Multi-week Period Research Articles

A Comparative Analysis on Indoor and Outdoor PM2.5 and Their Hourly Associations with Acute Respiratory Inflammation Among College Students in Lhasa.

Ambient concentrations are commonly used as proxies for personal PM2.5 exposure in epidemiological studies, despite indoor settings being the places where people spend most of their time. In a panel study of 110 nonsmoking, healthy college students in Lhasa, Tibet, indoor PM2.5 was monitored using calibrated low-cost sensors for two multiweek periods, in over 40 dormitories where participants resided. We also repeatedly measured fractional exhaled nitric oxide (FeNO), an acute respiratory inflammation biomarker, for each participant. Time-averaged indoor PM2.5 concentrations in individual dormitories ranged from 3.2 to 30 μg/m3 in the summer and from 3.6 to 57 μg/m3 in the fall, in most cases exceeding the outdoor level (4.3 and 4.9 μg/m3, respectively). The hourly mean indoor PM2.5 concentrations displayed a clear trimodal diel pattern, with peaks coincident with periods of increased activities. Further questionnaire-based analysis suggests that incense burning and smoking contributed to elevated levels of indoor PM2.5. Overnight PM2.5 levels in the dormitories were significantly associated with increased FeNO the following morning, with the effects attenuated as the hourly lag increased. In contrast, inconclusive associations were observed for ambient PM2.5. The results demonstrate that disregarding indoor exposure can result in biased estimates of acute health effects of PM2.5 in low PM2.5 areas.

EXPRESS: Individual Sellers’ Social Media Participation and Sales Performance in Peer-to-Peer Marketplaces: Evidence from a Quasi-Natural Experiment

Recent advancements in communication and social technologies have resulted in the growth of peer-to-peer (P2P) marketplaces, such as Xianyu and Poshmark. Individual sellers in these markets, most of whom are unbranded and lack credibility, often face immense challenges in building trust to facilitate P2P transactions. We consider the potential of sellers’ use of social media to establish trust and drive sales. Specifically, we address whether social media use benefits the sales performance of individual sellers, and how social media use affects customer retention and acquisition. We draw on social capital theory to explore the theoretical mechanisms by which social media participation may affect sales, hypothesizing that such participation enables the seller to accumulate social capital and develop trust. We empirically test this by exploiting a quasi-natural experiment wherein a large P2P marketplace for second-hand goods (Xianyu) unexpectedly removed its social media feature (Fishponds). We employ a difference-in-differences design, leveraging a proprietary dataset capturing social media participation and sales activities for individual sellers, covering more than 180,000 transactions over a multi-week period around the event. We find that sellers who initially participated in social media channels experienced a significant decline in sales after the shutdown, which is attributable to poorer customer retention and acquisition. We explore heterogeneity in the effects and find that the sellers who benefit most from social media participation are those with the most to gain from trust-building. An additional online controlled experiment provides further evidence of the trust-building mechanism. Our study implies individual sellers can harness social media to generate additional sales from existing and new customers. These insights are important, as they speak to the value of social media channels as drivers of trust in P2P marketplaces.

Development, Demonstration, and Evaluation of Routine Monitoring of Aerosol Carbon, Oxygen, and Sulfur Content.

Traditional online measurements of the chemical composition of particulate matter have relied on expensive and complex research-grade instrumentation based on mass spectrometry and/or chromatography. However, routine monitoring requires lower-cost alternatives that can be operated autonomously, and such tools are lacking. Routine monitoring of particulate matter, especially organic aerosol, relies instead on offline techniques such as filter collection that require significant operator effort. To address this gap, we present here a new online instrument, the "ChemSpot", that provides information on organic aerosol mass loading, volatility, and degree of oxygenation, along with sulfur content. The instrument grows particles with water condensation, impacts them onto a passivated surface with low heat capacity, and uses stepped thermal desorption of analytes to a combination of flame ionization detector (FID) and flame photometric detector (FPD) and then to a CO2 detector downstream of the FID/FPD setup. By relying on detectors designed for gas chromatography, calibration is achieved almost entirely through the introduction of gases without the need for regular introduction of particle-phase calibrants. Particle collection efficiency of greater than 95% was achieved consistently, and the collection cell was shown to rapidly and precisely heat to ∼800 °C at a rate as fast as 10 °C per second. Measurements of total organic carbon, volatility distribution of organic aerosol, total sulfur, and oxygen-to-carbon ratio (O:C) collected during a continuous multi-week period are presented here to demonstrate the autonomous operation of "ChemSpot". Colocated measurements with a mass spectrometer, an aerosol chemical speciation monitor (ACSM), show good correlation and relatively low bias between the instruments (mean absolute percentage error of 21% and 27% for organic carbon and equivalent sulfate measurements, respectively).

Try Before You Buy: Are There Benefits to a Random Trial Period before Students Choose Their Collaborative Teams?

The cognitive and performance benefits of group work in undergraduate courses depend on understanding how to structure groups to promote communication and comfort while also promoting diversity and reducing conflict. The current study utilized social network analysis combined with self-reported survey data from 555 students in 155 groups to understand how students identified group members whom they wished to work with. Students' willingness to work with their peers was positively associated with behavioral traits pertaining to attention, participation, and preparedness in class. We tested whether preventing students from choosing their group members until completing a multiweek period of random assignment to different groups each week influenced group selection criteria, and we found little effect. Students continued to depend on demographic similarities such as gender and ethnicity when selecting groupmates and enforcing random interactions before the group formation did not influence group satisfaction and/or grades. Random interactions before group formation did influence the willingness of students to continue working with peers who were persistently poorly rated based on behavioral attributes and contribution to the group work. Thus, the effort of random assignment could be beneficial to identify struggling students and improve collaboration.

Rationale and design of the SenseWhy project: A passive sensing and ecological momentary assessment study on characteristics of overeating episodes.

Overeating interventions and research often focus on single determinants and use subjective or nonpersonalized measures. We aim to (1) identify automatically detectable features that predict overeating and (2) build clusters of eating episodes that identify theoretically meaningful and clinically known problematic overeating behaviors (e.g., stress eating), as well as new phenotypes based on social and psychological features. Up to 60 adults with obesity in the Chicagoland area will be recruited for a 14-day free-living observational study. Participants will complete ecological momentary assessments and wear 3 sensors designed to capture features of overeating episodes (e.g., chews) that can be visually confirmed. Participants will also complete daily dietitian-administered 24-hour recalls of all food and beverages consumed. Overeating is defined as caloric consumption exceeding 1 standard deviation of an individual's mean consumption per eating episode. To identify features that predict overeating, we will apply 2 complementary machine learning methods: correlation-based feature selection and wrapper-based feature selection. We will then generate clusters of overeating types and assess how they align with clinically meaningful overeating phenotypes. This study will be the first to assess characteristics of eating episodes in situ over a multiweek period with visual confirmation of eating behaviors. An additional strength of this study is the assessment of predictors of problematic eating during periods when individuals are not on a structured diet and/or engaged in a weight loss intervention. Our assessment of overeating episodes in real-world settings is likely to yield new insights regarding determinants of overeating that may translate into novel interventions.

Temperature dependence of volatile organic compound emissions from surface coatings: Inter-species difference observed in real indoor environments and mechanistic understanding

Coatings often cover two-thirds of the surfaces in indoor environments and represent important sources of indoor volatile organic compounds (VOCs). Temperature is known to affect VOC emission rates from coatings, yet inter-species difference in the temperature dependence still needs to be understood. Based on time-resolved VOC measurements in an indoor air campaign conducted in residences in Beijing, China, we identified dibasic ester (DBE), a solvent mixture often used in coatings, and found that the concentration ratios of DBE components exhibited strong temperature dependence in an apartment when the indoor temperature declined stepwise over a multiweek period. To interpret the observational results, we developed a simplified mechanistic model relating the temperature dependence of VOC emission rates from coated surfaces to the temperature dependence of the diffusion coefficient of the emitted VOCs in the coating layer and further to a predicable molecular property of the emitted VOCs, molar volumes at 0 K, based on the free-volume theory. This correlation was quantitatively verified using the DBE data as well as using the data of alkanes, another set of VOCs that might be emitted from coatings, observed in two apartments in the same campaign. Given that indoor temperature varies considerably over seasons and across regions, the correlation proposed herein may help better predict indoor VOC emissions from coatings.

Susceptible windows of exposure to fine particulate matter and fetal growth trajectories in the Spanish INMA (INfancia y Medio Ambiente) birth cohort

While prior studies report associations between fine particulate matter (PM2.5) exposure and fetal growth, few have explored temporally refined susceptible windows of exposure.We included 2328 women from the Spanish INMA Project from 2003 to 2008. Longitudinal growth curves were constructed for each fetus using ultrasounds from 12, 20, and 34 gestational weeks. Z-scores representing growth trajectories of biparietal diameter, femur length, abdominal circumference (AC), and estimated fetal weight (EFW) during early (0–12 weeks), mid- (12–20 weeks), and late (20–34 weeks) pregnancy were calculated. A spatio-temporal random forest model with back-extrapolation provided weekly PM2.5 exposure estimates for each woman during her pregnancy. Distributed lag non-linear models were implemented within the Bayesian hierarchical framework to identify susceptible windows of exposure for each outcome and cumulative effects [βcum, 95% credible interval (CrI)] were aggregated across adjacent weeks. For comparison, general linear models evaluated associations between PM2.5 averaged across multi-week periods (i.e., weeks 1–11, 12–19, and 20–33) and fetal growth, mutually adjusted for exposure during each period. Results are presented as %change in z-scores per 5 μg/m3 in PM2.5, adjusted for covariates.Weeks 1–6 [βcum = −0.77%, 95%CrI (−1.07%, −0.47%)] were identified as a susceptible window of exposure for reduced late pregnancy EFW while weeks 29–33 were positively associated with this outcome [βcum = 0.42%, 95%CrI (0.20%, 0.64%)]. A similar pattern was observed for AC in late pregnancy. In linear regression models, PM2.5 exposure averaged across weeks 1–11 was associated with reduced late pregnancy EFW and AC; but, positive associations between PM2.5 and EFW or AC trajectories in late pregnancy were not observed.PM2.5 exposures during specific weeks may affect fetal growth differentially across pregnancy and such associations may be missed by averaging exposure across multi-week periods, highlighting the importance of temporally refined exposure estimates when studying the associations of air pollution with fetal growth.

Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations

Abstract. Aggregation is a key microphysical process for the formation of precipitable ice particles. Its theoretical description involves many parameters and dependencies among different variables that are either insufficiently understood or difficult to accurately represent in bulk microphysics schemes. Previous studies have demonstrated the valuable information content of multi-frequency Doppler radar observations to characterize aggregation with respect to environmental parameters such as temperature. Comparisons with model simulations can reveal discrepancies, but the main challenge is to identify the most critical parameters in the aggregation parameterization, which can then be improved by using the observations as constraints. In this study, we systematically investigate the sensitivity of physical variables, such as number and mass density, as well as the forward-simulated multi-frequency and Doppler radar observables, to different parameters in a two-moment microphysics scheme. Our approach includes modifying key aggregation parameters such as the sticking efficiency or the shape of the size distribution. We also revise and test the impact of changing functional relationships (e.g., the terminal velocity–size relation) and underlying assumptions (e.g., the definition of the aggregation kernel). We test the sensitivity of the various components first in a single-column “snowshaft” model, which allows fast and efficient identification of the parameter combination optimally matching the observations. We find that particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while the sticking efficiency and the cloud ice habit have less influence. The setting which optimally matches the observations is then implemented in a 3D model using the identical scheme setup. Rerunning the 3D model with the new scheme setup for a multi-week period revealed that the large overestimation of aggregate size and terminal velocity in the model could be substantially reduced. The method presented is expected to be applicable to constrain other ice microphysical processes or to evaluate and improve other schemes.

Application of the community dialogues method to identify ethical values and priorities related to pharmacogenomics.

Given the expansion of genetics in medicine, there is a growing need to develop approaches to engage patients in understanding how genetics affects their health. Various qualitative methods have been applied to gain a deeper understanding of patient perspectives in topics related to genetics. Community dialogues (CD)are a bi-directional research method that invites community members to discuss a pertinent, challenging topic over the course of a multi-week period and the community members openly discuss their positions on the topic. Authors discuss the first application of the CDmethod to the topic of pharmacogenetics testing. Additional CDare needed to engage diverse participant populations on this topic to improve genetics literacy, enhance physician engagement and drive policy change.

Buyer-Seller Social Interaction and Sales Activity in Online P2P Markets for Used Goods

Recent advancements in communication and social technologies have resulted in the growth of social marketplaces. Many of these marketplaces integrate social media features with peer-to-peer transaction activities, so that buyers and sellers may achieve trust through unstructured socialization. Despite ample evidence showing the positive effects of social media marketing and engagement on buyers' purchase intention and product usage, complementary evidence is lacking about the benefits to individual sellers from engaging via these channels. In this study, we examine the business impact of social media participation on individual seller's sales activities, exploiting a natural experiment wherein a large social marketplace for second-hand goods (Xianyu) unexpectedly removed its social media features (Fishponds). We leverage a novel dataset capturing both social media participation and sales activities for individual sellers, comprising of more than 180,000 transactions during a multi-week period around the event. To estimate the causal effect of the event, we employ a difference-in-differences design. We also assess the robustness of our results to an alternative estimation approach, namely generalized synthetic control. Our results consistently demonstrate that sellers who initially participated in social media channel experienced a significant decline in their sales after the channel shutdown. Exploring heterogeneity in the effects around a variety of seller and product listing features, we find evidence consistent with the notion that the sellers who benefit most from social media participation are those who face the greatest difficulty establishing trust with buyers. Our results provide robust evidence that social media participation thus serves a crucial role in fostering trust in social marketplaces. Our work offers nontrivial managerial implications, both for marketplace operators and individual sellers.

No Effect of Microgravity and Simulated Mars Gravity on Final Bacterial Cell Concentrations on the International Space Station: Applications to Space Bioproduction.

Microorganisms perform countless tasks on Earth and they are expected to be essential for human space exploration. Despite the interest in the responses of bacteria to space conditions, the findings on the effects of microgravity have been contradictory, while the effects of Martian gravity are nearly unknown. We performed the ESA BioRock experiment on the International Space Station to study microbe-mineral interactions in microgravity, simulated Mars gravity and simulated Earth gravity, as well as in ground gravity controls, with three bacterial species: Sphingomonas desiccabilis, Bacillus subtilis, and Cupriavidus metallidurans. To our knowledge, this was the first experiment to study simulated Martian gravity on bacteria using a space platform. Here, we tested the hypothesis that different gravity regimens can influence the final cell concentrations achieved after a multi-week period in space. Despite the different sedimentation rates predicted, we found no significant differences in final cell counts and optical densities between the three gravity regimens on the ISS. This suggests that possible gravity-related effects on bacterial growth were overcome by the end of the experiment. The results indicate that microbial-supported bioproduction and life support systems can be effectively performed in space (e.g., Mars), as on Earth.

Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign

Abstract. Large-eddy simulations (LESs) of a multi-week period during the HD(CP)2 (High-Definition Clouds and Precipitation for advancing Climate Prediction) Observational Prototype Experiment (HOPE) conducted in Germany are evaluated with respect to mean boundary layer quantities and turbulence statistics. Two LES models are used in a semi-idealized setup through forcing with mesoscale model output to account for the synoptic-scale conditions. Evaluation is performed based on the HOPE observations. The mean boundary layer characteristics like the boundary layer depth are in a principal agreement with observations. Simulating shallow-cumulus layers in agreement with the measurements poses a challenge for both LES models. Variance profiles agree satisfactorily with lidar measurements. The results depend on how the forcing data stemming from mesoscale model output are constructed. The mean boundary layer characteristics become less sensitive if the averaging domain for the forcing is large enough to filter out mesoscale fluctuations.

Coefficients of variation of ground reaction force measurement in cats.

Gait analysis has been extensively performed in dogs and horses; however, very little is known about feline biomechanics. It was, therefore, the aim of this study to determine the coefficient of variation (CV) among three ground reaction force (GRF) measurements taken for 15 client-owned European shorthaired cats without a training period and a short acclimatisation time. Gait was measured as each cat walked across a pressure-sensitive walkway, and measurements were made three times over a multi-week period (range: 2 to 17 weeks). The parameters evaluated were peak vertical force (PFz), vertical impulse (IFz), stance phase duration (SPD), step length (SL), paw contact area (PCA) and symmetry index (SI%) of the front and hind limbs. After averaging each of the values from the three measurements, the CV and 95% confidence interval (CI) were calculated for all parameters. PFz showed the lowest CV (~ 3%), while IFz showed the highest CV (~11%) when normalised to body mass. When the GRFs were normalised to total force, the CV of PFz dropped to ~2% and that of IFz dropped to ~3%. The CV of SL and PCA were lower (~6% respectively ~5%) compared to the CV for SPD (~10%). The SI% for both PFz and IFz were comparable to the values reported in the gait analysis literature for dogs. Results of the current study indicate that gait analysis of cats using pressure-sensitive walkways produces reliable data and is a promising approach for evaluation of lameness. The results also suggest that PFz may be a more reliable parameter than IFz and that normalisation to percent of total force may aid in interpretation of the evaluated data.

Coupled subdaily and multiweek cycles during the lava dome eruption of Soufrière Hills Volcano, Montserrat

Observations of volcanoes extruding andesitic lava to produce lava domes often reveal cyclic behavior. At Soufrière Hills Volcano, Montserrat, cycles with subdaily and multiweek periods have been recognized on many occasions. Observations clearly show that the period of subdaily cycles is modulated by the multiweek cycle. The subdaily and multiweek cycles have been modeled separately as stick‐slip magma flow at the junction between a dyke and an overlying cylindrical conduit and as the filling and discharge of magma through the elastic‐walled dyke, respectively. Here, we couple these two models to describe the behavior over a period of well‐observed multiweek cycles, with accompanying subdaily cycles, from 13 May to 21 September 1997. The coupled model captures well the asymmetrical first‐order behavior: the first 40% of the multiweek cycle consists of high rates of lava extrusion during short period/high amplitude subdaily cycles as the dyke reservoir discharges itself. The remainder of the cycle involves increasing pressurization as more magma is stored, and extrusion rate falls, followed by a gradual increase in the period of the subdaily cycles.

An analysis of weekly out-of-home discretionary activity participation and time-use behavior

Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week discretionary activity participation.

Use of Passive Acoustics for Assessing Behavioral Interactions in Individual Toadfish

AbstractMost passive acoustics studies focus on daily and seasonal timing and location of choruses of calling fish, particularly sciaenids. Because male toadfishOpsanusspp. are stationary for extended periods, it is possible to extract detailed information about their calls and interactions, making them a powerful model for passive acoustics studies on commercially important species. Toadfishes of both sexes produce a short, pulsatile agonistic grunt, and males produce a “boatwhistle” advertisement call for male‐male competition and to attract females. We identify unseen vocal individuals (oyster toadfishO. tauand Gulf toadfishO. beta) near a stationary hydrophone and describe call variability and changes over short‐ and long‐term periods, source levels, call propagation, and directionality. Calls exhibit a directional pattern related to the heart‐shaped swim bladder morphology, generating a maximal level behind the fish; grunt frequency spectra allow differentiation of individual callers over multiweek periods. Boatwhistle parameters of oyster toadfish calls change geographically, seasonally, and with temperature, and males call day and night. The Gulf toadfish call rate increases during twilight, when individuals produce shorter and simpler calls. Finally, nearby calling males compete acoustically by increasing their calling rates or producing a grunt (an acoustic tag) during another male's boatwhistle. Toadfishes have been successful models for addressing numerous questions in unseen fish by means of passive acoustics.

Weather systems occurring over Fort Simpson, Northwest Territories, Canada, during three seasons of 1998–1999: 1. Cloud features

An investigation of high‐latitude continental cloud systems was carried out in the interior of the Northwest Territories of Canada during three multiweek periods during the fall, winter, and spring of 1998–1999 as part of the Canadian Global Energy and Water Cycle Experiment (GEWEX) Enhanced Study. Radar data supplemented by satellite, upper air, and surface observations were used to determine the seasonal behavior of cloud macroscopic properties and compare these with similar observations elsewhere. Unique features included the prevalence of multilayered systems, the cold temperatures of low clouds, and a significant diurnal trend in cloud properties in the winter. A synoptic classification was developed and shown to be an important factor in explaining the variability of cloud properties. A consistent picture emerges of the upslope component and wind shear aloft contributing to the cloud structure in five synoptic classes. Vertically resolved cloud properties highlighted the importance of the ice process in these cloud systems. The cloud system reflectivity and temperature dependencies further supported the synoptic characterizations and highlighted the significance of using seasonally based relationships in automated cloud identification algorithms. The implication of the cloud system variability for radiation measurements was also shown. The radar reflectivity data, degraded to match CloudSat resolution and sensitivity, showed that cloud detection was reliable but that there was a positive bias with cloud thickness. Negative biases in cloud top retrievals based on advanced very high resolution radiometer data were also identified. The Global Environmental Multiscale model illustrated some degree of bias in the occurrence and vertical distribution of these cloud systems. Winter situations in general and midclouds situations in particular were the most poorly handled in both the satellite applications and the model simulations.

An analysis of the impact of information and communication technologies on non-maintenance shopping activities

This paper examines the use and travel impacts of two forms of information and communication technologies (ICTs): mobile telephones and computers. The travel impacts are examined in the context of participation in out-of-home non-maintenance shopping activities over a multiweek period through the modeling of the duration between successive shopping activity participations. The empirical analysis uses a continuous six-week travel survey collected in the cities of Halle and Karlsruhe in Germany in the Fall of 1999. The results indicate that the effects of ICTs on activity–travel patterns are mediated by individual sociodemographic and locational factors, as well as by unobserved individual characteristics. The results also show that the substitution between mobile phone use and shopping travel is grossly underestimated if the effects of common unobserved factors affecting mobile phone use and shopping travel are not considered. In addition, there is quite substantial intra-individual variation in intershopping duration.

Intershopping duration: an analysis using multiweek data

This study examines the rhythms in the shopping activity participation of individuals over a multiweek period by modeling the duration between successive shopping participations. A hazard-based duration model is used to model intershopping duration, and a latent segmentation method is applied to distinguish between erratic shoppers and regular shoppers. The paper applies the methodology to examine the regularity and frequency of shopping behavior of individuals using a continuous six-week travel survey collected in the cities of Halle and Karlsruhe in Germany in the fall of 1999. The empirical results underscore the need to adopt a flexible hazard model form for analyzing intershopping durations. The results also provide important insights into the determinants of the regularity and frequency of individuals’ shopping activity participation behavior.

Problem perception, optimism, and related states as a function of time of day (diurnal rhythm) and moderate exercise: Two arousal systems in interaction

Two experiments are described in which perceptions of personal problems, optimism, and associated feelings were studied as a function of time of day and moderate exercise (walking). Over multiweek periods, participants completed self-ratings at fixed times of day representing high and low periods of energetic arousal. While ratings on individual days varied, means across all rating days indicated that problems were perceived as more serious at mid-to late afternoon, a period of low energetic arousal, than they were at late morning and after the walk, periods of high energetic arousal. Separate analyses of rated high energy and low tension states, compared to their opposites, indicated that problems were perceived as least serious in the former states. Ratings of optimism and associated feelings were consistent with the problem ratings. Also discussed are theoretical implications regarding moods as predispositional variables, state-dependent memory, and research involving naturalistic settings and aggregated measurements.