Point-wise Comparisons Research Articles

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients.

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance. At baseline, compared with HCs, patients with RRMS showed a widespread decrease in FA and increases in MD, AD, and RD among tracts. In the pointwise comparisons, more detailed abnormalities were localized to specific positions. At follow-up, although there was no significant difference in the entire WM fibre tract, there was a reduction in FA in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and elevations in MD and AD in the anterior and posterior portions of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS patients. RRMS patients exhibited widespread WM microstructure alterations at baseline and alterations in certain regions at follow-up, and the altered metrics were widely correlated with cognitive performance in RRMS patients, which will enhance our understanding of WM microstructure damage and its cognitive correlation in RRMS patients.

Characterizing White Matter Changes along Fibers in Treatment-Naive Pediatric Posttraumatic Stress Disorder

Children and adolescents are more susceptible than adults to developing posttraumatic stress disorder (PTSD). Pediatric PTSD is characterized by functional alterations in brain fear circuitry, but little is known about the underlying microstructural changes; previous work has mainly focused on the corpus callosum. This study is aimed at investigating brain-wide microstructural abnormalities in pediatric PTSD, their relationship to age and sex, and their potential diagnostic value. The microstructure of major white matter tracts was assessed from diffusion tensor images acquired from 24 treatment-naive non-comorbid PTSD patients <18 years and 24 trauma-exposed non-PTSD controls (TENP) matched for age, sex, and years of education. Statistical analyses included pointwise comparisons, correlations with symptom severity, and diagnosis-by-age/sex interactions; support vector machine analyses were conducted to determine whether microstructure distinguishes PTSD from TENP. Compared with TENP, pediatric PTSD patients showed higher fractional anisotropy and lower radial diffusivity in right superior longitudinal fasciculus and lower axial diffusivity in right uncinate fasciculus. These white matter microstructural abnormalities were highly correlated with PTSD symptom severity. No significant diagnosis by age or sex interaction was observed. The pointwise axial diffusivity measurements presented the best PTSD vs. TENP classification performance. In summary, pediatric PTSD patients showed clinically relevant microstructural abnormalities in uncinate and superior longitudinal fasciculus, which extend understanding of pediatric PTSD neurobiology beyond the corpus callosum and have diagnostic potential in distinguishing stressed individuals with and without PTSD.

Quick bathymetry mapping of a Roman archaeological site using RTK UAS-based photogrammetry

Recent technological advances are leading numerous researchers and professional users to the adoption of photogrammetric products for a wide range of geoscientific applications. Especially, drone-based Structure-from-Motion (SfM) photogrammetry is often applied as a high-resolution topographic modelling technique with advantages in terms of time and cost when compared to traditional surveying approaches. The aim of this work, carried out within the GeoArchaeo Sites Project, is to investigate the adaptability of drone-based surveys, even with a targetless approach, and to estimate bathymetrical accuracies in shallow waters. The approach was applied on an underwater site to show the potential for the digitalization and monitoring of an archaeological coastal geosystem in central Italy. Specifically, this work has compared the photogrammetric surveying capability of two drones including a Phantom 4 RTK (‘P4RTK’) and a low-cost Mavic Mini 2 (‘MM2’) and an Olympus TG-6 (underwater camera) for a site submerged with a maximum depth of ∼1.6 m. The assessment of the drone SfM-based products was performed through area-based and point-wise comparisons. Specifically, the area-based were assessed through an underwater photogrammetric survey obtained by acquiring images by an operator snorkeling along a portion of the site of interest. The point-wise comparison was performed using data acquired with a Global Navigation Satellite System (GNSS). This study demonstrates that coupling SfM-photogrammetry and UAS-based surveys have potential to define submerged topography. In particular, the imagery acquired with the P4RTK survey can produce dense 3D models of the underwater surface with high resolution (about 0.02 m) and bathymetric measurements with a vertical accuracy ranging between 0.06 and 0.29 m for the area-based and point-wise analysis, respectively. Thus, the approach adopted and tested involving the use of a P4RTK has the potential to reduce constraints and limitations in terms of GCPs distribution and measurement. Also, with such an approach the need for qualified operators for underwater photogrammetric workflow can be avoided.

Are Speech Perception Scores in Cochlear Implant Recipients Consistent Across Different Tests?

Cochlear implant (CI) candidacy and postoperative outcomes are assessed using sets of speech perception tests that vary from center to center, limiting comparisons across institutions and time periods. The objective of this study was to determine if scores on one speech perception test could be reliably predicted from scores on another test. Arizona Biomedical (AzBio) Sentence Test, Consonant-Nucleus-Consonant word (CNCw), and Hearing in Noise Test (HINT) scores in quiet for the implanted ear were collected for individuals who received a CI between 1985 and 2019. Scores collected during the same testing session were analyzed using Bland-Altman plots to assess agreement between testing methods. Simple linear regression with logit transformation was used to generate predictive functions and 95% confidence intervals for expected mean and individual scores. Single academic medical center. A total of 1,437 individuals with a median age of 59.9 years (range, 18-95 yr) and 46% (654 of 1,437) male. N.A. Agreement as a function of test score, mean, variance, and correlation coefficients. A total of 2,052 AzBio/CNCw, 525 AzBio/HINT, and 7,187 CNCw/HINT same-session score pairings were identified. Pairwise test comparisons demonstrated limited agreement between different tests performed in the same session, and a score correlation between different speech tests revealed large variances. Transformation functions between test batteries were predictive of mean scores but performed poorly for prediction of individual scores. Point-wise comparisons of scores across CI test batteries should be used with caution in clinical and research settings.

Combining wavelet analysis of track irregularities and vehicle dynamics simulations to assess derailment risks

Guaranteeing operational safety is mandatory for railways worldwide. As track deteriorates, its geometry deviates from ideal conditions (e.g. perfectly aligned and levelled), which creates undesired vibration and oscillations to the vehicle, increasing dynamic loads and derailment risk. Although current standards establish nominal limits for the track geometry, such point-wise comparisons may overlook relevant track features that can lead to unsafe conditions. The proposed investigation is a contribution to the study of the response of the rail vehicle to the track quality, aimed at identifying track irregularities that produce high track forces and unsafe vehicle responses. To fulfil this aim, Wavelet Analysis is combined with Vehicle Dynamics Simulations to evaluate how the track irregularities, filtered in various wavelength ranges and reconstructed with different wavelets and coefficients' amplitudes, impact vehicle safety in terms of the Nadal safety criterion Y/Q. The simulations demonstrate that defects occurring in some scales/frequencies are more harmful than others, and the similarity between the geometric defects and the shape of the wavelets may be used to identify track locations that require maintenance. These findings suggest that it is possible to create ‘stamps’ of track irregularities that can be used by infrastructure managers to improve their maintenance strategies.

On some nonlocal problems in the calculus of variations

The goal of this paper is to investigate some nonlocal problems of the calculus of variations where pointwise comparisons principles fail but where some kind of nonlocal maximum principle or monotonicity property remains true for some quantities attached to the problem. A special attention will be given to a nonlocal energy recently introduced in Mikayelyan (2018).

Psychoradiological abnormalities in treatment-naive noncomorbid patients with posttraumatic stress disorder.

BackgroundNeuroimaging studies in posttraumatic stress disorder (PTSD) have identified various alterations in white matter (WM) microstructural organization. However, it remains unclear whether these are localized to specific regions of fiber tracts, and what diagnostic value they might have. This study set out to explore the spatial profile of WM abnormalities along defined fiber tracts in PTSD.MethodsDiffusion tensor images were obtained from 77 treatment‐naive noncomorbid patients with PTSD and 76 demographically matched trauma‐exposed non‐PTSD (TENP) controls. Using automated fiber quantification, tract profiles of fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were calculated to evaluate WM microstructural organization. Results were analyzed by pointwise comparisons, by correlation with symptom severity, and for diagnosis‐by‐sex interactions. Support vector machine analyses assessed the ability of tract profiles to discriminate PTSD from TENP.ResultsCompared to TENP, PTSD showed lower fractional anisotropy accompanied by higher radial diffusivity and mean diffusivity in the left uncinate fasciculus, and lower fractional anisotropy accompanied by higher radial diffusivity in the right anterior thalamic radiation. Tract profile alterations were correlated with symptom severity, suggesting a pathophysiological relevance. There were no significant differences in diagnosis‐by‐sex interaction. Tract profiles allowed individual classification of PTSD versus TENP with significant accuracy, of potential diagnostic utility.ConclusionsThese findings add to the knowledge of the neuropathological basis of PTSD. WM alterations based on a tract‐profile quantification approach are a potential biomarker for PTSD.

Wavefield-based evaluation of DAS instrument response and array design

SUMMARY Distributed acoustic sensing (DAS) networks promise to revolutionize observational seismology by providing cost-effective, highly dense spatial sampling of the seismic wavefield, especially by utilizing pre-deployed telecomm fibre in urban settings for which dense seismic network deployments are difficult to construct. However, each DAS channel is sensitive only to one projection of the horizontal strain tensor and therefore gives an incomplete picture of the horizontal seismic wavefield, limiting our ability to make a holistic analysis of instrument response. This analysis has therefore been largely restricted to pointwise comparisons where a fortuitious coincidence of reference three-component seismometers and colocated DAS cable allows. We evaluate DAS instrument response by comparing DAS measurements from the PoroTomo experiment with strain-rate wavefield reconstructed from the nodal seismic array deployed in the same experiment, allowing us to treat the entire DAS array in a systematic fashion irrespective of cable geometry relative to the location of nodes. We found that, while the phase differences are in general small, the amplitude differences between predicted and observed DAS strain rates average a factor of 2 across the array and correlate with near-surface geology, suggesting that careful assessment of DAS deployments is essential for applications that require reliable assessments of amplitude. We further discuss strategies for empirical gain corrections and optimal placement of point sensor deployments to generate the best combined sensitivity with an already deployed DAS cable, from a wavefield reconstruction perspective.

VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION

Abstract. Point clouds acquired by light detection and ranging (lidar) and photogrammetry technology (e.g., structure from motion/multi-view stereo-SfM/MVS) are widely used for various applications such topographic mapping due to their high resolution and accuracy. To generate a digital elevation model (DEM) or extract other features in the data, the ground points and non-ground points usually need to be separated first. This process, called ground filtering, can be tedious and time consuming as it requires substantial manual effort for high quality results. Although many have developed automated ground filtering algorithms, very few have the versatility to process data acquired from different scenes and systems. In this paper, we propose a versatile ground filter based on multi-scale voxelization and smooth segments, named Vo-SmoG. The proposed method introduces a novel voxelization approach, followed by isolated voxel filtering, lowest point filtering, local smooth filtering, and ground clustering. The result of the Vo-SmoG ground filtering is a classified point cloud. The effectiveness and efficiency of our method are demonstrated qualitatively and quantitatively. The quantitative evaluation consists of both point-wise and grid-wise comparisons. The recall, precision, and F1-score are over 97% in terms of classification while the root mean squared error (RMSE) of the DEM is within 0.1 m, which is on par with the reported vertical accuracy of the tested data. We further demonstrate the versatility of the Vo-SmoG via large-scale, real-world datasets collected from different environments with mobile laser scanning, airborne laser scanning, terrestrial laser scanning, uncrewed aircraft system (UAS)-SfM, and UAS-lidar.

Comparison of Static and Dynamic Baseline Creatinine Surrogates for Defining Acute Kidney Injury

Background: “Dynamic” baseline serum creatinine (sCr), based on a rolling 48-h window, and a static baseline sCr (previous outpatient sCr) were used to define acute kidney injury (AKI). Methods: Retrospective cohort study of adult admissions to the University of Alabama (UAB) Health System hospitals for years 2016–2018. Included admissions had >1- and <180-day length of stay, >2 inpatient sCr measurements, and an averaged estimated glomerular filtration rate >15 mL/min/1.73 m². The final cohort of 62,380 patients included 100,570 admissions, 3,509 inpatient deaths, and 1,916 admissions with inpatient dialysis. AKI was defined by Kidney Disease Improving Global Outcomes (KDIGO) criteria and a static or dynamic baseline sCr. Discrimination was evaluated with area under receiver operator curves (AUC), logistic regression, and net reclassification improvement (NRI). Results: Preadmission outpatient “static” sCr values were available for 43,433 admissions. The lowest sCr value during a rolling 48-h window before each inpatient sCr defined a “dynamic” baseline sCr. Using point-wise comparisons, the dynamic baseline sCr performed better than static baseline sCr for inpatient mortality (AUC [0.819 vs. 0.741; p < 0.001] and NRI ≥0.306 [p < 0.001]) and inpatient dialysis (AUC [0.903 vs. 0.864; p < 0.001] and NRI ≥0.317 [p < 0.001]). Conclusions: The dynamic baseline sCr is available without reference to preadmission sCr values and avoids confounding associated with missing outpatient sCr values. AKI defined with the dynamic baseline sCr significantly improved discrimination of risk for inpatient mortality and dialysis compared to static baseline sCr.

Comparison of Outcomes Following Allogeneic HCT for Adverse Cytogenetic/Molecular Risk AML Using T-Replete Haploidentical Donors to Optimally Matched Related and Unrelated Donors

Outcomes following allogeneic hematopoietic cell transplantation (allo-HCT) for AML patients characterized as adverse risk by cytogenetic or molecular criteria have historically been unsatisfactory. Relapse of AML is the primary cause of treatment failure in these patients and can affect the majority of patients with long term survival seen in only a minority of transplanted patients. We evaluated all consecutive patients who underwent first allo-HCT for AML classified as adverse risk on cytogenetic and molecular criteria by the European LeukemiaNet (Dohner et al Blood 2017) at our center between Feb 2003 and Feb 2017 in order to compare outcomes between patients transplanted using a T-replete HLA-haploidentical donor (HID) to those transplanted from a conventional matched sibling (MRD) or optimally matched unrelated donor (MUD). Patient characteristics and outcome data were prospectively entered into our institutional database from which they were retrospectively extracted for this analysis. Our institutional algorithm for donor selection prioritized MRD followed by 8/8 HLA matched MUD. HID were used for patients lacking timely access to a fully matched donor. All patients were treated using standardized supportive care algorithms in the outpatient setting. All HID transplants received GVHD prophylaxis using post-transplant cyclophosphamide (100mg/kg), tacrolimus (d+5 to +180) and MMF (d +5 to +35), MRD and MUD patients received GVHD prophylaxis with tacrolimus and methotrexate. A total of 78 patients were analyzed (HID =31, MUD=27 MRD=20) Patient characteristics were: median age=53 (23-73); male 47%; HCT-CI 0-2=45%, >3=55%; DRI Into=20%, High=65%, v High =15%; ELN adverse risk criterion - complex/monosomal karyotype = 81% , Other adverse cytogenetics = 13% , High-risk mutation =6% ; Disease Status at BMT CR1=60%, other=40%; Graft Source PBSC=85%, BM=15%; Regimen Intensity MAC=63%, RIC/NST =37%. Median follow-up of survivors was 42 months. Patient characteristics were not significantly different between HID, MRD and MUD transplants except that BM was more often the graft source in HID (23%) than MRD (0%) and MUD (15%) (p=0.06). For the entire population and patients with HID, MRD and MUD respectively, 3 year cumulative incidences of non-relapse mortality (NRM) were 21%, 19%, 20% 24% and of relapse were 45%, 42%, 50%, 44%. Three year Kaplan-Meier estimates of overall survival(OS) were 41%, 47%, 43%, 33% and disease-free survival (DFS) were 34%, 39%, 30%, 32% respectively (p=NS for all endpoints on pointwise and global comparisons for donor type). Disease-risk index (DRI) was a significant predictor of three year OS (77% for DRI-intermediate vs 34% for High/v.High, p=0.013 and 0.03 on pointwise and global comparison respectively), DFS (68% vs 26%, p=0.004 and 0.009), relapse (9% vs. 54%, p<0.001 and p=0.002 respectively). Regimen intensity (MAC vs RIC/NST), patient age (<55 vs >55), year of transplant (2003-2011vs 2012-2017) were not significant predictors of any outcome parameter. Female gender was associated with a lower 3 yr NRM (14% vs 29%), and higher OS (52%vs 41%) (p=0.059 and 0.058 on global comparison). These data demonstrate that in patients defined as Adverse Risk by the current ELN classification, three year DFS can be achieved in approximately a third of patients with a NRM of approximately 20%. DRI remains predictive even within this adverse risk cohort. HID donors produce equivalent outcomes to those using optimally matched conventional donors and specifically, relapse rate is not higher following HID even within this high-risk population. Thus, HID may be used to facilitate early transplantation. [Display omitted] DisclosuresSolh:ADC Therapeutics: Research Funding; Celgene: Speakers Bureau; Amgen: Speakers Bureau.

Sum things are not what they seem: Problems with point-wise interpretations and quantitative analyses of proxies based on aggregated radiocarbon dates

Radiocarbon-date assemblages are commonly used as proxies for past human and environmental phenomena. Prominent examples of target phenomena include past population levels and sea level fluctuations. These processes are thought to have affected the amount of organic carbon deposited into the archaeological and/or palaeoenvironmental record. Time-series representing through-time fluctuations in the frequency of radiocarbon samples are, therefore, often used as proxies for such processes. However, there are critical problems with using radiocarbon “dates-as-data” in point-wise comparisons and these problems have gone largely underappreciated. The key problem is that the established proxies are easily misinterpreted. They conflate process variation and chronological uncertainty, which makes them unsuitable for point-wise comparisons aimed at identifying rates of change, comparing variables directly, or estimating parameters in regression models. Here we explore the interpretive and analytical problems in detail in an effort to raise awareness and promote skepticism about the use of the established proxies in point-wise comparisons. We also provide suggestions for future research and point to potential methodological alternatives that may improve the viability of dates-as-data approaches.

DEVELOPING APACHE SPARK BASED RIPLEY’S K FUNCTIONS FOR ACCELERATING SPATIOTEMPORAL POINT PATTERN ANALYSIS

Abstract. Ripley’s K functions are powerful tools for studying the spatial arrangement or spatiotemporal distribution characteristics of geographic phenomena and events in spatial analysis and has been used in many fields. However, the K functions are compute-intensive for point-wise distance comparisons, edge correction and simulations for significance test. Although parallel computing technologies have been adopted to accelerate K functions, previous works haven’t extended the optimization from space to space-time dimension. This study presents an acceleration method for K functions upon state-of-the-art distributed computing framework Apache Spark, and four optimization strategies are leveraged to simplify calculation procedures and accelerate distributed computing respectively, including 1) spatiotemporal indexing based on R-tree with Sort-Tile-Recursive (STR) algorithm for reducing distance comparison when retrieving potential spatiotemporally neighbouring points; 2) Hash-Table-based caching for spatiotemporal edge correction weights reuse and reducing repetitive computation; 3) Spatiotemporal partitioning using KDB-tree as well as cylinder intersection redundancy strategy for decreasing ghost buffer redundancy in partitions and supporting near-balanced distributed processing; 4) Customized serialization of spatiotemporal objects and indexes for lowering the overhead of data transmission. Experiments verify the effectiveness and time efficiency of the proposed optimization strategies, and also evaluate the overall performance and scalability. Based on the proposed methods, a web-based visual analytics framework has been developed and publicly shared through GitHub, and four types of the distributed K functions are implemented, including space, space-time, local and cross K functions, which demonstrates its value on promoting geographical and socioeconomic studies.

Analysis of the applicability of two-dimensional detector arrays in terms of sampling rate and detector size to verify scanned intensity-modulated proton therapy plans.

The introduction of advanced treatment techniques in proton therapy, such as intensity-modulated proton therapy, leads to an increased need for patient-specific quality assurance, especially an accurate treatment plan verification becomes inevitable. In this study, signal theoretical analysis of dose distributions in scanned proton therapy is performed to investigate the feasibility and limits of two-dimensional (2D) detector arrays for treatment plan verification. 2D detector arrays are characterized by two main aspects: the distance between the single detectors on the array or the sampling frequency; and the lateral response functions of a single detector. The analysis is based on single spots, reference fields and on measured and calculated dose distributions of typical intensity-modulated proton therapy treatment plans with and without range shifter. Measurements were performed with Gafchromic EBT3 films (Ashland Speciality Ingredients G.P., Bridgewater, NJ, USA), the MatriXX PT detector array (IBA Dosimetry, Schwarzenbruck, Germany) and the OCTAVIUS detector array 1500XDR (PTW-Freiburg, Germany) at an IBA Proteus PLUS proton therapy system (Ion Beam Applications, Louvain-la-Neuve, Belgium). Dose calculations were performed with the treatment planning system RayStation 6 or 8 (RaySearch Laboratories, Sweden). The Fourier analysis of the data of the treatment planning system and film measurements show maximum frequencies of 0.06/mm for the plan with range shifter and 0.083/mm for the plan without range shifter. According to the Nyquist theorem, this corresponds to minimum required sampling distances of 8.3 and 6mm, respectively. By comparison, the sampling distances of the arrays of 7.6mm (MatriXX PT) and 7.1mm (OD1500XDR) are sufficient to reconstruct the dose distributions adequately from measurements if range shifters are used, whereas some fields of the plans without range shifter violated the Nyquist requirement. The lateral dose response functions of the single detectors within the arrays have clearly higher frequencies than the treatment plans and thus the volume effect only slightly influences the measurements. Consequently, the array measurements show high gamma passing rates with at least 96 % and a good agreement between the investigated line profiles. The results indicate that the detector dimensions and sampling distances of the arrays are in most studied cases adequate not to substantially influence the measurement process when they are used for analyzing typical intensity-modulated proton therapy treatment plans. Nevertheless, clinical conditions have been identified, for instance treatment plans without range shifter, under which the Nyquist theorem is violated such that a full representation of the dose distributions with the measurements is not feasible. In these cases, analysis of measurements is limited to pointwise comparisons.

Rate of visual field decay in glaucomatous eyes with acquired pits of the optic nerve

BackgroundTo evaluate the relationship between the presence of an acquired pit of the optic nerve (APON) and the rate of visual field (VF) decay in primary open-angle glaucoma (POAG).MethodsConsecutive patients...

Quantifying similarity for spectra with a large number of overlapping transitions: Examples from soft X-ray spectroscopy

Theoretical simulations are frequently used to assign electronic and geometric structure from spectral fingerprints. However, such assignments are prone to expectation bias. Bias can be reduced by using numerical measures of the similarity between calculated and experimental spectra. However, the commonly used point-wise comparisons cannot handle larger deviations in peak position. Here a weighted cross-correlation function is used to evaluate similarity scores for soft X-ray spectra of first-row transition metals. These spectra consist of hundreds of overlapping resonances, which makes spectral decomposition difficult. They are also challenging to model, leading to significant errors in both peak position and intensity. It is first shown how the choice of weight-function width can be related to the modeling errors. The method is then applied to evaluate the sensitivity of multiconfigurational wavefunction and charge-transfer multiplet simulations to model choices. The approach makes it possible to assess the reliability of assignments from spectral fingerprinting.

A novel optimization approach to minimize aggregate-fit-loss for improved breast sizing

Ready-to-wear clothing is typically based on the body-shape of human fit models that an apparel company hires. The body-shape difference between a consumer and the fit model of their size results in fit-loss of a certain degree. Aggregate-fit-loss is a concept attempting to quantify and estimate the accumulative fit-loss that a population may encounter. This paper reports on a novel method that minimizes the aggregate-fit-loss of a sizing system for bras, through shape categorization and optimized selection of prototypes (which can be regarded as the most appropriate fit models, or standard dress forms) for the categorized groups. A fit-loss function was introduced that calculates the dissimilarity between any two three-dimensional body scans, via pointwise comparisons of the point-to-origin distances of 9000 points on the scan surface. The within-group aggregate-fit-loss is minimized by an algorithm that returns the optimal prototype for the group. The overall aggregate-fit-loss is reduced by breast shape categorization based on the dissimilarities between the scans. Finally, the constraint of band sizes was brought into the categorization to provide a more feasible solution for improved bra sizing. The findings of this study can also contribute to the optimization of sizing systems for other apparel products.

Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis

With increasing point of interest (POI) datasets available with fine-grained spatial and temporal attributes, space–time Ripley’s K function has been regarded as a powerful approach to analyze spatiotemporal point process. However, space–time Ripley’s K function is computationally intensive for point-wise distance comparisons, edge correction and simulations for significance testing. Parallel computing technologies like OpenMP, MPI and CUDA have been leveraged to accelerate the K function, and related experiments have demonstrated the substantial acceleration. Nevertheless, previous works have not extended optimization of Ripley’s K function from space dimension to space–time dimension. Without sophisticated spatiotemporal query and partitioning mechanisms, extra computational overhead can be problematic. Meanwhile, these researches were limited by the restricted scalability and relative expensive programming cost of parallel frameworks and impeded their applications for large POI dataset and Ripley’s K function variations. This paper presents a distributed computing method to accelerate space–time Ripley’s K function upon state-of-the-art distributed computing framework Apache Spark, and four strategies are adopted to simplify calculation procedures and accelerate distributed computing respectively: (1) spatiotemporal index based on R-tree is utilized to retrieve potential spatiotemporally neighboring points with less distance comparison; (2) spatiotemporal edge correction weights are reused by 2-tier cache to reduce repetitive computation in L value estimation and simulations; (3) spatiotemporal partitioning using KDB-tree is adopted to decrease ghost buffer redundancy in partitions and support near-balanced distributed processing; (4) customized serialization with compact representations of spatiotemporal objects and indexes is developed to lower the cost of data transmission. Based on the optimized method, a web-based visual analytics framework prototype has been developed. Experiments prove the feasibility and time efficiency of the proposed method, and also demonstrate its value on promoting applications of space–time Ripley’s K function in ecology, geography, sociology, economics, urban transportation and other fields.

Multiscale simulation of erosive wear in a prototype-scale Pelton runner

The technical capacity to predict the erosion process is instrumental for the optimization of the runner designs and operation strategies of hydroelectric plants. A multiscale model of erosion recently formulated by the authors and validated on a laboratory-scale jet impingement case is used to study the erosion of a prototype-scale Pelton runner. The model is shown to provide physically-sound descriptions of the sediment impact condition distributions on the bucket surface; furthermore, the erosion distribution obtained is explained in terms of these underlying impact condition distributions. The model predictions for the erosion depth distribution on the bucket surface are validated with the corresponding experimental data, resulting in an average error of 35% for eight point-wise comparisons, 14% for line-averaged values along four transversal sections, and 4% for the surface-averaged erosion depth, directly linked to the total eroded mass. A careful error propagation analysis of the comparison between simulation and field data yields an uncertainty of ±22%. Based on these considerations, the modeling error is estimated to be 26±22%. The results obtained, namely quantitative predictions of the erosion of industrial-scale hydraulic machines, have no precedent in the literature; they demonstrate the accuracy and transferability of the multiscale model of erosion and suggest an improvement over the state-of-the-art computational fluid dynamics models based on empirical erosion correlations.

Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point.

Medical research frequently involves comparing an event time of interest between treatment groups. Rather than comparing the entire survival or cumulative incidence curves, it is sometimes preferable to evaluate these probabilities at a fixed point in time. Performing a covariate adjusted analysis can improve efficiency, even in randomized clinical trials, but no currently available group sequential test for fixed point analysis provides this adjustment. This paper introduces covariate adjusted group sequential pointwise comparisons of survival and cumulative incidence probabilities. Their test statistics have an asymptotic distribution with independent increments, permitting use of common stopping boundary specification methods. These tests are demonstrated through a redesign of BMT CTN 0402, a clinical trial that evaluated a prophylactic treatment for adverse outcomes following blood and marrow transplantation. A simulation study demonstrates that these tests maintain the type I error rate and power at nominal levels under a variety of settings involving influential covariates.