Abstract Seismic experiments on welded steel moment connections (WSMCs) demonstrated that the welding sequence can influence the failure responses by influencing the localized low-cycle fatigue responses. It is widely known that welding residual stresses relax with a few inelastic loading cycles, and hence, there is a notion that the welding sequence or resulting residual stress does not influence the seismic response of WSMCs. Because of this notion, investigations on the influence of weld sequence on WSMC failure responses have been limited. This study developed a numerical scheme based on a sequentially uncoupled thermomechanical analysis technique to simulate the weld-induced residual stress and its influence on the WSMC seismic responses. The simulation technique is validated against the recorded temperatures and strains. The simulations indicate that high residual stresses tend to concentrate near the weld toe and around the weld access holes. Subsequent seismic loading prescribed to the WSMCs in the presence of weld residual stress simulates the influence of residual stress on the localized strain responses. Strain accumulations are simulated at locations where cracks were observed in the seismic experiments of WSMCs. The numerical results also demonstrate the influence of weld sequences on the local strain responses of WSMCs. Finally, the residual stresses at critical locations are found to relax with a few inelastic loading cycles, as widely known; however, strain accumulation persisted with inelastic loading cycles at the locations where cracks were observed in the experiments. The reason for this phenomenon is demonstrated to be related to the influence of the multiaxial stress state at these locations.
Plasma assisted catalysis of post consumer plastics remains an intensive area of research with the potential to provide a method for a closed loop lifecycle for plastics waste. This work focuses on applying a nonthermal plasma (NTP) pretreatment of polypropylene (PP) prior to catalytic deconstruction to modify the product distribution. With the addition of NTP pretreatment, a conversion percentage of up to 94.5%, an increase of 2.9% from catalytic deconstruction without a NTP pretreatment, was observed. Propene was observed as the largest product in all pretreatments tests with the maximum propene yield with no pretreatment. With the addition of different pretreatment conditions, the yield of different major products including propane, butane, methane, and carbon monoxide could be increased with little decrease in conversion. The effects of the plasma pretreatment on the PP were investigated with FTIR and XPS studies. The plasma conditions were investigated with optical absorption spectroscopy and optical emission spectroscopy for ozone and gas temperature measurements, respectively. This research provides a potential method for the tuning of catalytic deconstruction products to better meet market fluctuations in product prices.
Purpose This study examines how machine learning (ML) adoption reshapes consumer purchasing behavior and value creation across major e-commerce platforms within the broader context of the information society. Specifically, it investigates the impact of ML technologies on conversion rates and Average Order Value (AOV) in digitally mediated marketplaces. Methods Using secondary data from Amazon, Alibaba, and Etsy covering the period 2020–2023, the study applies descriptive statistics, t-tests, analysis of variance (ANOVA), regression analysis, and difference-in-differences techniques to compare platform performance before and after ML adoption. These methods allow for cross-platform comparison while controlling for marketing expenditure and seasonality. Results The findings reveal statistically significant increases in both conversion rates and AOV following ML adoption across all three platforms. However, the magnitude of these effects differs significantly by platform, reflecting variations in market structure, consumer access mechanisms, and platform-specific personalization strategies. Conclusion The results demonstrate that machine learning functions as a critical infrastructural force shaping consumer access, engagement, and economic outcomes in contemporary digital marketplaces. These findings contribute to understanding how algorithmic systems influence value formation in the information society and raise important implications for platform governance, ethical personalization, and digital inclusion.
This paper measures how a seasonal vehicle entry fee affects disc golf course choice in Westchester County, New York. We use ten years of user-level data from the UDisc app, covering 30,531 visits by 758 players from 2015 to 2024, in a setting with two nearby and broadly comparable courses. One course is located within a state park that charges a predictable $10 per-vehicle fee on a seasonal calendar, while the other course is free year-round. We estimate a within-user probit model of course choice, controlling for weather differences and a linear time index, with fixed effects for user, day of week, holiday, and year. When the fee is in effect, the probability of choosing the fee course falls by about 1.5 percentage points, a result that is robust to logit and linear probability models.
OBJECTIVES:Severe tuberculosis (TB) is a major cause of critical illness and death in people living with HIV (PLWH) worldwide. Despite this, the immunopathology of severe HIV-associated TB (HIV/TB) is poorly understood. We aimed to identify an immunopathologic signature of severe HIV/TB in sub-Saharan Africa. DESIGN AND SETTING:We analyzed proteomic data from two prospective observational cohorts of adults hospitalized with severe undifferentiated infection in Uganda: an urban discovery cohort (Entebbe, n = 241) and a rural validation cohort (Tororo, n = 253). PATIENTS:Adults (age ≥ 18 yr) hospitalized with severe febrile illness. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Across both cohorts, severe HIV/TB was common, affecting 18% of participants in the discovery cohort and 21% in the validation cohort. Overall mortality was significant (30-d mortality of 22% in the discovery cohort and 60-d mortality of 26% in the validation cohort). Participants were stratified into three HIV/TB phenotypes: HIV-negative without TB, PLWH without TB, and PLWH with microbiologically diagnosed TB. We applied ordinal random forest models in the discovery cohort as a supervised feature-selection approach to identify proteins associated with progressive HIV/TB phenotype. In both cohorts, PLWH with microbiologically diagnosed TB were at highest risk of critical illness and death (30-d mortality of 42% in the discovery cohort and 60-d mortality of 52% in the validation cohort). An eight-protein signature reliably distinguished this phenotype, reflecting mediators of macrophage/dendritic cell activation (lysosome-associated membrane glycoprotein 3), natural killer cell and T-cell stimulation and cytotoxicity (cluster of differentiation 70, class I-restricted T-cell-associated molecule), B-cell activation (immunoglobulin lambda constant 2), protease-mediated tissue injury (protease, serine 2 [trypsin-2]), dysregulated coagulation (serpin peptidase inhibitor, clade A [alpha-1 antitrypsin], member 5), extracellular matrix remodeling (epidermal growth factor-containing fibulin-like extracellular matrix protein 1), and growth hormone/insulin-like growth factor axis dysregulation (insulin-like growth factor binding protein 3). CONCLUSIONS:We identified an immunologic signature of severe HIV/TB defined by mediators of macrophage/dendritic cell and cytotoxic lymphocyte activation, extracellular matrix remodeling, and dysregulated coagulation. These findings offer new insight into HIV/TB pathobiology and highlight potential targets for host-directed therapies in this high-risk population.
