Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile facilities such as embassies and petrochemical plants,this concern now extends to a wider array of infrastructures and facilities.Engineers and scholars increasingly prioritize structural safety against explosions,particularly to prevent disproportionate collapse and damage to nearby structures.Urbanization has further amplified the reliance on oil and gas pipelines,making them vital for urban life and prime targets for terrorist activities.Consequently,there is a growing imperative for computational engineering solutions to tackle blast loading on pipelines and mitigate associated risks to avert disasters.In this study,an empty pipe model was successfully validated under contact blast conditions using Abaqus software,a powerful tool in mechanical engineering for simulating blast effects on buried pipelines.Employing a Eulerian-Lagrangian computational fluid dynamics approach,the investigation extended to above-surface and below-surface blasts at standoff distances of 25 and 50 mm.Material descriptions in the numerical model relied on Abaqus’default mechanical models.Comparative analysis revealed varying pipe performance,with deformation decreasing as explosion-to-pipe distance increased.The explosion’s location relative to the pipe surface notably influenced deformation levels,a key finding highlighted in the study.Moreover,quantitative findings indicated varying ratios of plastic dissipation energy(PDE)for different blast scenarios compared to the contact blast(P0).Specifically,P1(25 mm subsurface blast)and P2(50 mm subsurface blast)showed approximately 24.07%and 14.77%of P0’s PDE,respectively,while P3(25 mm above-surface blast)and P4(50 mm above-surface blast)exhibited lower PDE values,accounting for about 18.08%and 9.67%of P0’s PDE,respectively.Utilising energy-absorbing materials such as thin coatings of ultra-high-strength concrete,metallic foams,carbon fiber-reinforced polymer wraps,and others on the pipeline to effectively mitigate blast damage is recommended.This research contributes to the advancement of mechanical engineering by providing insights and solutions crucial for enhancing the resilience and safety of underground pipelines in the face of blast events.

Proteomic and metabolic prediction of response to therapy in gastric cancer

Several new treatment options for gastric cancer have been introduced but the prognosis of patients diagnosed with gastric cancer is still poor. Disease prognosis could be improved for high-risk individuals by implementing earlier screenings. Because many patients are asymptomatic during the early stages of gastric cancer,the diagnosis is often delayed and patients present with unresectable locally advanced or metastatic disease. Cytotoxic treatment has been shown to prolong survival in general,but not all patients are responders. The application of targeted therapies and multimodal treatment has improved prognosis for those with advanced disease.However,these new therapeutic strategies do not uniformly benefit all patients.Predicting whether patients will respond to specific therapies would be of particular value and would allow for stratifying patients for personalized treatment strategies.Metabolic imaging by positron emission tomography was the first technique with the potential to predict the response of esophagogastric cancer to neoadjuvant therapy.Exploring and validating tissue-based biomarkers are ongoing processes.In this review,we discuss the status of several targeted therapies for gastric cancer,as well as proteomic and metabolic methods for investigating biomarkers for therapy response prediction in gastric cancer.

Prediction of Response to Multimodality Treatment inEsophageal Cancer

Patients with locally advanced esophageal cancer have a dismal prognosis when treated exclu- sively by surgery. This fact prompted many investigators to apply neoadjuvant treatment strategies in an e?ort to improve survival. Results from phase III randomized trials are encouraging however, they revealed 五笔字型计算机汉字输入技术 that only patients with major histopathological response will bene?t from treatment. Therefore, predic- tive molecular markers indicating response or non-response to neoadjuvant treatment would be extremely helpful in selecting patients for current and future treatment protocols. In this paper we review the role of the molecular markers ERCC1 (excision repair cross-complementing 1 gene) and c-erbB-2 (synonym: HER2/neu) in predicting response to radiochemotherapy and outcome for patients with locally advanced resectable esophageal cancers (cT2-4, Nx, M0). The results are promising and it appears that we might expect to unequivocally identify with ERCC1 and c-erbB-2 respectively, approximately up to one third of patients who ful?l the criteria for neoadjuvant treatment for locally advanced esophageal cancer but will not bene?t from our treatment protocol. Integration of such markers in the clinical setting might prevent a substantial number of patients from expensive, non-e?ective and potentially harmful therapies, and could lead to a more individualized type of combined multimodality treatment in the near future.

基于物理信息引导深度学习的建筑响应实时预测方法

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

What control the spatial patterns and predictions of runoff response over the contiguous USA?

Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook several steps to explore the relationship.Initially,we partitioned runoff response change(RRC)into multiple components associated with climate and catchment properties,and examined the spatial patterns and smoothness indicated by the Moran's Index of RRC across the contiguous United States(CONUS).Subsequently,we employed a machine learning model to predict RRC using catchment attribute predictors encompassing climate,topography,hydrology,soil,land use/cover,and geology.Additionally,we identified the primary factors influencing RRC and quantified how these key factors control RRC by employing the accumulated local effect,which allows for the representation of not only dominant but also secondary effects.Finally,we explored the relationship between ecoregion patterns,climate gradients,and the distribution of RRC across CONUS.Our findings indicate that:(1)RRC demonstrating significant connections between catchments tends to be well predicted by catchment attributes in space;(2)climate,hydrology,and topography emerge as the top three key attributes nonlinearly influencing the RRC patterns,with their second-order effects determining the heterogeneous patterns of RRC;and(3)local Moran's I signifies a collaborative relationship between the patterns of RRC and their spatial smoothness,climate space,and ecoregions.

Effects of ovarian response prediction index and follicle-oocyte index on pregnancy outcomes:a retrospective cohort study of 12,218 fresh transfer cycles

Objective:To investigate the potential relationships among the ovarian response prediction index(ORPI),follicle-oocyte index(FOI),and clinical pregnancy rate(CPR)in women undergoing their first in vitro fertilization/intracytoplasmic sperm injectionembryo transfer(IVF/ICSI-ET)fresh cycle transfer.Methods:In this retrospective cohort study,we included 12,218 women who underwent their first IVF/ICSI-ET cycle between December 2014 and January 2021.The primary and secondary outcomes of our study were CPR and cumulative live birth rate(CLBR),respectively.The data were divided into three groups according to the ORPI and FOI tertiles.Multivariate logistic regression analyses,stratification analyses,interaction,restricted cubic splines,and receiver operating characteristic(ROC)curves were constructed to identify the relationships among ORPI,FOI,and CPR.Results:A statistically significant increase in CPR was detected from the lowest to the highest tertile group(ORPI:48.12%,54.07%,and 53.47%,P<0.001;FOI:49.99%,52.95%,and 52.71%,P=0.012).A higher CLBR was observed in the high group(ORPI:38.63%,44.62%,and 44.19%,P<0.001;FOI:41.02%,43.78%,and 42.59%,P=0.039).Multivariate logistic regression analysis revealed no statistically significant differences between ORPI,FOI,and neither CPR(odds ratio[OR][95%confidence interval{CI}],0.99[0.97–1.00]vs.[1.02{0.84–1.24}])nor CLBR(OR[95%CI],0.99[0.97–1.01]vs.0.99[0.81–1.20]).No significant association was found among FOI,ORPI,and CPR,even in the subgroups.Restricted cubic spline analyses indicated the existence of a non-linear relationship across the entire range of FOI and ORPI.The ORPI and FOI variables had poor predictive ability(AUC<0.60)for CPR.Conclusions:Both ORPI and FOI are not reliable predictors of clinical pregnancy or live birth outcomes in fresh ETs.Clinicians and researchers should avoid using FOI and ORPI to assess pregnancy outcomes after fresh ET because of their limited relevance and predictive value.

Neoadjuvant treatment for advanced esophageal cancer: response assessment before surgery and how to predict response to chemoradiation before starting treatment

Patients with advanced esophageal cancer（T3-4, N） have a poor prognosis. Chemoradiation or chemotherapy before esophagectomy with adequate lymphadenectomy is the standard treatment for patients with resectable advanced esophageal carcinoma. However, only patients with major histopathologic response（regression to less than 10% of the primary tumor） after preoperative treatment will have a prognostic benefit of preoperative chemoradiation. Using current therapy regimens about 40% to 50% of the patients show major histopathological response. The remaining cohort does not benefit from this neoadjuvant approach but might benefit from earlier surgical resection. Therefore, it is an aim to develop tools for response prediction before starting the treatment and for early response assessment identifying responders. The current review discusses the different imaging techniques and the most recent studies about molecular markers for early response prediction. The results show that [^18F]-fluorodeoxyglucose-positron emission tomography（FDGPET） has a good sensitivity but the specificity is not robust enough for routine clinical use. Newer positron emission tomography detector technology, the combination of FDG-PET with computed tomography, additional evaluation criteria and standardization of evaluation may improve the predictive value. There exist a great number of retrospective studies using molecular markers for prediction of response. Until now the clinical use is missing. But the results of first prospective studies are promising. A future perspective may be the combination of imaging technics and special molecular markers for individualized therapy. Another aspect is the response assessment after finishing neoadjuvant treatment protocol. The different clinical methods are discussed. The results show that until now no non-invasive method is valid enough to assess complete histopathological response.

Biomarkers for response to immune checkpoint inhibitors in gastrointestinal cancers

Gastrointestinal(GI)cancers account for a large proportion of cancer deaths worldwide and pose a major public health challenge.Immunotherapy is considered to be one of the prominent and successful approaches in cancer treatment in recent years.Among them,immune checkpoint inhibitor(ICI)therapy,has received widespread attention,and many clinical findings support the feasibility of ICIs,with sustained responses and significantly prolonged lifespan observed in a wide range of tumors.However,patients treated with ICIs have not fully benefited,and therefore,the identification and development of biomarkers for predicting ICI treatment response have received further attention and exploration.From tumor genome to molecular interactions in the tumor microenvironment,and further expanding to circulating biomarkers and patient characteristics,the exploration of biomarkers is evolving with high-throughput sequencing as well as bioinformatics.More large-scale prospective and specific studies are needed to explore biomarkers in GI cancers.In this review,we summarize the known biomarkers used in ICI therapy for GI tumors.In addition,some ICI biomarkers applied to other tumors are included to provide insights and further validation for GI tumors.Moreover,we present single-cell analysis and machine learning approaches that have emerged in recent years.Although there are no clear applications yet,it can be expected that these techniques will play an important role in the application of biomarker prediction.

Prediction Model for Vortex-Induced Vibration of Circular Cylinder with Data of Forced Vibration

A model based on the data from forced vibration experiments is developed for predicting the vortex-induced vibra- tions （VIV） of elastically mounted circular cylinders in flow. The assumptions for free and forced vibration tests are explored briefly. Energy equilibrium is taken into account to set up the relationship between the dynamic response of selfexcited oscillations and the force coemcients from forced vibration experiments. The gap between these two cases is bridged straightforwardly with careful treatment of key parameters. Given reduced mass m^# and material damping ratio of an elastically mounted circular cylinder in flow, the response characteristics such as amplitude, frequency, lock-in range, added mass coefficient, cross-flow fluid force and the corresponding phase angle can be predicted all at once. In- stances with different combination of reduced mass and material damping ratio are compared to investigate their effects on VIV. The hysteresis phenomenon can be interpreted reasonably. The predictions and the results from recent experiments carried out by Wifliamson＇ s group are in rather good agreement.

Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases

In patients with colorectal liver metastasis(CRLMs)unsuitable for surgery,oncological treatments,such as chemotherapy and targeted agents,can be performed.Cross-sectional imaging[computed tomography(CT),magnetic resonance imaging(MRI),18-fluorodexoyglucose positron emission tomography with CT/MRI]evaluates the response of CRLMs to therapy,using post-treatment lesion shrinkage as a qualitative imaging parameter.This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them.Consequently,there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients.Advanced quantitative imaging(diffusionweighted imaging,perfusion imaging,molecular imaging)allows the in vivo evaluation of specific biological tissue features described as quantitative parameters.Furthermore,radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters.As a result,parametric analysis(PA)translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion,structural heterogeneity,cellularity,oxygenation,and glucose consumption.PA could be a potentially useful imaging marker for predicting CRLMs treatment response.This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019(COVID-19)has exerted a profound adverse impact on human health.Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people.Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks.Monitoring of pathogenic microorganisms in the air,especially in densely populated areas,may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage.The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation,allocate health resources,and formulate epidemic response policies.This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission,which lays a theoretical foundation for the monitoring and prediction of epidemic development.Secondly,the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized.Subsequently,this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology,atmospheric sciences,environmental sciences,sociology,demography,etc.By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere,this review proposes suggestions for epidemic response,namely,the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Neoadjuvant chemoradiation changes podoplanin expression in esophageal cancer patients

BACKGROUND Locally advanced adenocarcinoma of the esophagus(EAC) and squamous cell carcinoma(ESCC) result in a worse prognosis. Neoadjuvant treatment improves survival, however, only for responders. The transmembrane glycoprotein podoplanin is overexpressed in squamous cell carcinomas, mi RNA-363 is associated to its regulation in head and neck cancer.AIM To predict therapy response and prognosis markers, and targets for novel therapies would individualize treatments leading to more favourable outcomes.METHODS Expression of podoplanin protein has been visualized by immunohistochemistry in surgical specimens of 195 esophageal cancer patients who underwent transthoracic esophagectomy: 90 ESCC and 105 EAC with clinical T2-3, Nx, M0.One hundred and six patients received neoadjuvant chemoradiation. RNA was extracted from paraffin-embedded tissue, and mi RNA-363 quantified by realtime Taq Man-real-time-PCR. D2-40 mab staining of > 5% was scored as high podoplanin expression(HPE). We related podoplanin and mi RNA-363 expression to histopathologic response after neoadjuvant treatment and clinicopathological characteristics, such as histological tumor type, survival rate or clinical tumor category.RESULTSWe confirmed expression of membrane-bound podoplanin in 90 ESCC patients.26% showed HPE of > 5%. In addition, absence in EAC patients(only 2% with HPE) was shown. Lower podoplanin expression has been detected in resectionspecimen of 58 ESCC patients after neoadjuvant(RTx/CTx) treatment, only 11% with HPE, compared to 50% HPE of 32 non-pretreated primary surgery patients,P = 0.0001. This difference of podoplanin expression was confirmed comparing pre-treatment biopsies with matching post-treatment surgical specimens, P <0.001. Podoplanin has been identified as a prognostic marker in 32 patients that underwent primary surgery without neoadjuvant treatment. Low(0-5%)podoplanin expression was associated with better prognosis compared to patients with HPE, P = 0.013. Podoplanin expression has been associated with post-transcriptional regulation by mi RNA-363. At a cut-off value of miR-363 < 7,lower mi R-363 expression correlated with HPE in surgical tissue specimens of primary surgery patients, P = 0.013. Therefore, ESCC patients with mi RNA-363 expression < 7 had a worse prognosis than patients expressing mi RNA-363 ≥ 7, P= 0.049.CONCLUSION Analysis of the molecular process that leads to decrease in podoplanin expression during neoadjuvant treatment and its regulation may provide novel markers and targets to improve targeted therapy of ESCC.

Intensity measures for seismic liquefaction hazard evaluation of sloping site

This work investigates the correlation between a large number of widely used ground motion intensity measures(IMs) and the corresponding liquefaction potential of a soil deposit during earthquake loading. In order to accomplish this purpose the seismic responses of 32 sloping liquefiable site models consisting of layered cohesionless soil were subjected to 139 earthquake ground motions. Two sets of ground motions, consisting of 80 ordinary records and 59 pulse-like near-fault records are used in the dynamic analyses. The liquefaction potential of the site is expressed in terms of the the mean pore pressure ratio, the maximum ground settlement, the maximum ground horizontal displacement and the maximum ground horizontal acceleration. For each individual accelerogram, the values of the aforementioned liquefaction potential measures are determined. Then, the correlation between the liquefaction potential measures and the IMs is evaluated. The results reveal that the velocity spectrum intensity(VSI) shows the strongest correlation with the liquefaction potential of sloping site. VSI is also proven to be a sufficient intensity measure with respect to earthquake magnitude and source-to-site distance, and has a good predictability, thus making it a prime candidate for the seismic liquefaction hazard evaluation.

Output Prediction of Helical Microfiber Temperature Sensors in Cycling Measurement by Deep Learning

The inconsistent response curve of delicate micro/nanofiber(MNF)sensors during cycling measurement is one of the main factors which greatly limit their practical application.In this paper,we proposed a temperature sensor based on the copper rod-supported helical microfiber(HMF).The HMF sensors exhibited different light intensity-temperature response relationships in single-cycle measurements.Two neural networks,the deep belief network(DBN)and the backpropagation neural network(BPNN),were employed respectively to predict the temperature of the HMF sensor in different sensing processes.The input variables of the network were the sensor geometric parameters(the microfiber diameter,wrapped length,coiled turns,and helical angle)and the output optical intensity under different working processes.The root mean square error(RMSE)and Pearson correlation coefficient(R)were used to evaluate the predictive ability of the networks.The DBN with two restricted Boltzmann machines(RBMs)provided the best temperature prediction results(RMSE and R of the heating process are 0.9705℃and 0.9969,while the values of RMSE and R of the cooling process are 0.7866℃and 0.9977,respectively).The prediction results obtained by the optimal BPNN(five hidden layers,10 neurons in each layer,RMSE=1.1266℃,R=0.9957)were slightly inferior to those obtained by the DBN.The neural network could accurately and reliably predict the response of the HMF sensor in cycling operation,which provided the possibility for the flexible application of the complex MNF sensor in a wide sensing range.

Arc-length technique for nonlinear finite element analysis

Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.

Peripheral CD4^(+) T cell signatures in predicting the responses to anti-PD-1/PD-L1 monotherapy for Chinese advanced non-small cell lung cancer

Limited benefit population of immune checkpoint inhibitors makes it urgent to screen predictive biomarkers for stratifying the patients.Herein,we have investigated peripheral CD4^(+) T cell signatures in advanced non-small cell lung cancer(NSCLC)patients receiving anti-PD-1/PD-L1 treatments.It was found that the percentages of IFN-γand IL-17A secreting naïve CD4^(+) T cells(Tn),and memory CD4^(+) T cells(Tm)expressing PD-1,PD-L1 and CTLA-4 were significantly higher in responder(R)than non-responder(NonR)NSCLC patients associated with a longer progression free survival(PFS).Logistic regression analysis revealed that the baseline IFN-γ-producing CD4^(+) Tn cells and PD-1^(+)CD4^(+) Tm cells were the most significant signatures with the area under curve(AUC)value reaching 0.849.This was further validated in another anti-PD-1 monotherapy cohort.Conversely,high percentage of CTLA-4^(+)CD4^(+) Tm cells was associated with a shorter PFS in patients receiving anti-PD-L1 monotherapy.Our study therefore elucidates the significance of functional CD4^(+) Tn and Tm subpopulations before the treatment in predicting the responses to anti-PD-1 treatment in Chinese NSCLC patients.The fact that there display distinct CD4^(+) T cell signatures in the prediction to anti-PD-1 and anti-PD-L1 monotherapy from our study provides preliminary evidence on the feasibility of anti-PD-1 and anti-PD-L1 combination therapy for advanced NSCLC patients.

Patient-derived bladder cancer organoid model to predict sensitivity and feasibility of tailored precision therapy

Background:Bladder cancer is a common and highly heterogeneous malignant tumor with a relatively poor prognosis.Thus,personalized treatment strategies for bladder cancer are essential for improving patient outcomes.Materials and methods:We developed an efficient 3-dimensional in vitro organoid culture system for bladder cancer organoids(BCOs),which maintains the homology with the original patient tumors and the heterogeneity between different individuals.In addition,we constructed chimeric antigen receptor(CAR)-T cells targeting B7H3 and evaluated the antitumor function of CAR-T cells by coculturing them with BCOs.Results:The BCOs closely resembled the characteristics of human tumors and were used to test individual sensitivity to platinum-based drugs and olaparib therapy.Coculture with CAR-T cells demonstrated specific antigen recognition and immune activation,indicating their potential in immunotherapy.Conclusions:Our study highlights the potential of BCOs to facilitate the development of personalized medicine for bladder cancer and improve the efficiency of drug discovery for bladder cancer therapy.

Detailed parametric design methodology for hydrodynamics of liquid-solid circulating fluidized bed using design of experiments

A design-of-experiments methodology is used to develop a statistical model for the prediction of the hydrodynamics of a liquid–solid circulating fluidized bed. To illustrate the multilevel factorial design approach, a step by step methodology is taken to study the effects of the interactions among the independent factors considered on the performance variables. A multilevel full factorial design with three levels of the two factors and five levels of the third factor has been studied. Various statistical models such as the linear, two-factor interaction, quadratic, and cubic models are tested. The model has been developed to predict responses, viz., average solids holdup and solids circulation rate. The validity of the developed regression model is verified using the analysis of variance. Furthermore, the model developed was compared with an experimental dataset to assess its adequacy and reliability. This detailed statistical design methodology for non-linear systems considered here provides a very important tool for design and optimization in a cost-effective approach