Efficient Analysis Method Research Articles

ImmuneApp for HLA-I epitope prediction and immunopeptidome analysis

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

MSBERT: Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning.

Tandem mass spectrometry (MS/MS) is a powerful technique for chemical analysis in many areas of science. The vast MS/MS spectral data generated in liquid chromatography-mass spectrometry (LC-MS) experiments require efficient analysis and interpretation methods for the following compound identification. In this study, we propose MSBERT based on self-supervised learning strategies to embed MS/MS spectra into reasonable embeddings for efficient compound identification. It adopts the transformer encoder as the backbone for mask learning and uses the same spectra with different masks for contrastive learning. MSBERT is trained on the GNPS data set and tested on the GNPS data set, the MoNA data set, and the MTBLS1572 data set. It exhibits enhanced library matching and analogous compound searching capabilities compared to existing methods. The recalls at 1, 5, and 10 on a GNPS test subset with structures not in the training set are 0.7871, 0.8950, and 0.9080, respectively. The results are better than those of Spec2Vec with 0.6898, 0.8276, and 0.8620, and DreaMS with 0.7158, 0.8327, and 0.8635. The rationality of embeddings is demonstrated by t-SNE visualization, structural similarity, spectra clustering, compound identification, and analogous compound searching. A user-friendly web server is provided for efficient spectral analysis, and the source code for MSBERT is available at https://github.com/zhanghailiangcsu/MSBERT.

An Integrated Strategy for Chemical Profile Establishment of Premna Microphylla Turcz. leaves and Metabolites in Vivo.

Premna microphylla Turcz. (PMT) is a traditional food and medicinal plant, which has been used to treat cure hemostasis, rheumatism and dysentery. However, it still lacks a clear understanding about chemical profile of PMT and metabolites in vivo. To establish a rapid and efficient analytical method for the identification of phytochemical in PMT and metabolites in vivo. Firstly, the fingerprint of PMT was established by high-performance liquid chromatography (HPLC) with methodology validation. Then, the phytochemical composition in PMT leaves were identified using ultra-performance liquid chromatography tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF-MS/MS). Finally, the prototype and correlated metabolites were detected after oral administration in mice to understand the absorption and metabolism of phytochemical in vivo. The result showed established HPLC method for fingerprints evaluation of PMT has good precision, repeatability and stability. Additionally, a total of 103 phytochemicals were identified in PMT, mainly including flavonoids and terpenoids. Then, 37 prototype components and 20 derived metabolites in vivo were detected. In this study, we constructed a fingerprint method which has good stability, precision and repeatability, and the fingerprint of PMT was established. Then the chemical profile of PMT in vitro and in vivo was performed. The result showed that flavonoids and terpenoids were the main phytochemicals in PMT, and methylation, sulfonation, dihydroxylation were the main metabolic pathway in vivo. The present study provided the phytochemical basis for the subsequent study of pharmacological activity.

Fatigue strength reliability assessment of turbofan blades subjected to intake disturbances based on the improved kriging model

This paper aims to develop an efficient and precise reliability analysis method to enhance the numerical prediction accuracy for complex structures. Kriging, an implicit surrogate model, used to address highly nonlinear and complex problems. In this study, genetic algorithms (GA) are utilized to optimize the parameters of the Kriging model, which is then integrated with a distributed collaborative strategy to introduce the Genetic Algorithm Optimized Distributed Collaborative Kriging Model (DCGAK). Using the CFM56-fan blade as a case study, the impact of intake disturbances at the engine inlet is evaluated to assess the fatigue strength reliability of the blade. Comparison with different mathematical models demonstrates that the prediction accuracy of DCGAK closely aligns with the Monte Carlo sampling results, suggesting promising prospects for its application in numerical prediction and reliability analysis. This approach enriches the current methods for structural reliability analysis of complex mechanical systems.

7030 Molecular Analysis Of Congenital Adrenal Hyperplasia-X Syndrome And Development Of A Pilot Panel For Analyzing Structural Variations With Long-Read Sequencing

Abstract Disclosure: J. Kim: None. S. Park: None. J. Yoon: None. D. Kim: None. S. Hwang: None. G. Kim: None. J. Kim: None. J. Choi: None. H. Yoo: None. Purpose: Genomic recombination events between the CYP21A2 and CYP21A1P genes are frequent due to their high sequence homology. Deletions in both the CYP21A2 and TNXB genes result in a chimeric TNXA/TNXB gene, leading to congenital adrenal hyperplasia (CAH)-X syndrome, characterized by a hypermobile form of Ehlers-Danlos syndrome. The aim of study was to determine the frequency of CAH-X syndrome and to develop a pilot panel for structural variation analysis using long-read sequencing technology. Methods: Among the 165 unrelated families with confirmed genetic mutations in the CYP21A2 gene, 44 probands had large deletions in one or both alleles. Long-range PCR was performed in 12 patients out of 44 probands using a forward primer for the 5’-UTR of the CYP21A2 and CYP21A1P and a reverse primer specific for exon 32 of TNXB. The amplified PCR products were then subjected to Sanger sequencing. We constructed a custom panel of 140 kb containing the RCCX modules arranged as STK19-C4A-CYP21A1P-TNXA-STK19B-C4B-CYP21A2-TNXB. In 4 patients with deletions in both the CYP21A2 and TNXB genes, long-read sequencing was performed using the custom panel on the PacBio Sequel II platform. Results: Out of 12 probands, eight harbored TNXA/TNXB chimeras. One patient was homozygous for CAH-X CH1 with a 120 bp deletion in exon 35 and had a Beighton score of 6 for joint hypermobility and multiple diverticula in the ascending colon. Two patients were heterozygous for CAH-X CH1, while three were heterozygous for the CAH-X CH2 with an intact exon 35 and the c.P4058W in exon 40. Two patients carried heterozygous mutations p.S4175N in exon 43, which is an unclassified type. Using long-read sequencing with a custom panel, missense mutations and TNXA/TNXB chimeras were detected in four patients previously identified by conventional direct sequencing and MLPA. Conclusions: This study identified 8 patients with CAH-X syndrome carrying four different TNXA/TNXB chimeras by long-range PCR. Long-read sequencing emerged as a comprehensive and efficient method for the molecular analysis of CAH, especially for the identification of structural variations. Biallelic deletion of TNXB was associated with severe clinical manifestations. Therefore, screening for CAH-X is essential for clinical management and prevention of the long-term consequences of CAH-X syndrome. Presentation: 6/1/2024

9266 Molecular Analysis Of Congenital Adrenal Hyperplasia-X Syndrome And Development Of A Pilot Panel For Analyzing Structural Variations With Long-Read Sequencing

Abstract Disclosure: J. Kim: None. S. Park: None. J. Yoon: None. D. Kim: None. S. Hwang: None. G. Kim: None. J. Kim: None. J. Choi: None. H. Yoo: None. Purpose: Genomic recombination events between the CYP21A2 and CYP21A1P genes are frequent due to their high sequence homology. Deletions in both the CYP21A2 and TNXB genes result in a chimeric TNXA/TNXB gene, leading to congenital adrenal hyperplasia (CAH)-X syndrome, characterized by a hypermobile form of Ehlers-Danlos syndrome. The aim of study was to determine the frequency of CAH-X syndrome and to develop a pilot panel for structural variation analysis using long-read sequencing technology. Methods: Among the 165 unrelated families with confirmed genetic mutations in the CYP21A2 gene, 44 probands had large deletions in one or both alleles. Long-range PCR was performed in 12 patients out of 44 probands using a forward primer for the 5’-UTR of the CYP21A2 and CYP21A1P and a reverse primer specific for exon 32 of TNXB. The amplified PCR products were then subjected to Sanger sequencing. We constructed a custom panel of 140 kb containing the RCCX modules arranged as STK19-C4A-CYP21A1P-TNXA-STK19B-C4B-CYP21A2-TNXB. In 4 patients with deletions in both the CYP21A2 and TNXB genes, long-read sequencing was performed using the custom panel on the PacBio Sequel II platform. Results: Out of 12 probands, eight harbored TNXA/TNXB chimeras. One patient was homozygous for CAH-X CH1 with a 120 bp deletion in exon 35 and had a Beighton score of 6 for joint hypermobility and multiple diverticula in the ascending colon. Two patients were heterozygous for CAH-X CH1, while three were heterozygous for the CAH-X CH2 with an intact exon 35 and the c.P4058W in exon 40. Two patients carried heterozygous mutations p.S4175N in exon 43, which is an unclassified type. Using long-read sequencing with a custom panel, missense mutations and TNXA/TNXB chimeras were detected in four patients previously identified by conventional direct sequencing and MLPA. Conclusions: This study identified 8 patients with CAH-X syndrome carrying four different TNXA/TNXB chimeras by long-range PCR. Long-read sequencing emerged as a comprehensive and efficient method for the molecular analysis of CAH, especially for the identification of structural variations. Biallelic deletion of TNXB was associated with severe clinical manifestations. Therefore, screening for CAH-X is essential for clinical management and prevention of the long-term consequences of CAH-X syndrome. Presentation: 6/1/2024

Fast determination of organophosphate esters and their metabolites in human matrices by a straightforward cold-induced strategy coupled with HPLC-MS/MS.

Organophosphate esters (OPEs) are commonly used chemicals and are also regarded as emerging environmental pollutants. Recently, it has been proved that metabolites of OPEs (mOPEs) could also cause health concerns. However, analytical methods for the concurrent measurement of OPEs and mOPEs in human matrices are still complicated. In this study, a convenient and efficient analytical method combining a cold-induced strategy and HPLC-MS/MS was developed to simultaneously determine 18 OPEs and 10 mOPEs in human serum, urine, and human milk. In brief, after the sample was extracted with acetonitrile, a "one-step" treatment combining purification and enrichment was accomplished by cold-induced liquid-liquid extraction (CI-LLE), and analytes were then quantified by HPLC-ESI-MS/MS. The ratio of acetonitrile/water, and the temperature and time set in the CI-LLE procedure were optimized for achieving the highest enrichment factors. Under thebest conditions, linearity, limits of detection (LODs), recovery, precision, and matrix effects of OPEs/mOPEs were verified. LODs of OPEs/mOPEs in serum, urine, and human milk were 0.1-113pg/mL, 0.1-22pg/mL, and 0.2-22pg/mL, respectively. Average recoveries ranged from 80 to 123%, with relative standard deviations lower than 15% for most analytes. The matrix effect test showed slight signal enhancement or inhibition, and the use of isotopically labeled internal standards (ISs) could compensate for the effects. In real sample analysis, both OPEs and mOPEs showed high detecting frequency, which indicated their ubiquity in humans.

An analytical dynamic stiffness method for efficient broadband vibro-acoustic analysis of complex built-up structures

An analytical modelling technique is presented, combining the dynamic stiffness method (DSM) with the spectral DSM (SDSM) for the broadband vibro-acoustic analysis of complex built-up structures. In particular, the structures are modelled exactly by the DSM based on particular solutions tailored for general acoustic or distributed force excitations; whereas the acoustic cavities are modelled by the SDSM where the boundary conditions are described by modified Fourier series with a rapid convergence rate. The vibro-acoustic coupling along the interfaces between the structures and the cavities is achieved by applying the normal velocity continuity condition, leading to a coupling matrix in an analytical manner. Both structural and acoustic cavity elements are assembled directly to model complex vibro-acoustic systems and no extra element discretization is required for both structural and acoustic domains. Consequently, the proposed method uses very few degrees of freedom (DoFs) but describes the broadband vibro-acoustic behaviour highly accurately. It is demonstrated that the proposed method exhibits a predominant advantage over the finite element package COMSOL in terms of accuracy and computational efficiency. This approach also has the potential to serve as the benchmark for a wide range of vibro-acoustic problems.

Analysis of fine-grained sediment dynamics from field observations with a vector autoregressive model

Empirical models and process-based theoretical or numerical models are valuable tools for analyzing sediment dynamics by reproducing the suspended sediment concentration (SSC) observations, but they are either limited by simplified assumptions or computationally complex. Some data-driven models have been developed to predict SSC but are still weak in explaining the causes of its changes. A field observation of silty sediment dynamics was performed in the Yellow River Delta, China. Then, a vector autoregressive (VAR) model is employed to analyze and predict the SSC variations. Using the impulse response function, the VAR model quantifies the transient and cumulative responses of the SSC to storm and current shocks (i.e., the impulse input), which reveals the key mechanisms of SSC changes from a new perspective. Results show that the storm effect on SSC in the study area lasts for ca. 85 h, and the SSC peaks at around the 36th hour with a cumulative increase of about 1.3 g/L due to the storm’s cumulative effect. Quantitative analysis reveals that waves contribute 73.78% to the SSC, while tidal currents contribute 26.22%. Fine SSC is significantly impacted by horizontal advection. A single reciprocal motion of the tidal current affects the SSC for approximately 6.25 h, while the overall duration exceeds 100 h. The proposed model physically reveals that the storm-induced high concentration gradient has been pushed back and forth by the tidal currents, affecting the SSC at the observation site, and the horizontal advection effect lasts for more than 100 h. Moreover, the VAR model achieves high-accuracy interval predictions of SSC for the next 5 h using only historical hydrodynamic data, with a 100% coverage probability of the 95% prediction interval. This paper provides a simple and efficient method for sediment dynamics analysis, which is of great significance for marine environment protection and disaster warning.

Simultaneous metabolomics and lipidomics analysis based on 4in1 online analysis system reveal metabolic signatures in atherosclerotic mice

Developing efficient and comprehensive analysis methods for metabolomics and lipidomics in the biological tissues and body fluids is essential for understanding the disease mechanisms. Although various two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) methods have been proposed to expand metabolite coverage, achieving higher efficiency in integrated metabolomics and lipidomics studies remains a technical challenge. In this work, a novel 4in1 online analysis system with excellent reproducibility and mass accuracy was constructed for metabolomics and lipidomics study in various biological samples from atherosclerotic mice. This system enabled the simultaneous detection in both positive and negative ion modes with extensive polarity separation in a single analytical run. Using the 4in1 online analysis system, we identified distinct but complementary metabolic signatures associated with atherosclerosis in different biological samples. Specifically, a total of 230 and 170 differential metabolites or lipids were detected in mice plasma samples and aortic tissue samples, respectively, including glycerophospholipids, sphingolipids, fatty acyls, glycerolipids, carboxylic acids, and pyrimidine nucleosides. Additionally, atherosclerosis-related metabolic pathways involved in biosynthesis of unsaturated fatty acids, sphingolipid metabolism, cholesterol metabolism, glycerophospholipid metabolism, and choline metabolism further revealed. These findings demonstrate that the novel 4in1 online analysis system is a faithful, stable and powerful tool for comprehensive metabolomics and lipidomics studies in complex biological matrices.

An analytical heat transfer model for transient Raman thermometry analysis.

Transient Raman thermometry improves on its steady-state counterpart by eliminating the error-prone steps of temperature calibration and laser absorption measurement. However, the accompanying complex heat transfer process often requires numerical analysis, such as the finite element method, to decipher the measured data. This step can be time-consuming, inconvenient, and difficult to derive a physical understanding of the heat transfer process involved. In this work, the finite element method is replaced by fitting the measured data to an analytical three-dimensional heat transfer model. This process can be completed in a few seconds. Using this approach, the in-plane thermal conductivity of two bulk layered materials and the interfacial thermal conductance between two-dimensional materials and quartz have been successfully measured. Based on our model, we performed an analytical quantitative sensitivity analysis for transient Raman thermometry to discover new physical insights. The sensitivity of the in-plane thermal conductivity of bulk layered materials is dictated by the ratio between the spot radius and heat spreading distance. The sensitivity of the interfacial thermal conductance between two-dimensional materials and quartz is determined by its conductance value. In addition, the uncertainty of the measured value contributed by the uncertainty of the input parameters can be efficiently estimated using our model. Our model provides an efficient data and sensitivity analysis method for the transient Raman thermometry technique to enable high throughput measurements, facilitate designing experiments, and derive physical interpretations of the heat transfer process.

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.

Optimization of adenosine và cordycepin extraction from cordyceps

Adenosine and cordycepin are bioactive compounds with health benefits. Therefore, both substances are often used to assess the quality of Cordyceps products. The optimization of the extraction conditions to determine adenosine and cordycepin from products containing Cordyceps (powder, extract, and capsule samples) were studied. The samples were prepared using an ultrasound-assisted extraction, the solvent was methanol and quantification of adenosine and cordycepin by HPLC-PDA. Optimization of adenosine and cordycepin extraction conditions by Response surface methodology using the Box-Behnken design, and by using design expert software. The experimental matrix includes 17 experiments of 3 investigation factors with the running range: material/solvent ratio (1:10-1:50 g/mL), methanol concentration (5-25%), ultrasound time (30-90 minutes). The results showed the optimal conditions for the extraction of adenosine and cordycepin for Cordyceps powder samples are the ratio of raw materials:solvent = 1:40 (g/mL), methanol concentration of 17.5% and ultrasound time is 70 minutes, the mean contents of adenosine and cordycepin were 24.15 and 330.78 mg/100g, the optimal methanol concentration for the extraction process is 15% for Cordyceps extract samples, those in their were: 158.30 and 1050.78 mg/100g, the most appropriate methanol concentration is 50% for Cordyceps capsule samples, while those in their were: 23.98 mg/100g and 213.21 mg/100g, relatively. This study provides an efficient analysis method to determine the optimal extraction conditions of adenosine and cordycepin from products containing Cordyceps that can be used as a basis for evaluating the quality of these products.

Multi Environment Trials and Adaption of Advanced Bread Wheat (<i>Triticum aestivum </i>L.) Genotypes in Low Moisture Stress Areas of Ethiopia

To predict bread wheat genetic potential across environments and adaption in low moisture stress wheat growing areas of Ethiopia. Multi-location trials were conducted in Ethiopia from 2020 to 2021 in main seasons. A total of advanced genotypes including the checks were arranged in randomized complete block design in a rectangular (row x column) array of plots with two replications. The results showed that, under the linear mixed model, the spatial and factor analytic models were efficient methods of data analysis for this study. By ranking average best linear unbiased prediction (BLUPs) within clusters, the 13 bread wheat environments were clustered into three mega environments (C1, C2, and C3) for the trait grain yield. This method used as a selection indicator, assisting in the selection of superior and adaptable types. The predicted performance of genotypes based on BLUP values averaged across correlated settings of C1 and C2, eliminating C3 due to low genetic correlation with the other trials and low genetic variation. Based on these clusters, the genotypes with the highest potential EBW192350 and EBW192369 were selected for a subsequent verification study that might potentially use them as a released variety. For genetic variance, the estimates for variance component parameters ranged from 0.069 to 2.896 and error variance, they ranged from 0.175 to 1.002. Therefore, increasing the application of this efficient analysis method will improve the selection of superior bread wheat varieties. The two genotypes can be further verified using national performance trials/ or verified in farmers’ fields for registration and commercialization.

An efficient multimodal attentional principal component analysis for continual learning-based dynamic process monitoring

Traditional multimode process monitoring methods extract features from time series data. Due to the catastrophic forgetting effect, data-driven multimode dynamic process monitoring is challenging based on a single monitoring model paradigm, i.e. the learned knowledge from previous modes may diminish as operating conditions undergo changes between modes, yet it is impractical to access all past data to retrain the model. In this work, a novel efficient method of multimodal attentional principal component analysis (M-APCA) with continual learning ability is introduced. Under the assumption that data from successive modes are received sequentially, dynamic process data are modeled using an attention mechanism to capture the relationship between data and the latent space, whereby meaningful information is concentrated as dynamic features which are extracted via a vector autoregressive model. In order to overcome the catastrophic forgetting problem, the idea of replay continual learning is employed. Specifically, past modes’ data which are significant to reflect the operating conditions, are selected and stored. These are repeatedly used in tandem with sequential data as replay data. Two types of attention mechanisms are considered and analyzed, each of which is specifically designed to learn from data in an unsupervised manner, so the overall algorithm is efficient both in time and storage costs. The proposed attentional principal component analysis and M-APCA are analyzed against several state-of-the-art methods to highlight the virtues of the proposed method. Compared with multimode monitoring methods, the effectiveness is demonstrated through case studies of: a continuous stirred tank heater, the Tennessee Eastman process and a practical coal pulverizing system.

Research on the efficiency analysis method of 9AT transmission planetary gear system based on power loss

As the core element of power transmission in Automatic Transmission (AT), the planetary gear system has advantages in the gear ratio range, load carrying capacity and transmission efficiency, and its transmission efficiency analysis is an important link in the design of automatic transmission.This paper proposes a method to analyze the transmission efficiency of planetary gear system of 9AT transmission considering power losses. Firstly, based on the hypergraph theory to establish the power transmission roadmap of each gear of the transmission (9AT) and the triangular hypergraph of the four-row planetary gear system, as well as the power flow model of the four-row planetary gear system without considering the power losses, and then the rotational speed, torque, and power change of each gear of the gear system are analyzed through the power spectrum. Secondly, a power flow model considering power losses and circulating power was established to analyze the influence law of different operating parameters (speed, torque, viscosity, gear meshing friction coefficient), and structural parameters (characteristic coefficients of each planetary row) on the transmission efficiency of the system. Finally, the equivalent planetary gear system transmission efficiency experimental bench considering losses is established to measure the transmission efficiency of the two-row planetary gear system at different speeds and torques, and the results of the experimental tests have the same trend with the theoretical calculations, which provides an analytical method to improve the transmission efficiency of the planetary gear system of the 9AT transmission.

Numerical analysis of fractional‐order Euler–Bernoulli beam model under composite model

The primary objective of this study is to develop a new constitutive model by combining a fractional‐order Kelvin–Voigt model with an Abel dashpot element in parallel. Subsequently, this new model will be incorporated into the Euler–Bernoulli beam's governing equation, utilizing shifted Legendre polynomials as basis functions, a classical orthogonal polynomial system, to solve the fractional‐order partial differential equations. By comparing the numerical solutions with the analytical solutions, we aim to evaluate the applicability of shifted Legendre polynomials in solving such problems and the accuracy of the obtained numerical solutions. Furthermore, we will investigate the performance of viscoelastic HDPE beams under different loading conditions and conduct a comparative analysis of the displacements of HDPE beams under the new constitutive model and the traditional fractional‐order Kelvin–Voigt model. Through this research, we hope to gain a deeper understanding of the characteristics of fractional‐order phenomena and provide more accurate and efficient numerical simulation and analysis methods for the field of structural mechanics, promoting the development of related engineering applications.

Comparative study of enterprise efficiency and environmental, social and governance performance before and after COVID‐19 epidemic—A study of China's A‐share listed Fintech companies

AbstractSince the outbreak of COVID‐19, it is worth noting how the enterprise efficiency and ESG performance of Chinese fintech enterprises are affected by pandemic. However, researches using Data Envelopment Analysis (DEA) to explore this issue are still very scarce. Therefore, this paper selects 45 A‐share listed fintech enterprises to construct A two‐stage RDM‐DDF efficiency analysis model, and evaluates the efficiency of different stages and different input–output indexes by using the window efficiency analysis method. At the same time, target enterprises were grouped according to their registered locations, so as to explore the regional differences in efficiencies and changes in the efficiency before and after the outbreak of COVID‐19. The analysis results show that: (1) Regional differences in corporate efficiency are quite pronounced. (2) There is no obvious positive correlation between ESG performance and traditional financial performance of target enterprises. (3) Following the outbreak of the COVID‐19 pandemic, the efficiency changes in target companies have been complex.

The Spread of Invasive and Poisonous Plants: A Lesson from Alkaloids

Invasive plant species pose a significant threat to global biodiversity and ecosystems. Climate changes favor the spread of non-native plants, whether voluntary or accidentally introduced into a new environment, as these plants possess a greater ability to adapt to changing environments. The spreading of these alien species has a negative impact also on agro-ecosystems, on agricultural yields, and on the nutritional quality of food crops. The high metabolic plasticity of these plants helps them to adapt to new ecosystems, enabling them to succeed in competing with crops. In particular, many alien plants are producers of alkaloids. These molecules represent the main chemical defense to biotic stressors and also the major risk for human health. In this review, we focused on invasive plants producing tropane alkaloids (TAs) and pyrrolizidine alkaloids (PAs). We explored the potential role of these molecules in the fitness of invasive plants in the context of climate change and reviewed the knowledge regarding their biosynthesis steps and examined the mechanism of toxicity when accidentally ingested. Finally, we summarized the most efficient analytical and molecular methods to detect either alkaloid contamination or the presence of invasive plant contaminants, which are the source of these molecules, in food crops. Possible solutions and precautions to ensure food safety have been also proposed.

Double friction pendulum-based isolation optimization for transformer-bushing systems

Transformers and their bushings are critical equipment pieces in power substations, which are often exposed to significantly high seismic risk. To enhance the seismic performance of the transformer-bushing system, isolation through double friction pendulum (DFP) bearings has been considered. However, the selection of appropriate isolation parameters lacks theoretical guidance and efficient analysis methods. A theoretical model for the transformer-bushing system isolated with DFP bearings has been proposed. Based on this model, isolation parameters that ensure acceptable displacement response while achieving optimal acceleration isolation efficiency can be determined. Taking a high-voltage transformer isolated by DFP bearings as an example, a set of isolation parameters is optimized and used to design a shaking table test. The results demonstrate that the proposed model exhibits reliable calculation accuracy for the seismic response of the transformer-bushing system, regardless of whether it is isolated or not, within the range of peak ground acceleration(PGA) of 0.1–0.6 g. Moreover, at 0.4 g, the experimental results indicate that the isolation efficiency remains consistent with the parameter optimization, approaching 50 %. This research contributes to the understanding of seismic resistance and isolation strategies for transformer-bushing systems, providing valuable insights for the selection and optimization of isolation parameters in practice.