A modified back analysis method for deep excavation with multi-objective optimization procedure

Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task.

Probabilistic back-analysis of rainfall-induced landslides for slope reliability prediction with multi-source information

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.

Low back pain in China:Disease burden and bibliometric analysis

BACKGROUND Low back pain has become a global problem.Since many traditional Chinese therapies are helpful for low back pain,the current status of low back pain in China may provide some insights to this issue.AIM To demonstrate the disease burden of low back pain in China and the response of Chinese scholars to this issue.METHODS The burden of low back pain in China was estimated using the Global Burden of Disease Study 2021 released by the American Institute for Health Metrics and Evaluation.The incidence,prevalence and disability-adjusted life years were analyzed.We also performed a bibliometric analysis to analyzed the publication trend,changes of cooperation models and research topics on low back pain.RESULTS Prevalence of low back pain increased from 69.61 million in 1990 to 102.96 million in 2021.New cases increased by a stunning 44.50 million in 2021.Low back pain led to an increase of 4.16 per 1000000 population in terms of disability-adjusted life years from 1990 to 2021.But the increase in China was slower than that of world average.As a response,publications of low back pain published by Chinese scholars were stably raised.From 1990 to 2023,domestic cooperation significantly increased,while international cooperation and no cooperation decreased.However,domestic cooperation decreased between 2021 and 2023.The number of studies on prevention and treatment of low back pain reduced from 1990 to 2023,while the mechanism,etiology and other aspects of low back pain augmented.CONCLUSION The burden of low back pain in China is heavy.Together with the government,Chinese institutions of medical science should do more in declining the impacts of low back pain.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Research status and prospects of the fractal analysis of metal material surfaces and interfaces

As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.

Antioxidant and lipoxygenase inhibitory properties of a novel flavonoid from Pistacia chinensis Bunge and its molecular docking analysis

Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.

Prenatal ultrasonography and genetic analysis of fetal cleidocranial dysplasia:A case report

BACKGROUND Cleidocranial dysplasia(CCD)is an infrequent clinical condition with an autosomal dominant inheritance pattern.It is characterized by abnormal clavicles,patent sutures and fontanelles,supernumerary teeth,and short stature.Approximately 60%-70%of patients with CCD have mutations in the RUNX family transcription factor 2 gene.However,prenatal diagnosis of CCD is difficult when the family history is unknown.CASE SUMMARY We report a rare case of fetal CCD with an unknown family history,confirmed by prenatal ultrasonography and genetic testing at a gestational age of 16 weeks.The genetic reports indicated that the fetus carried pathogenic mutations in the RUNX family transcription factor 2 gene(c.674G>A).After careful consideration,the pregnant woman and her family decided to continue the pregnancy.CONCLUSION Definitive prenatal diagnosis of CCD should include family history,ultrasound diagnosis,and genetic analysis,especially if family history is unknown.

Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study

BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

Comprehensive bibliometric analysis of pharmacotherapy for bipolar disorders:Present trends and future directions

BACKGROUND Bipolar disorder(BD)is a severe mental illness characterized by significant mood swings.Effective drug treatment modalities are crucial for managing BD.AIM To analyze the current status and future trends of global research on BD drug treatment over the last decade.METHODS The Web of Science Core Collection database spanning from 2015 to 2024 was utilized to retrieve literature related to BD drug treatment.A total of 2624 articles were extracted.Data visualization and analysis were conducted using CiteSpace,VOSviewer,Pajek,Scimago Graphica,and R-studio bibliometrix to identify RESULTS The United States,China,and the United Kingdom have made the most significant contributions to research on BD drug treatment and formed notable research collaboration networks.The University of Pittsburgh,Massachusetts General Hospital,and the University of Michigan have been identified as the major research institutions in this field.The Journal of Affective Disorders is the most influential journal.A keyword analysis revealed research hotspots related to clinical symptoms,drug efficacy,and genetic mechanisms.A citation analysis identified the management guidelines published by Yatham et al in 2018 as the most cited paper.CONCLUSION This study provides a detailed overview of the field of BD drug treatment,highlighting key contributors,research hotspots,and future directions.The study findings can be employed as a reference for future research and policymaking,which may enable further development and optimization of BD pharmacotherapy.

Game Analysis of Regulation for Online Prescription Drugs under Customer Feedback Mechanism

Objective To study the way to better regulate the online sales of prescription drugs,and to provide reference for the adjustment of relevant policies since the online sales of prescription drugs has become an inevitable trend.Methods A game model was constructed for the strategy choice of pharmaceutical e-commerce platform,customers and government departments based on differential game theory and Nash equilibrium game model to analyze the pure strategy Nash equilibrium,Nash equilibrium dominant strategy of each subject and the mixed strategy Nash equilibrium under different conditions.Besides,Matlab was used to carry out simulation analysis.Results and Conclusion The study shows that:(1)Improving the credibility of the government and reducing the cost of government regulation can not only make the pharmaceutical e-commerce platform operate with high quality,but also give greater play to government functions;(2)The greater the influence of social evaluation on pharmaceutical e-commerce platforms,the lower the cost of high-quality operation of pharmaceutical e-commerce platform,and the greater the probability of customer choosing real evaluation strategy;(3)The greater the customers’perception of potential risk,the greater the compensation,and the lower the cost of reporting.Then,the greater the probability that government departments will choose strict regulation.Finally,the model solution and simulation analysis are combined to provide countermeasures and suggestions for the safety regulation of online sales of prescription drugs.

Targeted gene sequencing and bioinformatics analysis of patients with gallbladder neuroendocrine carcinoma:A case report

BACKGROUND Gallbladder neuroendocrine carcinoma(NEC)represents a subtype of gallbladder malignancies characterized by a low incidence,aggressive nature,and poor prognosis.Despite its clinical severity,the genetic alterations,mechanisms,and signaling pathways underlying gallbladder NEC remain unclear.CASE SUMMARY This case study presents a rare instance of primary gallbladder NEC in a 73-year-old female patient,who underwent a radical cholecystectomy with hepatic hilar lymphadenectomy and resection of liver segments IV-B and V.Targeted gene sequencing and bioinformatics analysis tools,including STRING,GeneMANIA,Metascape,TRRUST,Sangerbox,cBioPortal and GSCA,were used to analyze the biological functions and features of mutated genes in gallbladder NEC.Twelve mutations(APC,ARID2,IFNA6,KEAP1,RB1,SMAD4,TP53,BTK,GATA1,GNAS,and PRDM3)were identified,and the tumor mutation burden was determined to be 9.52 muts/Mb via targeted gene sequencing.A protein-protein interaction network showed significant interactions among the twelve mutated genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were used to assess mutation functions and pathways.The results revealed 40 tumor-related pathways.A key regulatory factor for gallbladder NEC-related genes was identified,and its biological functions and features were compared with those of gallbladder carcinoma.CONCLUSION Gallbladder NEC requires standardized treatment.Comparisons with other gallbladder carcinomas revealed clinical phenotypes,molecular alterations,functional characteristics,and enriched pathways.

Fatigue Strength Analysis of Dissimilar Aluminum Alloy TIG Welds

The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of 6061.The maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture location.The weld zone on the 7075 side experiences severe material softening,with a large gradient.However,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 HV.No obvious defects are found on the fatigue fracture,but a large number of secondary cracks appear.Cracks germinate from the weld toe and propagate in the direction of the plate thickness.Weld reinforcement has a serious impact on fatigue life.Fatigue life will decrease exponentially as the weld reinforcement increases under low stress.It is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low stress.The equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.

Machine learning based damage state identification:A novel perspective on fragility analysis for nuclear power plants considering structural uncertainties

Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.

Multilevel analysis of the central-peripheral-target organ pathway:contributing to recovery after peripheral nerve injury

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

Comparative transcriptome analysis identifies key regulators of nitrogen use efficiency in chrysanthemum

Nitrogen(N)is a limiting factor that determines the yield and quality of chrysanthemum.Genetic variation in N use efficiency(NUE)has been reported among chrysanthemum genotypes.We performed a transcriptome analysis of two chrysanthemum genotypes,'Nannonglihuang'(LH,N-efficient genotype)and'Nannongxuefeng"(XF,N-inefficient genotype),under low N(0.4 mmol L^(-1)N)and normal N(8 mmol L^(-1)N)treatments for 15 d and an N recovery treatment for 12 h(low N treatment for 15 d and then normal N treatment for 12 h)to understand the genetic factors impacting NUE in chrysanthemum.The two genotypes exhibited contrasting responses to the different N treatments.The N-efficient genotype LH had significant superiority in agronomic traits,N accumulation and glutamine synthase activity under both normal N and low N treatments.Low N treatment promoted root growth in LH,but inhibited root growth in XF.Transcriptome analysis revealed that the low N treatment increased the expression of some N metabolism genes,genes related to auxin and abscisic acid signal transduction in the roots of both genotypes,as well as genes related to gibberellin signal transduction in roots of LH.The N recovery treatment just increased the expression of genes related to cytokinin signal transduction in roots of LH.The expression levels of the NRT2.1,AMT1.1,and Gln1 genes related to gibberellin and cytokinin signal transduction were higher in roots of LH than in XF under different N treatments,suggesting that the genes related to N metabolism and hormone(auxin,abscisic acid,gibberellin,and cytokinin)signal transduction in roots of LH are more sensitive to different N treatments than those of XF.Co-expression network analysis(WGCNA)also identified hub genes like bZIP43,bHLH93,NPF6.3,IBR10,MYB62,PP2C,PP2C06 and NLP7,which may be the key regulators of N-mediated responses in chrysanthemum and play crucial roles in enhancing NUE and resistance to low N stress in the N-efficient chrysanthemum genotype.These results revealed the key factors involved in regulating NUE in chrysanthemum at the genetic level,which provides new insights into the complex mechanism of efficient nitrogen utilization in chrysanthemum,and can be useful for the improvement and breeding of high NUE chrysanthemum genotypes.

Analysis of Renewable Energy Absorption and Economic Feasibility in Multi-Energy Complementary Systems under Spot Market Conditions

As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.

Global trends of purinergic receptors and depression:A bibliometric analysis from 2003 to 2023

BACKGROUND Depression significantly threatens human health.Purinergic receptors are reported to be associated with depression.However,there is no bibliometric research in this field have been published.AIM To provide some reference for the further research in the field of purinergic receptors and depression utilizing bibliometric analysis.METHODS Relevant researches were retrieved from the Web of Science Core Collection database.The period of the search was from January 1,2003 to December 31,2023.The CiteSpace(6.2.R7)and VOSviewer(1.6.19)were applied to identify the main contributors of countries,authors,institutions,references and journals.Besides,we evaluate keywords to assess the hotspots and trends over the previous 2 decades.RESULTS Totally,247 articles were identified,showing an increasing trend over time.The most productive country,institution,and journal in this field are China,Harvard University,and Biological Psychiatry,respectively.Liang SD and Rodrigues,Ana Lucia S were the most prolific authors.Burnstock G ranked first among the cited authors.The cooperation among countries and disciplines is crucial.The P2X7 receptor provides promising prospects for treating depression and further studies are warranted to validate the scope and significance of depression therapeutic strategies.CONCLUSION This study provides an overview of the worldwide research status and future trends in purinergic receptors and depression.P2X7 receptor is considered an appropriate target for the treatment of depression,as well as neurological diseases.It is implied that based on purinergic system,the future prospects for interventions aimed at depression treatment are promising,showing the way for both augmentation strategies and new drug treatments in the context of the pharmacology of depression.

Improving cutoff frequency estimation via optimized π-pulse sequence

The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistant and optimized)to a quantum probe coupled to a bosonic environment. To demonstrate the precision of cutoff frequency estimation, we theoretically derive the quantum Fisher information(QFI) and quantum signal-to-noise ratio(QSNR) across sub-Ohmic,Ohmic, and super-Ohmic environments, and investigate their behaviors through numerical examples. The results indicate that, compared to the equidistant π-pulse sequence, the optimized π-pulse sequence significantly shortens the time to reach maximum QFI while enhancing the precision of cutoff frequency estimation, particularly in deep sub-Ohmic and deep super-Ohmic environments.

Transcriptome Analysis of Derris fordii and Derris elliptica to Identify Potential Genes Involved in Rotenoid Biosynthesis

Derris fordii and Derris elliptica belong to the Derris genus of the Fabaceae family, distinguished by their high isoflavonoid content, particularly rotenoids, which hold significance in pharmaceuticals and agriculture. Rotenone, as a prominent rotenoid, has a longstanding history of use in pesticides, veterinary applications, medicine, and medical research. The accumulation of rotenoids within Derris plants adheres to species-specific and tissue-specific patterns and is also influenced by environmental factors. Current research predominantly addresses extraction techniques, pharmacological applications, and pesticide formulations, whereas investigations into the biosynthesis pathway and regulatory mechanism of rotenoids remain relatively scarce. In this study, we observed notable differences in rotenone content across the roots, stems, and leaves of D. fordii, as well as within the roots of D. elliptica. Utilizing RNA sequencing (RNA-seq), we analyzed the transcriptomes and expression profiles of unigenes from these four tissues, identifying a total of 121,576 unigenes. Differentially expressed genes (DEGs) across four comparison groups demonstrated significant enrichment in the phenylpropanoid and flavonoid biosynthesis pathways. Key unigenes implicated in the rotenoid biosynthesis pathway were identified, with PAL, C4H, CHS, CHI, IFS, and HI4OMT playing critical roles in D. fordii, while IFS and HI4OMT were determined to be essential for rotenoid biosynthesis in D. elliptica. These findings enhance our understanding of the biosynthesis mechanism of rotenoids in Derris species. The unigenes identified in this study represent promising candidates for future investigations aimed at validating their roles in rotenoid biosynthesis.