI: 22.22%, 95% confidence interval: 6.54%–37.90%, p=0.005). However, among patients with the PHI of ≥43.5, there was no significant difference between the combination model and the PHI alone (NRI: 11.11%, 95% confidence interval: −0.74%–22.97%, p=0.066). ConclusionThe combination of the PHI and 18F-PSMA-1007-PET/MR outperforms the PHI alone for predicting PCa, especially in avoiding unnecessary biopsies. However, for patients with the PHI of ≥43.5, the addition of 18F-PSMA-1007-PET/MR to the PHI does not yield additional benefits.

Research on load curve fitting method based on K-means algorithm

Some areas of China are affected by the fluctuation of new energy output and load, and the power supply as a whole presents an unbalanced phenomenon of abundant electricity supply but shortage of power supply. To understand the characteristics of the load electricity consumption curve more accurately, this paper proposes a method of fitting the load electricity consumption curve based on K-mea ns algorithm. First, analyze the characteristics of the load electricity consumption based on the industry subdivision, and build the load electricity consumption characteristic index system. Then, the k-mea ns cluster analysis is carried out based on the characteristics of the load power consumption curve of the subdivided industries to determine the number of clusters, and the curve fitting and deviation test are carried out for each cluster group. This paper carries out practical analysis and theoretical research based on the data of typical industry load power consumption curve in Ningxia.

Effect of epoxy value on the rheological properties and microcosmic mechanism of WER emulsified asphalt.

Waterborne epoxy resin (WER), a cleaning material with exceptional high-temperature resistance, has attracted much attention to modify emulsified asphalt in the pavement material field. Epoxy value is the critical characteristic index of WER. In this research, three WER with the epoxy values of 0.20 eq/100g, 0.44 eq/100g, and 0.51 eq/100g were utilized as asphalt modifiers. The influence of epoxy value on WER-EA was investigated by comparing the rheological properties of three kinds of WER emulsified asphalt (WER-EA). The modification mechanism of WER-EA has been analyzed using FTIR and SEM. The results demonstrate that different WER-EA resulted in significantly different rheological properties. WER-EA with the epoxy value of 0.20 eq/100g (E20) performed best at high temperatures, with a maximum increase of 17477% in G*/sinδ compared to the neat asphalt and a maximum increase of 66.3% in G*/sinδ compared to the other two WER-EA. WER-EA with 0.44 eq/100g epoxy value (E44) performed best at low temperatures, with a maximum increase in m value of 39.4% and a maximum decrease in S value of 33.3% compared to the other two WER-EA. In addition, the interpenetrating polymer network (IPN) in E20 was observed to be more solid and stable, and IPN in E44 was more uniform. To summarize, lower epoxy value led to a higher degree of WER reaction and higher content of rigid groups, which is more conducive to optimizing the high-temperature property of WER-EA. WER with moderate epoxy value resulted in a low content of polar bonds and thus high content of flexible segments, which helps emulsified asphalt to form a more uniform IPN.

Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques

Seismic vulnerability assessment in urban areas would, in principle, require the detailed modeling of every single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at an urban scale where many buildings must be considered; therefore, it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns is the application of machine learning algorithms, which are able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper, machine learning algorithms were applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity that occurred in 2009. A combination of a trained neural network and a random forest algorithm allows us to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison, an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

Construction Project Cost Prediction Method Based on Improved BiLSTM

In construction project management, accurate cost forecasting is critical for ensuring informed decision making. In this article, a construction cost prediction method based on an improved bidirectional long- and short-term memory (BiLSTM) network is proposed to address the high interactivity among construction cost data and difficulty in feature extraction. Firstly, the correlation between cost-influencing factors and the unilateral cost is calculated via grey correlation analysis to select the characteristic index. Secondly, a BiLSTM network is used to capture the temporal interactions in the cost data at a deep level, and the hybrid attention mechanism is incorporated to enhance the model’s feature extraction capability to comprehensively capture the interactions among the features in the cost data. Finally, a hyperparameter optimisation method based on the improved particle swarm optimisation algorithm is proposed using the prediction accuracy as the fitness function of the algorithm. The MAE, RMSE, MPE, MAPE, and coefficient of determination of the simulated prediction results of the proposed method on the dataset are 7.487, 8.936, 0.236, 0.393, and 0.996%, respectively, where MPE is a positive coefficient. This avoids the serious consequences of underestimating the cost. Compared with the unimproved BiLSTM, the MAE, RMSE, and MAPE are reduced by 15.271, 18.193, and 0.784%, respectively, which reflects the superiority and effectiveness of the method and can provide technical support for project cost estimation in the construction field.