Intensity Measurements Research Articles

Quantitative phase imaging endoscopy with a metalens

Quantitative phase imaging (QPI) recovers the exact wavefront of light from intensity measurements. Topographical and optical density maps of translucent microscopic bodies can be extracted from these quantified phase shifts. We demonstrate quantitative phase imaging at the tip of a coherent fiber bundle using chromatic aberrations inherent in a silicon nitride hyperboloid metalens. Our method leverages spectral multiplexing to recover phase from multiple defocus planes in a single capture using a color camera. Our 0.5 mm aperture metalens shows robust quantitative phase imaging capability with a 28∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${28}^{\\circ}$$\\end{document} field of view and 0.2π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${2}{\\pi}$$\\end{document} phase resolution ( ~ 0.1λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${1}{\\lambda}$$\\end{document} in air) for experiments with an endoscopic fiber bundle. Since the spectral functionality is encoded directly in the imaging lens, the metalens acts both as a focusing element and a spectral filter. The use of a simple computational backend will enable real-time operation. Key limitations in the adoption of phase imaging methods for endoscopy such as multiple acquisition, interferometric alignment or mechanical scanning are completely mitigated in the reported metalens based QPI.

Measurement of acute postoperative pain intensity in orthopedic trials: a qualitative concept elicitation study.

Pain intensity is an important outcome in clinical trials of surgery because pain relief is important to patients. Currently, recommended scales are the numeric rating scale 0-10 and visual analogue scale. However, these scales allow for considerable influence of individual imagination, previous experience, and coping skills, limiting proficiency in comparative clinical trials. We aimed to explore postoperative expressions of "how much it hurts"-the first step to improve pain intensity measurement. This was a qualitative study using inductive content analysis: words and visual cues describing pain intensity were collected from (i) existing pain intensity measures by search of COSMIN, PubMed, and Google, (ii) patient interviews recorded and transcribed word-for-word, (iii) clinician interviews transcribed likewise, and (iv) 100 patient telephone interviews with notes taken. After familiarization, the collected expressions were labelled inductively in categories and assembled in tables (case and theme-based matrices). Descriptors fell into 12 categories: intensity (slight/strong), evaluative (negligible/unbearable), cognitive impact (distracting/can be ignored), activity impact (limits some/all activity), sleep impact (can/cannot sleep), examples (like stubbing a toe), physical signs (crying/writhing), associated symptoms (nauseating/tiring), treatment (ice helps/need morphine), affective (annoying/dreadful), discriminative (aching/piercing), and general recovery (hindering recovery/functional interference). Many visual cues were also identified. Literature and recorded interviews gave rise to the categories, and telephone interviews found saturation, providing no further categories. Pain intensity is expressed by terms that fall into 12 categories and by a variety of graphic elements. This advances development of a patient-reported outcome measure of pain intensity for orthopedic trials.

Optimized evaluation of ground motion intensity measures for the prefabricated cantilevered roadbed structure in mountainous areas

The prefabricated cantilevered roadbed structure (PCRS) is a novel design that addresses the significant excavation and embankment volume associated with traditional mountainous roadbeds. The seismic performance of it has not yet been fully investigated. Seismic fragility analysis is one of the main methods for assessing the damage pattern of a structure, and the ground motion intensity measure (IM) is a key parameter affecting the damage assessment results. To this end, a finite element model is first conducted based on structural load-bearing capacity verification experiments, relying on a prefabricated cantilevered roadbed structure in a mountainous region. Then, the nonlinear time-history analysis of the structure was carried out, and 20 common IMs were selected and compared from the perspectives of practicality, efficiency, proficiency, and sufficiency. Finally, the seismic fragility analysis of the structure is conducted based on the optimal IM. The analysis results indicate that the peak ground acceleration of response spectrum (PSA) is the optimal IM for such structures. Quadratic fitting exhibits higher accuracy compared to linear fitting. Among all components, fixed-end foundations are identified as the most vulnerable. Anchored reinforcement remains elastic throughout the seismic response calculations and suffers little damage.

Impact of postpartum physical activity on maternal depression and anxiety: a systematic review and meta-analysis

ObjectiveTo examine the influence of postpartum exercise on maternal depression and anxiety.DesignSystematic review with random effects meta-analysis and meta-regression.Data sourcesOnline databases up to 12 January 2024, reference lists, recommended studies...

Solar Panel Light Intensity and Voltage Measurement System Using Atmega 328

This research explores the development of a real-time measurement system for light intensity and voltage in solar panels. It utilizes the ATmega328 microcontroller with an INA219 sensor for voltage measurement, a BH1750 sensor for light intensity measurement, and a logger module. The objective of this research is to assist and reduce losses in the use of solar panels. The research method is a simplified version of the 4D model, reduced to 3D: define, design, and develop. This system is designed to accurately collect data directly from solar panels, with a recorded data error rate of 23%. The gathered data is stored via an SD card on the logger module and converted into CSV text format. Test results indicate that this system can measure accurately and store data in a readily accessible and processable format. ATmega328, as the system's core, allows accurate real-time monitoring of solar panel conditions while storing data in CSV format, facilitating further analysis. Therefore, this system can be a valuable tool for monitoring and enhancing the energy efficiency produced by solar panels.

Probabilistic seismic demand analysis for bridges based on earthquake scenarios

The current probabilistic seismic demand analysis of structures predominantly considers the correlation between the ground motion intensity measure (IM) and structural seismic demand, but fails to effectively capture regional source characteristics. It is essential to integrate earthquake engineering with seismic response of structures using source parameters to quantify regional ground motion uncertainties and determine appropriate IMs. This study presented a probabilistic seismic demand analysis method for structures based on earthquake scenarios, characterized by 1) utilizing the stochastic finite-fault method to simulate ground motions at a specific site instead of employing multiple seismic waves from different source areas, and 2) incorporating the correlation between IMs and the key indicator of source parameters, stress drop, into the probabilistic seismic demand analysis. Taking a continuous girder bridge as an example, the performance of the stress drop and 14 individual IMs in predicting the longitudinal seismic demand and reflecting source characteristics was evaluated. The results revealed that it was difficult to establish a reliable relationship between the stress drop and structural seismic demand. Owing to the complexities of ground motion and structural parameters, the individual IMs were challenging to effectively capture their characteristics simultaneously. Therefore, composite IMs have been proposed to cover the characteristics of both the source and seismic demand. The optimal solutions were derived using a multi-objective genetic algorithm, which exhibited desirable capabilities in both aspects, providing a comprehensive guide for the seismic performance assessment of regional transportation networks.

Exercise intensity measurement using fractal analysis of heart rate variability: Reliability, agreement and influence of sex and cardiorespiratory fitness

ABSTRACT The study aimed to establish the test-retest reliability of detrended fluctuation analysis of heart rate variability (DFA-α1) based exercise intensity thresholds, assess its agreement with ventilatory- and lactate-derived thresholds and the moderating effect of sex and cardiorespiratory fitness (CRF) on the agreement. Intensity thresholds for thirty-seven participants (17 females) based on blood lactate (LT1/LT2), gas-exchange (VT1/VT2) and DFA-α1 (αTh1/αTh2) were assessed. Heart rate (HR) at αTh1 and αTh2 showed good test-retest reliability (coefficient of variation [CV] < 6%), and moderate to high agreement with LTs (r = 0.40 – 0.57) and VTs (r = 0.61 – 0.66) respectively. Mixed effects models indicated bias magnitude depended on CRF, with DFA-α1 overestimating thresholds versus VTs for lower fitness levels (speed at VT1 <8.5 km⋅hr−1), while underestimating for higher fitness levels (speed at VT2 >15 km⋅hr−1; VO2max >55 mL·kg−1·min−1). Controlling for CRF, sex significantly affected bias magnitude only at first threshold, with males having higher mean bias (+2.41 bpm) than females (−1.26 bpm). DFA-α1 thresholds are practical and reliable intensity measures, however it is unclear if they accurately represent LTs/VTs from the observed limits of agreement and unexplained variance. To optimise DFA-α1 threshold estimation across different populations, bias should be corrected based on sex and CRF.

A novel proxy for energy flux in multi-era wildfire reconstruction

Escalations in wildfire activity are of significant global concern, particularly within vulnerable wetland ecosystems integral to natural carbon sequestration and climate change mitigation. Our understanding and management of future wildfire activity may be better contextualised through the study of historic and ancient fire records, independent of human influence. Methods of study include ‘geothermometry’ - approximating ancient fire intensity from temperature-dependent changes in the chemistry of fossil charcoal. Though well established in their relation to experimental charcoalification, these methods still fail to quantify the true intensity of ancient fires, as a measure of energy release. As a result, their applicability, and contributions to the characterisation of modern fire activity, remain uncertain. Here, we present a novel measure of wildfire energy release, as a proxy for true intensity, through the co-application of cone calorimetry and Raman spectroscopy of charcoal. By applying a range of wildfire heat fluxes to variable peatland fuel mixes, this research demonstrates the complexity in correlating fire behaviour and charcoal microstructure. Further statistical analyses suggest a correlation between spectroscopic results, measures of CO and CO2 release, and fire severity. This offers a principal measure of ancient wildfire intensity, consistent with modern practices in wildfire modelling, monitoring, and management.

Machine learning-based prediction of seismic response of elevated steel tanks

Seismic response analysis assesses structural safety and integrity during earthquakes and guides design for optimal performance, compliance with regulations, and risk mitigation. This study proposes a novel approach utilizing machine learning (ML) techniques to predict the seismic response of elevated steel tanks accurately. ML is a powerful tool that can handle complexity, improve accuracy, provide data-driven insights, and optimize designs for predicting seismic responses of structures. Moreover, this study evaluates various ML models containing Random Forest, Support Vector Regression, Adaptive Boosting, LightGBM, Histogram-based Gradient Boosting, XGBoost, CatBoost, Bagging Regression, and Decision Trees to identify the most effective predictive model. In general, the developed ML-based predictive models provide a promising tool for engineers involved in the seismic design and retrofitting of elevated steel tanks, aiding in optimizing designs and enhancing structural safety and resilience against seismic hazards. As a result, the HGB model obtained the most suitable performance compared to other developed models with higher R2 and lower RMSE. In addition, a sensitivity analysis was performed based on SHAP values, and it was found that the height of the liquid and the intensity measure were the most influential variables in the seismic response of the tank.

Estimation of Earthquake-Induced Direct Economic Losses of Portfolio Buildings Based on Seismic Fragility Surface

Regional seismic loss assessment is crucial for developing earthquake emergency plans, which can significantly reduce casualties and socioeconomic losses in urban communities. Due to the complex types of building structures in a region, it is not possible to accurately measure the damage state of building structures using a single scalar ground motion intensity measure (IM). To more accurately reflect the responses of structures under earthquake excitations, the seismic fragility surface models for 4 typical RC frame structures are developed based on the vector ground motion IMs. The results indicate that the fragility surface model offers a superior level of precision in capturing the inherent uncertainty of ground motion and subsequently determines a more precise probability of structural failure, outperforming traditional single scalar ground motion IM fragility curve models. This enhanced accuracy holds significant implications for regional loss assessment. To fully consider the spatial cross-correlation of vector ground motion IMs, based on the established ground motion database of 8778 records, principal component analysis (PCA) and geostatistical analysis methods are used to construct a spatial cross-correlation influence field that simultaneously considers multiple ground motion IMs to simulate more realistic earthquake scenarios. Considering the impact of uncertainty in loss ratio on earthquake-induced direct economic loss estimation results, the maximum entropy technique is used in conjunction with an improved Latin Hypercube Sampling (LHS) to achieve spatially uniform sampling of loss ratio (LR). This study proposes an assessment framework for regional seismic direct economic loss for portfolio buildings based on the seismic fragility surface model considering spatial cross-correlation and uncertainty of loss ratio. The proposed framework is verified by numerical examples, and it is proven that the regional portfolio buildings considering multiple ground motion IMs is more reliable and robust than those merely considering a single ground motion IM, and when spatial cross-correlation is not considered, the probability of serious economic losses will be underestimated.

High correlation of quantitative susceptibility mapping and echo intensity measurements of nigral iron overload in Parkinson's disease.

Quantitative susceptibility mapping (QSM) and transcranial sonography (TCS) offer proximal evaluations of iron load in the substantia nigra. Our prospective study aimed to investigate the relationship between QSM and TCS measurements of nigral iron content in patients with Parkinson's disease (PD). In secondary analyses, we wanted to explore the correlation of substantia nigra imaging data with clinical and laboratory findings. Eighteen magnetic resonance imaging and TCS examinations were performed in 15 PD patients at various disease stages. Susceptibility measures of substantia nigra were calculated from referenced QSM maps. Echogenicity of substantia nigra on TCS was measured planimetrically (echogenic area) and by digitized analysis (echo-intensity). Iron-related blood serum parameters were measured. Clinical assessments included the Unified PD Rating Scale and non-motor symptom scales. Substantia nigra susceptibility correlated with echogenic area (Pearson correlation, r = 0.53, p = 0.001) and echo-intensity (r = 0.78, p < 0.001). Individual asymmetry indices correlated between susceptibility and echogenic area measurements (r = 0.50, p = 0.042) and, more clearly, between susceptibility and echo-intensity measurements (r = 0.85, p < 0.001). Substantia nigra susceptibility (individual mean of bilateral measurements) correlated with serum transferrin saturation (Spearman test, r = 0.78, p < 0.001) and, by trend, with serum iron (r = 0.69, p = 0.004). Nigral echogenicity was not clearly related to serum values associated with iron metabolism. Susceptibility and echogenicity measurements were unrelated to PD duration, motor subtype, and severity of motor and non-motor symptoms. The present results support the assumption that iron accumulation is involved in the increase of nigral echogenicity in PD. Nigral echo-intensity probably reflects ferritin-bound iron, e.g. stored in microglia.

Experimental study for a field diversity phase retrieval wavefront sensing approach

Field diversity wavefront sensing is one of the image-based wavefront methods, where the intensity measurements with phase diversities are directly obtained from different field positions of one image, without the need for any additional instruments (e.g., beam splitter) or operations (e.g., focus adjusting). While the phase diversities between different positions are unknown to us, this method is realized based on an in-depth understanding of the net aberration fields induced by misalignments and figure errors. However, this novel, to the best of our knowledge, image-based wavefront sensing method has not been experimentally studied, which restricts the application and promotion of this method. In this work, the analytic gradient of the field diversity wavefront sensing is derived, and the accuracy and effectiveness of this method in the active alignment of real three-mirror anastigmatic (TMA) optical systems are systematically demonstrated. The results show that this method can be applicable to wavefront sensing of large space telescopes.

In-line spectroscopy for iodine quantification in dynamic contrast-enhanced dedicated breast CT.

In 4D dynamic contrast-enhanced dedicated breast computed tomography (4D DCE-bCT), the functional properties of the breast will be characterized by monitoring the uptake and washout of iodine-based contrast agents over time. This information could be valuable in breast cancer treatment. However, prior to clinical implementation, it is crucial to validate the quantitative estimates of iodine concentrations at each time point during acquisition. To develop an in-line spectroscopy system capable of measuring iodine concentrations in a dynamic x-ray breast phantom in real-time. Potassium iodide served as the contrast agent. The system was set-up at both the entrance and exit of the phantom. It comprises a fiber-coupled green LED and collimator, which together ensure that a parallel beam passes through the sample holder. Transmitted light is captured by a collimator on the opposite side and directed through a fiber optic cable to a photodetector for intensity measurement. The relationship between 13 iodine concentrations (0-6mg I/mL) and light transmission was tested, and the system's repeatability and accuracy were determined. The system exhibited a strong correlation between iodine concentration and transmission values, achieving a root-mean-square error of 0.007. The repeated measurements had relative standard deviations of 0.04% and 0.1% for repeated water measurements at the phantom's entrance and exit, respectively. Furthermore, the accuracy measurements gave a mean error of fitting of 0.008(±0.07) mg I/mL. The in-line spectroscopy system can effectively monitor iodine concentrations in a dynamic breast phantom, providing a reliable method for quantitative validation of 4D DCE-bCT.

An in situ, prepulse insensitive and comparatively accurate intensity measurement of ultra-strong lasers based on stochastic radiation reactions

An in situ, prepulse insensitive and comparatively accurate intensity measurement of ultra-strong lasers based on stochastic radiation reactions

Development of an Experimental Acoustic Noise Characterization Setup for Electric Motor Drive Applications

This paper presents the development of an experimental setup for acoustic noise characterization of electric motors. It describes the sound measurement microphones utilized in the setup and discusses the application of octave bands and A-weighting in noise measurement. Various methods for acoustic noise measurement and sound power calculation, including those based on sound pressure and sound intensity, are also covered. Given the relatively noisy test environment and restricted access around the test setup, discrete point sound intensity measurement is selected for sound power calculation. Initially, a stationary probe-holding fixture is designed and fabricated for sound intensity measurements. To enhance the fixture’s flexibility and the accuracy of the measurements, a transportable fixture is subsequently designed and fabricated. The necessary hardware and software settings for acoustic noise characterization are then developed. Finally, the setup is used to conduct acoustic noise characterization of an IPM motor, validating the application of the transportable probe-holding fixture.

Investigating uncertainties in air quality models used in GMAP/SIJAQ 2021 field campaign: General performance of different models and ensemble results

The international field campaign, GMAP/SIJAQ 2021, was conducted in Korea from October 18th to November 25th to enhance the performance and validation of the Geostationary Environment Monitoring Spectrometer (GEMS) products algorithm and obtain a better understanding of the current air pollution status of the Korean Peninsula. Five chemical transport models (CTMs), including CMAQ, CMAQ-GIST, CAMx, WRF-Chem, and WRF GEOS-Chem, were utilized during the campaign to assist in organizing the observation plan and identifying changes in pollutant concentrations and their spatiotemporal distribution in Korea following the Korea–United States Air Quality (KORUS-AQ) 2016. In this study, we evaluated the forecasting performance, strengths, and limitations of these five CTMs and their ensemble in simulating air quality. Intensive measurement data and intercomparisons were employed to explain discrepancies between observed and simulated results. A comparison of the CTM ensemble results for PM2.5 and various gaseous pollutants between the current GMAP/SIJAQ 2021 and previous KORUS-AQ 2016 campaigns showed the R-value for the total mass PM2.5 concentration increased from 0.88 to 0.94. This improvement is related to CTM updates, including the emission inventory and better reproductions of the concentrations of gaseous species. However, the models consistently underestimated carbon monoxide (CO) concentrations, similar to the results from KORUS-AQ. This finding still suggests a further challenge that requires consideration of missing anthropogenic sources. The results of the ensemble model agreed well with the chemical composition of PM2.5 observed at the intensive monitoring station. However, for NO3− and NH4+, discrepancies were primarily due to inaccuracies in the meteorological inputs, such as precipitation, relative humidity (RH), and nighttime planetary boundary layer height (PBLH) in the CTMs. Hence, all models overestimated the concentration of elemental carbon (EC), therefore, it is necessary to revise EC emissions in the SIJAQv2 inventory, as these apply to unusual levels recorded in Seoul during the reference year of 2018.

An artificial intelligence-based landmark model for early detection of aortic stenosis in Cardiac MRI

Abstract Background The utilisation of ratiometric intensity measures from cardiac magnetic resonance imaging (CMR) offers a promising biomarker for the diagnosis of aortic stenosis (AS)[1]. The Ao:LV ratio, an approximation of AS severity, is determined by comparing the relative blood signal intensity in the aorta and left ventricle (LV) within three-chamber (3Ch) cine sequences[1]. In patients with AS, the blood signal intensity of aortic blood above the valve is diminished compared to that within the LV in steady-state free precession (SSFP) 3Ch CMR cine. Although the Ao:LV ratio serves as a reliable indicator of AS severity, manual computation of this ratio may encounter difficulties due to visual obstructions caused by small LV cavities or prominent papillary muscles. Purpose This study aims to leverage artificial intelligence (AI) to automate the Ao:LV ratio analysis from 3Ch SSFP cine images and introduce the Ao:LA (aorta to left atrium) ratio, thus enhancing the diagnostic accuracy for AS severity. Methods Our methodology extends the ratiometric analysis to include both Ao:LV and Ao:LA ratios in three main steps: first, developing an AI-based algorithm for automated detection and tracking of cardiac landmarks; second, using these landmarks to automatically compute regions of interest (ROIs) in the cardiac chambers; and third, training and validating classification models with 5-fold cross-validation to select the most effective for AS severity classification. The final model's performance is assessed on a holdout test set. Data for model training and evaluation were sourced from CMR scans across three hospitals and two scanner types. Results We analysed 220 patients of which 78 patients had no AS and 122 had AS (mild AS, n = 29; moderate AS; n = 35, severe AS; n = 78). Our model yielded high efficacy in classifying AS, particularly in differentiating any grade of AS from a normal classification, which is clinically relevant. The Ao:LA ratio demonstrated a stronger correlation with AS severity class than Ao:LV, suggesting a more reliable biomarker. The proposed model demonstrated robust performance in AS classification, achieving an Area Under the Curve (AUC) of 0.845 in the automated binary classification of AS at any grade. The model exhibited a precision of 0.889, recall of 0.865, and an F1 score of 0.877 on a holdout set. Conclusion By the integration of AI in automating the analysis of CMR images through a novel ratiometric approach that includes Ao:LV and Ao:LA ratios, our model significantly improves the accuracy of early AS detection. This advancement enhances imaging strategies for AS evaluation, promoting more efficient and effective use of cardiac MRI sequences as a diagnostic tool for aortic stenosis. It has the potential to be implemented in real-time scanning protocols as a clinical decision support system.AI-inferred cardiac landmarks.ROC curve delineating AS prediction.

Characterization of Defocused Coherent Imaging Systems with Periodic Objects

Recent advancements in quantum and quantum-inspired imaging techniques have enabled high-resolution 3D imaging through photon correlations. These techniques exhibit reduced degradation of image resolution for out-of-focus samples compared to conventional methods (i.e., intensity-based incoherent imaging). A key advantage of these correlation-based approaches is their independence from the system numerical aperture (NA). Interestingly, both improved resolution of defocused images and NA-independent scaling are linked to the spatial coherence of light. This suggests that while correlation measurements exploit spatial coherence, they are not essential for achieving this imaging advantage. This discovery has led to the development of optical systems that achieve similar performance by using spatially coherent illumination and relying on intensity measurements: direct 3D imaging with NA-independent resolution was recently demonstrated in a correlation-free setup using LED light. Here, we explore the physics behind the enhanced performance of defocused coherent imaging, showing that it arises from the modification of the sample’s spatial harmonic content due to diffraction, unlike the blurring seen in conventional imaging. The results we present are crucial for understanding the implications of the physical differences between coherent and incoherent imaging, and are expected to pave the way for the practical application of the discovered phenomena.

A case of severe psittacosis pneumonia complicated by splenic infarction

Clinical dataChlamydia psittaci pneumonia is a community-acquired pneumonia caused by Chlamydia psittaci. While severe cases may lead to critical conditions such as respiratory failure, splenic infarction is relatively uncommon. A severe patient with Chlamydia psittaci pneumonia admitted to our hospital experienced a splenic infarction during treatment. Fortunately, the patient’s situation was improved after careful treatment. Now, the patient has been discharged. Further exploration of the mechanism of concurrent splenic infarction is required.BackgroudPsittacosis pneumonia, a zoonotic infectious disease transmitted from birds to humans, is caused by Chlamydia psittaci and represents a type of chlamydial pneumonia [1]. Insome instances, the disease may progress to severe pneumonia and respiratory failure, necessitating intensive support measures, including mechanical ventilation. The advent of technologies such as Metagenomic Next-Generation Sequencing (mNGS) for the etiological diagnosis of infectious diseases [2] has improved the diagnostic and treatment success rates for Psittacosis. Instances of severe chlamydial pneumonia with complications such as splenic infarction are uncommon. A patient with severe Psittacosis pneumonia complicated by splenic infarction was admitted to the Emergency Intensive Care Unit (EICU) of Haining People’s Hospital and subsequently improved following effective anti-infective and anticoagulant therapy. This report is provided herein.

A codifiable methodology for estimating pallet sliding displacements on steel racking systems

AbstractTo facilitate the logistic processes within a modern warehouse, the contents of steel racking systems are not mechanically connected to the supporting beams, allowing a content‐sliding mechanism to develop when static friction is exceeded. Given that the mass of contents is dominant, this is beneficial for limiting the apparent inertia. On the other hand, pallet fall‐off may occur during strong seismic excitations, which is a failure mode that is not addressed by current seismic design processes and guidelines for racks. Along these lines, a codifiable methodology is proposed for estimating sliding displacements on steel racking systems, based on the statistical interpretation of a large set of response history analyses, using different rack configurations and ground motions. Firstly, a multi‐parametric analysis is conducted using simplified rack models to (i) select the intensity measure and engineering demand parameter that can best describe the problem of pallet sliding and (ii) identify the salient rack characteristics that dominate sliding behavior. Thereafter, a series of multi‐stripe analyses are performed using 180 rack realizations with different feature combinations to derive a so‐called, Empirical Sliding Prediction Equation (ESPE) by following a three‐step procedure: (a) perform regression on maximum sliding, (b) perform regression on the normalized sliding profile, and (c) combine steps a‐b to derive the denormalized profile at a given confidence level. The proposed empirical relationships are then validated through a comparison between the observed and fitted sliding displacements in three rack case studies.