Performance Of Pipeline Research Articles

Identity-Preserving Face Swapping via Dual Surrogate Generative Models

In this study, we revisit the fundamental setting of face-swapping models and reveal that only using implicit supervision for training leads to the difficulty of advanced methods to preserve the source identity. We propose a novel reverse pseudo-input generation approach to offer supplemental data for training face-swapping models, which addresses the aforementioned issue. Unlike the traditional pseudo-label-based training strategy, we assume that arbitrary real facial images could serve as the ground-truth outputs for the face-swapping network and try to generate corresponding input < source, target > pair data. Specifically, we involve a source-creating surrogate that alters the attributes of the real image while keeping the identity, and a target-creating surrogate intends to synthesize attribute-preserved target images with different identities. Our framework, which utilizes proxy-paired data as explicit supervision to direct the face-swapping training process, partially fulfills a credible and effective optimization direction to boost the identity-preserving capability. We design explicit and implicit adaption strategies to better approximate the explicit supervision for face swapping. Quantitative and qualitative experiments on FF++, FFHQ, and wild images show that our framework could improve the performance of various face-swapping pipelines in terms of visual fidelity and ID preserving. Furthermore, we display applications with our method on re-aging, swappable attribute customization, cross-domain, and video face swapping. Code is available under https://github.com/ICTMCG/CSCS.

The performance of transmission pipelines on February 6th, 2023 Kahramanmaras earthquake: a series of case studies

AbstractThe Mw 7.8 earthquake that struck Kahramanmaras on February 6, 2023, caused significant damage to hydrocarbon and water transmission lines due to permanent ground deformations. Explosions occurred in the Kahramanmaras–Gaziantep Natural Gas transmission pipeline causing disruption of gas in the cities of Gaziantep and Hatay Fault Displacement Hazard. Water transmission pipelines were also damaged due to extreme fault offsets. This paper presents the findings from the earthquake affected zones. Particular attention was given to the performance of the 2600 mm diameter Duzbag–Gaziantep water transmission pipeline due to three reported challenges: (#1) The behavior of the pipe in the Duzbag tunnel, (#2) The pipe bend behavior at the fault crossing, and (#3) The pull-out of the pipe at valve room. Based on field observations, extreme damage forms at pipes were presented. A three-dimensional nonlinear numerical analysis was performed to simulate the observed damage of the reoriented water pipe at the fault crossing for Case #2. The nonlinear finite element model was able to capture the complex nature of the post-yield behavior of steel pipe as well as the damage locations along the pipe and their sequence of occurrences. As a mitigation measure for new pipes, the need for using flexible connections at critical crossings was highlighted. For existing pipes, the importance of developing secondary (by-pass) lines was emphasized.

Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance

BackgroundThe advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset?ResultsHere, we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. We identify characteristics of datasets that correlate with strong prediction performance that could guide when such prediction models may be useful.ConclusionsSupervised machine learning models have utility for recommending analysis pipelines and therefore the potential to alleviate the burden of choosing from the near-infinite number of possibilities. Different aspects of datasets influence the predictive performance of such models which will further guide users.

Mitigating Geohazards: CFD-Driven Analysis of Viscoplastic Debris Flow Impact on Resilient Oil and Gas Pipeline Design in Alpine Terrains

Debris flow is one of the catastrophic geohazard, poses significant threat to all system of infrastructure including buildings, bridges, pipelines and retaining walls. For the sustainable design of oil and gas pipeline prone to debris flow hazard, It is crucial to consider the expected debris flow impact forces to ensure the safe and effective performance of pipeline. This study numerically investigates the dynamic impact of viscous debris flow on exposed pipeline. Altair Hyperwork CFD software was utilized to perform numerical investigation in CFD environment, simulating two-phase debris flow. Hershel Bulkley rheology and Spalart-Allmaras turbulence model was adopted to simulate the complex interaction of debris flow on pipeline. The results revealed that debris flow dynamics are highly dependent on sediment composition, fine content, and flow volume. These factors significantly influence the flow behavior and its potential impact on pipeline infrastructure. Furthermore, this study highlights the critical role of impact forces due to debris flow in pipeline design to enhance the safe and longevity of pipeline system in alpine terrain. The findings underscore the need for comprehensive approach of pipeline design that integrate the advanced numerical techniques with thorough understanding of local geological conditions.

Genetic Characterization and Population Structure of Drug-Resistant Mycobacterium tuberculosis Isolated from Brazilian Patients Using Whole-Genome Sequencing.

The present study aimed to determine the genetic diversity of isolates of Mycobacterium tuberculosis (Mtb) from presumed drug-resistant tuberculosis patients from several states of Brazil. The isolates had been submitted to conventional drug susceptibility testing for first- and second-line drugs. Multidrug-resistant (MDR-TB) (54.8%) was the most frequent phenotypic resistance profile, in addition to an important high frequency of pre-extensive resistance (p-XDR-TB) (9.2%). Using whole-genome sequencing (WGS), we characterized 298 Mtb isolates from Brazil. Besides the analysis of genotype distribution and possible correlations between molecular and clinical data, we determined the performance of an in-house WGS pipeline with other online pipelines for Mtb lineages and drug resistance profile definitions. Sub-lineage 4.3 (52%) was the most frequent genotype, and the genomic approach revealed a p-XDR-TB level of 22.5%. We detected twenty novel mutations in three resistance genes, and six of these were observed in eight phenotypically resistant isolates. A cluster analysis of 170 isolates showed that 43.5% of the TB patients belonged to 24 genomic clusters, suggesting considerable ongoing transmission of DR-TB, including two interstate transmissions. The in-house WGS pipeline showed the best overall performance in drug resistance prediction, presenting the best accuracy values for five of the nine drugs tested. Significant associations were observed between suffering from fatal disease and genotypic p-XDR-TB (p = 0.03) and either phenotypic (p = 0.006) or genotypic (p = 0.0007) ethambutol resistance. The use of WGS analysis improved our understanding of the population structure of MTBC in Brazil and the genetic and clinical data correlations and demonstrated its utility for surveillance efforts regarding the spread of DR-TB, hopefully helping to avoid the emergence of even more resistant strains and to reduce TB incidence and mortality rates.

Limit state equation and failure pressure prediction model of pipeline with complex loading

Assessing failure pressure is critical in determining pipeline integrity. Current research primarily concerns the buckling performance of pressurized pipelines subjected to a bending load or axial compression force, with some also looking at the failure pressure of corroded pipelines. However, there is currently a lack of limit state models for pressurized pipelines with bending moments and axial forces. In this study, based on the unified yield criterion, we propose a limit state equation for steel pipes under various loads. The most common operating loads on buried pipelines are bending moment, internal pressure, and axial force. The proposed limit state equation for intact pipelines is based on a three-dimensional pipeline stress model with complex load coupling. Using failure data, we investigate the applicability of various yield criteria in assessing the failure pressure of pipelines with complex loads. We show that the evaluation model can be effectively used as a theoretical solution for assessing the failure pressure in such circumstances and for selecting appropriate yield criteria based on load condition differences.

A novel fiber Bragg grating-based smart clamp with macro strain measurement: design, modeling, and application to incipient looseness detection

Ensuring the safety and optimal performance of hydraulic pipelines, particularly in aircraft, is of paramount importance. The challenge of detecting incipient looseness in fasteners within a time-varying temperature environment has garnered widespread recognition, making it a focal point in the field of structural health monitoring. In this study, we propose an innovative solution—a smart clamp based on Fiber Bragg Grating (FBG). This clamp aims to detect incipient looseness in hydraulic pipe systems by measuring its macro strain. Notable advantages include its ability to avoid the chirping of the FBG, the ease of building a distributed sensing network, especially for the blind-hole connected structure, and its applicability for monitoring clamps connected with small-diameter bolt. The analytical model of the clamp under the preload of the connected bolt, along with the unique condition for the measurement, is provided. Demonstrating a force resolution [Formula: see text], the clamp exhibits a strong capability in detecting incipient looseness. Furthermore, environmental temperature interference is mitigated by configuring two FBGs. Conducting application experiments in a hydraulic system, the results indicate that the proposed clamp can effectively detect the loosening and tightening processes in real time, even under the time-varying temperature environment.

Negligible effects of read trimming on the accuracy of germline short variant calling in the human genome

Background Next generation sequencing (NGS) has become a standard tool in the molecular diagnostics of Mendelian disease, and the precision of such diagnostics is greatly affected by the accuracy of variant calling from sequencing data. Recently, we have comprehensively evaluated the performance of multiple variant calling pipelines. However, no systematic analysis of the effects of read trimming on variant discovery with modern variant calling software has yet been performed. Methods In this work, we systematically evaluated the effects of adapters on the performance of 8 variant calling and filtering methods using 14 standard reference Genome-in-a-Bottle (GIAB) samples. Variant calls were compared to the ground truth variant sets, and the effect of adapter trimming with different tools was assessed using major performance metrics (precision, recall, and F1 score). Results We show that adapter trimming has no effect on the accuracy of the best-performing variant callers (e.g., DeepVariant) on whole-genome sequencing (WGS) data. For whole-exome sequencing (WES) datasets subtle improvement of accuracy was observed in some of the samples. In high-coverage WES data (~200x mean coverage), adapter removal allowed for discovery of 2-4 additional true positive variants in only two out of seven datasets tested. Moreover, this effect was not dependent on the median insert size and proportion of adapter sequences in reads. Surprisingly, the effect of trimming on variant calling was reversed when moderate coverage (~80-100x) WES data was used. Finally, we show that some of the recently developed machine learning-based variant callers demonstrate greater dependence on the presence of adapters in reads. Conclusions Taken together, our results indicate that adapter removal is unnecessary when calling germline variants, but suggest that preprocessing methods should be carefully chosen when developing and using machine learning-based variant analysis methods.

Analysis of numerical modeling of steady-state modes of methane–hydrogen mixture transportation through a compressor station to reduce CO2 emissions

This study presents a mathematical model to evaluate the performance of gas pipelines during hydrogen injection in a gas pipeline-compressor station. The developed model presents the calculation of methane–hydrogen mixture (CH4/H2) transportation through the compressor station, where the compensation of pressure drops in the mass and energy balance takes place. Simultaneously, in the operation of the centrifugal blower system of gas compressor stations, the emissions of CO2 are considered, considering the mixing of gas media and the compression of CH4/H2. This mathematical model is realized for the pipeline transportation of hydrogen, at which the principle of mixture expansion occurs. The aim is to solve the problem of CO2 emissions at compressor stations. The optimization procedure has been formulated using a system of nonlinear algebraic equalities. The research focuses on the adaptation of existing gas transportation systems to CH4/H2 transportation and the impact of environmental risks on the operation of compressor station equipment. In this case, it is possible to determine the quantitative amount of hydrogen that can be added to natural gas. By solving the problem of finding the inner point of sets using the system of nonlinear algebraic equalities, it is possible to obtain the control parameters for safety control of technological modes of CH4/H2 mixture transportation. The study findings reveal that the consumption of gas charger and hydrogen was 50.67 and 0.184 kg/s, respectively, and the estimated efficiency resulting from the modified turbine design was 75.1 percent. These results indicate that the equipment operates more efficiently when hydrogen is being transported. The numerical analytical results indicated in this study hold practical significance for design applications. It will assist in identifying and evaluating the restrictions that may develop during the technological, operational, and design stages of decision-making.

Seismic Response of Utility Tunnel in Trapezoidal Backfilled Soil Under P Wave and Amplification Effect of Ground Motion

ABSTRACT For the seismic risk of utility tunnel in trapezoidal backfilled soil and its possible amplification effect of ground motion, the seismic response of utility tunnel in trapezoidal backfilled soil under P wave is studied by a high-precision indirect boundary element method (IBEM) with the idea of “partition fit.” The results show that the peak surface displacement appears on the backfill surface, and the softer the backfilled soil, the greater the peak surface displacement; the peak vertical displacement at oblique incidence is generally smaller than that at vertical incidence; the existence of utility tunnel can restrain the incidence wave to a certain extent, especially for the low-frequency wave; the peak displacement and stress of utility tunnel in the soft backfilled soil are all greater than that in stiff backfilled soil; the peak stress of utility tunnel at oblique incidence is greater than that at vertical incidence; the stress concentration occurs at the axillary corner; and the stress in the inner wall of utility tunnel is greater than that in the outer wall. The similar conclusions could be further confirmed from the time history analysis. The research results can provide some reference for the seismic design of utility tunnel in the backfill site and the evaluation of seismic performance of near surface pipeline, etc.

Pipeline Provenance for Analysis, Evaluation, Trust or Reproducibility

Data volumes and rates of research infrastructures will continue to increase in the upcoming years and impact how we interact with their final data products. Little of the processed data can be directly investigated and most of it will be automatically processed with as little user interaction as possible. Capturing all necessary information of such processing ensures reproducibility of the final results and generates trust in the entire process. We present Pipeline pRovenance for Analysis, Evaluation, Trust Or Reproducibility (PRAETOR), a software suite that enables automated generation, modeling, and analysis of provenance information of Python pipelines. Furthermore, the evaluation of the pipeline performance, based upon a user defined quality matrix in the provenance, enables the first step of machine learning processes, where such information can be fed into dedicated optimization procedures.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration’s (FDA’s) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Optimizing natural gas pipeline risk assessment using hybrid fuzzy bayesian networks and expert elicitation for effective decision-making strategies

Natural gas pipelines are susceptible to external and internal risk factors, such as corrosion, environmental conditions, external interferences, construction and design faults, and equipment failures. Bayesian Networks (BN) is a promising risk assessment approach widely used to evaluate these risk factors. One of BN's inherent limitations is its inability to accurately capture statistical dependencies and causal relationships, which can be overcome by incorporating expert elicitation into BN. To account for uncertainty and vagueness in assessing pipeline failure risks, fuzzy set theory (FST) can be combined with BN, commonly known as Fuzzy Bayesian Networks (FBN). This study developed an FBN framework that uses linguistic variables to calculate fuzzy probability (FPr) through domain expert elicitation, and crisp probabilities (CPr) are computed using historical incident data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). Based on the findings from the case study of the Midwest region of the USA, external factors, i.e., third-party interference, outside force, and other incidents, significantly impact pipeline performance and reliability. Diagnosis inference indicates that in the Midwest region of the USA, pipeline material and age are critical factors leading to corrosion failure by threatening pipeline integrity. The findings from this study suggested that a targeted risk mitigation strategy is paramount for minimizing the risks associated with pipeline networks.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Failure analysis of buried pipelines under the action of oblique slip faults

Oil and gas are often transported by long-distance buried pipelines. When the pipeline is subjected to faulting, the pipeline is highly susceptible to failure under axial loading. However, there is a lack of understanding of the effect of inertial loads on pipeline failure in the analysis of influencing factors under the action of faults. Most of the current studies mainly use static or quasi-static methods to analyze the performance of buried pipelines, often ignoring inertial loads, which may also have an impact on pipeline failure. Therefore, this paper proposes a three-dimensional numerical model considering the dynamic response, and adopts ABAQUS explicit dynamics module to analyze the failure of pipelines under the action of sudden oblique slip faults. Firstly, the deformation features and strain development of the pipeline under the action of oblique slip faults were obtained using response analyses, and three key parameters indicating pipeline failure (local buckling, ovalization and the range of failure) were discussed. Then, the influence of fault velocity, internal pipeline pressure and pipeline wall thickness on pipeline failure were discussed. The results show that with the increase of the failure velocity, the higher the inertia, the smaller the strain and ovalization of the pipeline, the local buckling and ovalization failure are reduced, and the failure fault displacement of the pipeline will increase while the effect on the failure range of the pipeline is small. Therefore, designers and pipeline operators should consider the effects of inertia when analyzing the failure of buried pipelines under fault action.

Experimental and analytical evaluation of the performance of hybrid steel-CFRP pipelines at high external pressures

Behavior of hybrid steel-CFRP pipe systems under external pressures is studied herein using a combination of experimental, analytical, and finite element (FE) methods. Hyperbaric chamber tests are conducted on steel tubes and those wrapped with CFRP of different thicknesses and winding orientations to evaluate their performances. Based on the experimental observations and using analytical methods, new equations are proposed that can predict the collapse and propagation pressures of the hybrid steel-CFRP pipelines. The results revealed a significant enhancement in the collapse and buckle propagation capacities of the hybrid steel-CFRP pipes, with average increases of 60% and 200% respectively, compared to bare steel pipes. The failure mode observed in the hybrid steel-CFRP pipe was characterized by a U-shaped pattern, which occurred at higher pressures than the dog-bone shape mode observed in bare steel pipes. As evidenced in the FE results, the presence of CFRP in the hybrid steel-CFRP pipe system led to improved stress distribution, enhanced buckle propagation capacity, and reduced ovality. These findings demonstrate the effectiveness of CFRP in reinforcing steel pipes and highlight the potential for the hybrid steel-CFRP pipe system to offer superior performance in deep-waters.

Spatial-Temporal Kinetic Behaviors of Micron-Nano Dust Adsorption along Epoxy Resin Insulator Surfaces and the Physical Mechanism of Induced Surface Flashover.

The advanced Gas Insulated Switchgear/Gas Insulated Lines (GIS/GIL) transmission equipment serves as an essential physical infrastructure for establishing a new energy power system. An analysis spanning nearly a decade on faults arising from extra/ultra-high voltage discharges reveals that over 60% of such faults are attributed to the discharge of metal particles and dust. While existing technical means, such as ultra-high frequency and ultrasonic sensing, exhibit effectiveness in online monitoring of particles larger than sub-millimeter dimensions, the inherent randomness and elusive nature of micron-nano dust pose challenges for effective characterization through current technology. This elusive micron-nano dust, likely concealed as a latent threat, necessitates special attention due to its potential as a "safety killer". To address the challenges associated with detecting micron-nano dust and comprehending its intricate mechanisms, this paper introduces a micron-nano dust adsorption experimental platform tailored for observation and practical application in GIS/GIL operations. The findings highlight that micron-nano dust's adsorption state in the electric field predominantly involves agglomerative adsorption along the insulator surface and diffusive adsorption along the direction of the ground electrode. The pivotal factors influencing dust movement include the micron-nano dust's initial position, mass, material composition, and applied voltage. Further elucidation emphasizes the potential of micron-nano dust as a concealed safety hazard. The study reveals specific physical phenomena during the adsorption process. Agglomerative adsorption results in micron-nano dust speckles forming on the epoxy resin insulator's surface. With increasing voltage, these speckles undergo an "explosion", forming an annular dust halo with deepening contours. This phenomenon, distinct from the initial adsorption, is considered a contributing factor to flashovers along the insulator's surface. The physical mechanism behind flashovers triggered by micron-nano dust is uncovered, highlighting the formation of a localized short circuit area and intense electric field distortion constituted by dust speckles. These findings establish a theoretical foundation and technical support for enhancing the safe operational performance of AC and DC transmission pipelines' insulation.

Template-specific optimization of NGS genotyping pipelines reveals allele-specific variation in MHC gene expression.

Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the major histocompatibility complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, the DOC method, AmpliSAS and ACACIA, using MHC class IIβ data sets of three-spined stickleback gDNA, cDNA and "artificial" plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the gDNA-optimal parameter configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA typing and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA-typing of MHC-I revealed novel variants linked in haplotype blocks, and a higher-than-previously-reported individual MHC-I allelic diversity. In conclusion, we provide novel genotyping protocols for the three-spined stickleback MHC-I and -II genes, and evaluate the performance of popular NGS-genotyping pipelines. We also show that fine-tuned genotyping of paired gDNA-cDNA samples facilitates amplification bias-corrected MHC allele expression analysis.

Analysis of faulting destruction and water supply pipeline damage from the first mainshock of the February 6, 2023 Türkiye earthquake doublet

In 2023, two consecutive earthquakes exceeding a magnitude of 7 occurred in Türkiye, causing severe casualties and economic losses. The damage to critical urban infrastructure and building structures, including highways, railroads, and water supply pipelines, was particularly severe in areas where these structures intersected the seismogenic fault. Critical infrastructure projects that traverse active faults are susceptible to the influence of fault movement, pulse velocity, and ground motions. In this study, we used a unique approach to analyze the acceleration records obtained from the seismic station array (9 strong ground motion stations) located along the East Anatolian Fault (the seismogenic fault of the MW7.8 mainshock of the 2023 Türkiye earthquake doublet). The acceleration records were filtered and integrated to obtain the velocity and displacement time histories. We used the results of an on-site investigation, jointly conducted by China Earthquake Administration and Türkiye’s AFAD, to analyze the distribution of PGA, PGV, and PGD recorded by the strong motion array of the East Anatolian Fault. We found that the maximum horizontal PGA in this earthquake was 3.0 g, and the maximum co-seismic surface displacement caused by the East Anatolian Fault rupture was 6.50 m. As the fault rupture propagated southwest, the velocity pulse caused by the directional effect of the rupture increased gradually, with the maximum PGA reaching 162.3 cm/s. We also discussed the seismic safety of critical infrastructure projects traversing active faults, using two case studies of water supply pipelines in Türkiye that were damaged by earthquakes. We used a three-dimensional finite element model of the PE (polyethylene) water pipeline at the Islahiye State Hospital and fault displacement observations obtained through on-site investigation to analyze pipeline failure mechanisms. We further investigated the effect of the fault-crossing angle on seismic safety of a pipeline, based on our analysis and the failure performance of the large-diameter Thames Water pipeline during the 1999 Kocaeli earthquake. The seismic method of buried pipelines crossing the fault was summarized.

Cosmological Distance Measurement of Twelve Nearby Supernovae IIP with ROTSE-IIIb

We present cosmological analysis of 12 nearby (z < 0.06) Type IIP supernovae (SNe IIP) observed with the ROTSE-IIIb telescope. To achieve precise photometry, we present a new image-differencing technique that is implemented for the first time on the ROTSE SN photometry pipeline. With this method, we find up to a 20% increase in the detection efficiency and significant reduction in residual rms scatter of the SN lightcurves when compared to the previous pipeline performance. We use the published optical spectra and broadband photometry of well-studied SNe IIP to establish temporal models for ejecta velocity and photospheric temperature evolution for our SNe IIP population. This study yields measurements that are competitive with other methods even when the data are limited to a single epoch during the photospheric phase of SNe IIP. Using the fully reduced ROTSE photometry and optical spectra, we apply these models to the respective photometric epochs for each SN in the ROTSE IIP sample. This facilitates the use of the Expanding Photosphere Method (EPM) to obtain distance estimates to their respective host galaxies. We then perform cosmological parameter fitting using these EPM distances, from which we measure the Hubble constant to be 72.9−4.3+5.7kms−1Mpc−1 , which is consistent with the standard ΛCDM model values derived using other independent techniques.