Trace Data Research Articles

Determination of a heat transfer correlation for small internal combustion engines

Small internal combustion engines (ICEs) with displacements < 100 cm3 have greater surface area-to-volume ratios that promote heat transfer through the combustion chamber walls, subsequently lowering the fuel conversion efficiency. As a result, employing common heat transfer correlations developed for larger engines to estimate wall heat transfer yields inaccurate results. Therefore, it is necessary to review literature correlations and determine their applicability for small ICEs. To this end, this paper first presents a literature review highlighting the characteristic length scales, characteristic velocities, and the properties of the working fluid for ICE convective heat transfer correlations. This information is used within a zero-dimensional ICE model to determine an appropriate heat transfer expression for a relatively small two-stroke ICE (53.2 cm3). Through parameter optimization, by matching the gross indicated power over 85 experimental data points using MATLAB’s fmincon routine, the developed expression (Nu=0.449Re3/4Pr1/3) shows good confidence with respect to the power (R2 = 0.924, RMS = 0.257) and an in-cylinder pressure data trace (R2 = 0.991, RMS = 1.52). A following parametric study varying the bore-to-stroke (BtS) ratio for small engines (0.15 cm3 to 100 cm3) finds a localized choice of the BtS and ignition timing at each displacement volume based on the minimum heat transfer due to the combination of a changing surface area and heat transfer coefficient. In the future, including the effects of volumetric efficiency and tuned exhaust pipes can enhance the scope of the proposed model.

Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data.

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.

Two-Sided Cultural Fit: The Differing Behavioral Consequences of Cultural Congruence Based on Values Versus Perceptions

How do people establish and maintain cultural fit with an organization? Prior research has offered two perspectives that have heretofore been conceptually disconnected. One focuses on personal values, whereas another emphasizes perceptions of the cultural code. We develop a theoretical account that integrates these approaches by linking them to distinct mechanisms and behavioral consequences of cultural fit. We propose that value congruence—the match between one’s values and those that prevail in an organization—relates to the mechanism of group attachment and shapes behavior when one periodically steps back from day-to-day interactions, assesses one’s identification with an organization, and determines whether to stay or voluntarily depart. In contrast, we argue that perceptual congruence—the degree to which one implicitly understands an organization’s prevailing values and norms—relates to the mechanism of interpersonal coordination and influences behavior when one engages in routine peer interactions. Accordingly, we theorize that these two forms of cultural fit relate to distinct behaviors, voluntary exit and linguistic conformity with peers, respectively. Drawing on email and survey data from a midsized technology firm, we find support for our theory and discuss the implications of our findings for research on person-culture fit, dual-process models of culture and cognition, and the pairing of surveys with digital trace data. Funding: This work was supported by the Berkeley Culture Center, the Cortese Distinguished Chair in Management, the Grether Chair of Business Administration and Public Policy.

MHealth and social mediation: Mobile support among stigmatized people living with HIV and substance use disorder

The social mediation role of mobile technology is typified by mHealth apps designed to connect individuals to others and support substance use disorder (SUD) recovery. In this study, we examined the use and utility of one such app designed to support people living with HIV (PLWH) and SUD. Drawing on Ling’s emphasis on reciprocity and micro-coordination in mobile telephony as a social mediation technology, we gathered digital trace data from app logs to construct two metrics, initiation (i.e. whether a particular feature is engaged on a given day) and intensity (i.e. degree of involvement in the activity when engaged on that day), at three levels of communication—networked (one-to-many), dyadic (one-to-one), and intraindividual (self-to-self). We consider these system features alongside use of information resources, games and relaxation links, a meeting and events calendar, and support tools to address use urges. We found few differences in patterns of use by race, sex, and age, though African Americans were less likely to engage in intraindividual expression, whereas women and older users were more likely to make use of this feature. The initiation and intensity of network and dyadic reception, as well as the intensity of network expression, predicts recovery outcomes as measured on a weekly “check-in” survey, suggesting the utility of mobile log data for digital phenotyping in mHealth. By implementing this app during the COVID-19 pandemic, the study also found the disruption caused by national lockdown was negatively related to the app use.

How does solid‐state drives cluster perform for distributed file systems: An empirical study

SummaryAs the capacity of Solid‐State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and big data processing. However, despite its rapid developments, the performance of the SSD cluster remains largely under‐investigated, leaving its sub‐optimal applications in reality. To address this issue, in this paper we conduct extensive empirical studies for a comprehensive understanding of the SSD cluster in diverse settings. To this end, we configure a real SSD cluster and gather the generated trace data based on some often‐used benchmarks, then adopt analytical methods to analyse the performance of the SSD cluster with different configurations. In particular, regression models are built to provide better performance predictability under broader configurations, and the correlations between influential factors and performance metrics with respect to different numbers of nodes are investigated, which reveal the high scalability of the SSD cluster. Additionally, the cluster's network bandwidth is inspected to explain the performance bottleneck. Finally, the knowledge gained is summarised to benefit the SSD cluster deployment in practice.

Witnessing the fragmentation of a filament into prestellar cores in Orion B/NGC 2024

Context. Recent Herschel observations of nearby molecular clouds have shown that filamentary structures are ubiquitous and that most prestellar cores form in dense filaments. Probing the detailed density and velocity structure of molecular filaments is therefore crucial for improving our observational understanding of the star formation process. Aims. We aim to characterize both the density and the velocity field of a typical molecular filament in the process of fragmenting into cores. Methods. We mapped a portion of the NGC 2024 region in the Orion B molecular cloud with the Nobeyama 45m telescope, in the 12CO (J = 1–0), 13CO (J = 1–0), C18O (J = 1–0), and H13CO+ (J = 1–0) lines, and the southwestern part of NGC 2024, corresponding to the NGC 2024S filament, with the NOrthern Extended Millimeter Array (NOEMA) interferometer in H13CO+ (J = 1–0). Results. The maps of 13CO, C18O, and H13CO+ emission trace at least part of the filamentary structure seen in the 8″ resolution ArTéMiS+Herschel data. The median radial column density profile of the NGC 2024S filament as derived from ArTéMiS+Herschel dust emission data is well fitted by a Plummer profile with a half-power diameter DHPPlummer=0.081±0.014 pc, which is similar to the findings of previous studies of nearby molecular filaments with Herschel. On the other hand, the half-power diameters of NGC 2024S as measured from the Nobeyama 13CO and C18O data are broader than, and the half-power diameter derived from the H13CO+ data narrower than the filament diameter measured with Herschel. These results suggest that the 13CO and C18O data trace only the (low-density) outer part of the Herschel filament and the H13CO+ data only the (dense) inner part. We identify four cores in the portion of the Herschel map covered by NOEMA and find that each Herschel core corresponds to a single core detected in the combined NOEMA+45m H13CO+ data cube. The Nobeyama H13CO+ centroid velocity map reveals velocity gradients along both the major and the minor axis of the NGC 2024S filament, as well as velocity oscillations with a period λ ~0.2 pc along the major axis. Comparison between the centroid velocity and the column density distribution shows a tentative λ/4 phase shift in H13CO+ or C18O. This λ/4 shift is not simultaneously observed for all cores in any single tracer but is tentatively seen for each core in either H13CO+ or C18O. The difference between the H13CO+ and C18O velocity patterns may arise from differences in the range of densities probed by H13CO+ and C18O. We produced a toy model that takes into account the three velocity-field components: a transverse velocity gradient, a longitudinal velocity gradient, and a longitudinal oscillation mode caused by fragmentation. Examination of synthetic data shows that the longitudinal oscillation component produces an oscillation pattern in the velocity structure function of the model. Since the velocity structure function of the Nobeyama H13CO+ centroid velocity data does show an oscillation pattern, we suggest that our observations are partly tracing core-forming motions and the fragmentation of the NGC 2024S filament into cores. We also find that the mean core mass in NGC 2024S corresponds to the effective Bonnor-Ebert mass in the filament. This is consistent with a scenario in which higher-mass cores form in higher-line-mass filaments.

Adaptive and Scalable Caching With Erasure Codes in Distributed Cloud-Edge Storage Systems

Erasure codes have been widely used to enhance data resiliency with low storage overheads. However, in geo-distributed cloud storage systems, erasure codes may incur high service latency as they require end users to access remote storage nodes to retrieve data. An elegant solution to achieving low latency is to deploy caching services at the edge servers close to end users. In this paper, we propose adaptive and scalable caching schemes to achieve low latency in the cloud-edge storage system. Based on the measured data popularity and network latencies in real time, an adaptive content replacement scheme is proposed to update caching decisions upon the arrival of requests. Theoretical analysis shows that the reduced data access latency of the replacement scheme is at least 50% of the maximum reducible latency. With the low computation complexity of our design, nearly no extra overheads will be introduced when handling intensive data flows. For further performance improvements without sacrificing its efficiency, an adaptive content adjustment scheme is presented to replace the subset of cached contents that incur the aforementioned performance loss. Driven by real-world data traces, extensive experiments based on Amazon Simple Storage Service demonstrate the effectiveness and efficiency of our design.

Blockchain: Research and Application

Blockchain has been invented for years and it gradually become the most important technique in nowadays’ internet era. Blockchain technology has been widely used in various fields, such as finance, trading, logistics, currency, etc. This paper explains and demonstrates the main algorithm and application of the blockchain. Until now, the primary applications of blockchain are mainly focus on the digital currency such as ETH. The value of blockchain, however, is not limited by only the digital currency but all the aspect within the internet. For instance, based on these years’ social concern, the pandemic of COVID-19 is the most worried problem. The application of this paper concentrates on the connection between COVID-19 and blockchain. By relating the technique of blockchain, the security and data tracing of medical supplies have been insured. Moreover, the personal information security is maintained during the process of doing the covid-test and the trajectory of activity.

A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics.

Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.

Assessing carbon emissions reduction by incorporating automated monitoring system during transit: a case study

The green concept in operations is becoming an inevitable part of global maritime logistics activities and has an important influence on the improvement of efficiency and environmental performance. This paper aims to assess the continuous monitoring and tracking of container shipments at the ports in order to reduce carbon emissions thus improving environmental performance. In this research, near real-time RFID data tracing and tracking container cargo are shared by the automated monitoring system. The collected data gathered via digitalization is further analyzed to ensure a greener maritime logistics system. The significant findings of the study for the literature show that the actual fuel consumption is reduced when automated monitoring systems are used at the ports (Ports and CFS (Container freight station)). The reduced fuel consumption during the transit between the ports and CFS has resulted in a reduction in carbon emissions of environmental performance. The results show a 6 % reduction in emissions from port to CFS and 23% from CFS to ports. Thus, effective practices in Green Logistics are considered to be beneficial for carbon emissions. These findings contribute to the understanding and development of effective strategies for logistic operations using technologies to create a green performance. The study was performed in a certain set of environmental dimensions and the results may vary depending on the organization, which can be studied further in future research.

Know(ing) Infrastructure: The Wayback Machine as object and instrument of digital research

From documenting human rights abuses to studying online advertising, web archives are increasingly positioned as critical resources for a broad range of scholarly Internet research agendas. In this article, we reflect on the motivations and methodological challenges of investigating the world’s largest web archive, the Internet Archive’s Wayback Machine (IAWM). Using a mixed methods approach, we report on a pilot project centred around documenting the inner workings of ‘Save Page Now’ (SPN) – an Internet Archive tool that allows users to initiate the creation and storage of ‘snapshots’ of web resources. By improving our understanding of SPN and its role in shaping the IAWM, this work examines how the public tool is being used to ‘save the Web’ and highlights the challenges of operationalising a study of the dynamic sociotechnical processes supporting this knowledge infrastructure. Inspired by existing Science and Technology Studies (STS) approaches, the paper charts our development of methodological interventions to support an interdisciplinary investigation of SPN, including: ethnographic methods, ‘experimental blackbox tactics’, data tracing, modelling and documentary research. We discuss the opportunities and limitations of our methodology when interfacing with issues associated with temporality, scale and visibility, as well as critically engage with our own positionality in the research process (in terms of expertise and access). We conclude with reflections on the implications of digital STS approaches for ‘knowing infrastructure’, where the use of these infrastructures is unavoidably intertwined with our ability to study the situated and material arrangements of their creation.

Whole-brain mapping of effective connectivity by fMRI with cortex-wide patterned optogenetics

Functional magnetic resonance imaging (fMRI) with optogenetic neural manipulation is a powerful tool that enables brain-wide mapping of effective functional networks. To achieve flexible manipulation of neural excitation throughout the mouse cortex, we incorporated spatiotemporal programmable optogenetic stimuli generated by a digital micromirror device into an MRI scanner via an optical fiber bundle. This approach offered versatility in space and time in planning the photostimulation pattern, combined with in situ optical imaging and cell-type-specific or circuit-specific genetic targeting in individual mice. Brain-wide effective connectivity obtained by fMRI with optogenetic stimulation of atlas-based cortical regions is generally congruent with anatomically defined axonal tracing data but is affected by the types of anesthetics that act selectively on specific connections. fMRI combined with flexible optogenetics opens a new path to investigate dynamic changes in functional brain states in the same animal through high-throughput brain-wide effective connectivity mapping.

QUALITATIVE EVIDENCE ON CHILLING EFFECTS—HOW USERS’ IMAGINARIES OF DATAVEILLANCE LEAD TO INHIBITED DIGITAL BEHAVIOR

Our everyday life has become highly digitized, and all our online actions leave digital data traces that are automatically collected, aggregated, and analyzed. Emerging research shows that this dataveillance can lead to chilling effects on mundane and legitimate information and communication behaviors. However, the extent to which a sense of dataveillance deters individuals from freely engaging in legitimate digital behaviors remains empirically understudied. Therefore, we ask: How do internet users imagine dataveillance? And how does this perception lead to people limiting their digital behavior? To address these questions, we conduct qualitative semi-structured interviews with 15 individuals. Our results contribute to a better understanding of people’s imaginaries of dataveillance, as well as sources for these perceptions and reactions towards them in terms of chilled digital behaviors. We show how imagined sources of dataveillance can range from states to corporate actors and how knowledge about dataveillance can originate in news coverage on data scandals or own experiences. Also, we identify further legitimate, mundane behaviors in other life domains like participating in online dating and buying things online that are affected by a sense of dataveillance and have so far been neglected in research on chilling effects. These results contribute to an empirically founded understanding of the nature of a sense of dataveillance and resulting chilling effects on digital behavior. They provide the basis for a representative survey and a mobile experience sampling study as part of a mixed-methods research design investigating chilling effects on a population level.

Performance of antigen lateral flow devices in the UK during the alpha, delta, and omicron waves of the SARS-CoV-2 pandemic: a diagnostic and observational study

Antigen lateral flow devices (LFDs) have been widely used to control SARS-CoV-2. We aimed to improve understanding of LFD performance with changes in variant infections, vaccination, viral load, and LFD use, and in the detection of infectious individuals. In this diagnostic study, paired LFD and RT-PCR test results were prospectively collected from asymptomatic and symptomatic participants in the UK between Nov 4, 2020, and March 21, 2022, to support the National Health Service (NHS) England's Test and Trace programme. The LFDs evaluated were the Innova SARS-CoV-2 Antigen Rapid Qualitative Test, the Orient Gene Rapid Covid-19 (Antigen) Self-Test, and the Acon Flowflex SARS-CoV-2 Antigen Rapid Test (Self-Testing). Test results were collected across various community testing settings, including predeployment testing sites, routine testing centres, homes, schools, universities, workplaces, targeted community testing, and from health-care workers. We used multivariable logistic regression to analyse LFD sensitivity and specificity using RT-PCR as a reference standard, adjusting for viral load, LFD manufacturer, test setting, age, sex, test assistance, symptom status, vaccination status, and SARS-CoV-2 variant. National contact tracing data from NHS Test and Trace (Jan 1, 2021, to Jan 11, 2022) were used to estimate the proportion of transmitting index patients (with ≥1 RT-PCR-positive or LFD-positive contact) potentially detectable by LFDs (specifically Innova, as the most widely used LFD) with time, accounting for index viral load, variant, and symptom status. We assessed 75 382 pairs of LFD and RT-PCR tests. Of these, 4131 (5·5%) were RT-PCR-positive. LFD sensitivity versus RT-PCR was 63·2% (95% CI 61·7-64·6) and specificity was 99·71% (95% CI 99·66-99·74). Increased viral load was independently associated with being LFD positive (adjusted odds ratio [aOR] 2·85 [95% CI 2·66-3·06] per 1 log10 copies per mL increase; p<0·0001). There was no evidence that LFD sensitivity differed for delta (B.1.617.2) infections versus alpha (B.1.1.7) or pre-alpha (B.1.177) infections (aOR 1·00 [0·69-1·45]; p=0·99), whereas omicron (BA.1 or BA.2) infections appeared more likely to be LFD positive (aOR 1·63 [1·02-2·59]; p=0·042). Sensitivity was higher in symptomatic participants (68·7% [95% CI 66·9-70·4]) than in asymptomatic participants (52·8% [50·1-55·4]). Among 347 374 unique index patients with probable onward transmission, 78·3% (95% CI 75·3-81·2) were estimated to have been detectable with LFDs (Innova), and this proportion was mostly stable with time and for successive variants. Overall, the estimated proportion of infectious index patients detectable by the Innova LFD was lower in asymptomatic patients (57·6% [53·6-61·9]) versus symptomatic patients (79·7% [76·7-82·5]). LFDs remained able to detect most SARS-CoV-2 infections throughout vaccine roll-out and across different viral variants. LFDs can potentially detect most infections that transmit to others and reduce the risk of transmission. However, performance is lower in asymptomatic individuals than in symptomatic individuals. UK Health Security Agency, the UK Government Department of Health and Social Care, National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, and the University of Oxford NIHR Biomedical Research Centre.

Impact of public health and social measures on contact dynamics during a SARS-CoV-2 Omicron variant outbreak in Quanzhou, China, March to April 2022

ObjectivesTo evaluate the impact of early implementation of public health and social measures (PHSMs) on contact rates over time and explore contact behavior of asymptomatic versus symptomatic cases. MethodsWe used the largest contact tracing data in China thus far to estimate the mean contacts over time by age groups and contact settings. We used bootstrap with replacement to quantify the uncertainty of contact matrixes. The Pearson correlation was performed to demonstrate the number of contacts over time in relation to the evolution of restrictions. In addition, we analyzed the index cases with a high number of contacts and index cases that produced a high number of secondary cases. ResultsRapidly adapted PHSMs can reduce the mean contact rates in public places while increasing the mean contact rates within households. The mean contact rates were 11.81 (95% confidence interval, 11.61-12.01) for asymptomatic (at the time of investigation) cases and 6.70 (95% confidence interval, 6.54-6.87) for symptomatic cases. The percentage of asymptomatic cases (at the time of investigation) meeting >50 close contacts make up more than 65% of the overall cases. The percentage of asymptomatic cases producing >10 secondary cases account for more than 80% of the overall cases. ConclusionPHSMs may increase the contacts within the household, necessitating the need for pertinent prevention strategies at home. Asymptomatic cases can contribute significantly to Omicron transmission. By making asymptomatic people aware that they are already contagious, hence limiting their social contacts, it is possible to lower the transmission risk.

Quantitative prediction of the hydraulic performance of free water surface constructed wetlands by integrating numerical simulation and machine learning

Quantitative prediction of the design parameter-influenced hydraulic performance is significant for optimizing free water surface constructed wetlands (FWS CWs) to reduce point and non-point source pollution and improve land utilization. However, owing to limitations of the test conditions and data scale, a quantitative prediction model of the hydraulic performance under multiple design parameters has not yet been established. In this study, we integrated field test data, mechanism model, statistical regression, and machine learning (ML) to construct such quantitative prediction models. A FWS CW numerical model was established by integrating 13 groups of trace data from field tests. Subsequently, training, test and extension datasets comprising 125 (5^3), 25 (L25(56)) and 16 (L16(44)) data points, respectively, were generated via numerical simulation of multi-level value combination of three quantitative design parameters, namely, water depth, hydraulic loading rate (HLR), and aspect ratio. The short circuit index (φ10), Morrill dispersion index (MDI), hydraulic efficiency (λ) and moment index (MI) were used as representative hydraulic performance indicators. Training set with large samples were analyzed to determine the variation rules of different hydraulic indicators. Based on the control variable method, φ10, λ, and MI grew exponentially with increasing aspect ratio whereas MDI showed a decreasing trend; with increasing water depth, φ10, λ, and MI showed polynomial decreases whereas MDI increased; with increasing HLR, φ10, λ, and MI slowly increased linearly whereas MDI showed the opposite trend. Finally, we constructed models based on multivariate nonlinear regression (MNLR) and ML (random forest (RF), multilayer perceptron (MLP), and support vector regression. The coefficients of determination (R2) of the MNLR and ML models fitting the training and test sets were all greater than 0.9; however, the generalization abilities of different models in the extension set were different. The most robust MLP, MNLR without interaction term, and RF models were recommended as the preferred models to hydraulic performance prediction. The extreme importance of aspect ratio in hydraulic performance was revealed. Thus, gaps in the current understanding of multivariate quantitative prediction of the hydraulic performance of FWS CWs are addressed while providing an avenue for researching FWS CWs in different regions according to local conditions.

Normal and knocking combustion of hydrogen: A numerical study

This work presents a numerical study of normal and knocking combustion of hydrogen in a cooperative fuel research (CFR) engine. It is noteworthy that this study is the first to simulate this problem in a CFR engine. This engine has a significant relevance to spark-ignition engines as it is used to examine knock susceptibility of different fuels and explore fuel-engine interactions under a controlled environment. Using the Reynolds Averaged Navier Stokes (RANS) framework, a single compression ratio and four different spark timings are considered to investigate the transition from normal to knocking combustion. The results are validated against the experimental pressure trace data for both normal and knocking combustion. The model has been rigorously validated without changing the model constants across all cases. It is shown that for knocking cases an initial auto-ignition hotspot appears near the exhaust valve, where the unburned temperature is higher than the rest of the domain. A pressure wave then propagates, forming a secondary auto-ignition spot and two auto-ignitive flames eventually merge. The propagation speed of the auto-ignitive flame front is found to be 6–10 times higher than that of the premixed flame. Using the resonance theory of Bradley, the auto-ignition events are found to be in the developing detonation region. Furthermore, zero-dimensional homogeneous reactor simulations are shown to be suitable to predict the occurrence of auto-ignition in such engine, while 3-D RANS results are needed to accurately predict the pressure trace and auto-ignition timing. The results of this work also demonstrate how temperature stratification impacts the auto-ignition events.

Supplemental Material for Using browser data to understand desires to spend time online.

There is growing recognition that many people feel the need to regulate their use of the internet and other digital technologies to support their well-being. In this study, we used Mozilla Firefox browser telemetry to investigate the role played by various usage factors in desires to regulate time spent online. In particular, we investigated how six metrics pertaining to time spent on the internet, and the diversity and intensity of use, predict participants’ (n = 8,094) desires to spend more or less time online. Across all six metrics, we did not find evidence for a relationship between browser usage metrics and participants wanting to spend more or less time online. This finding was robust across various analytical pathways. The study highlights a number of considerations and concerns that need to be addressed in future industry–academia collaborations that draw on trace data or usage telemetry.

Using Browser Data to Understand Desires to Spend Time Online.

There is growing recognition that many people feel the need to regulate their use of the internet and other digital technologies to support their wellbeing. In this study, we used Mozilla Firefox browser telemetry to investigate the role played by various usage factors in desires to regulate time spent online. In particular, we investigated how six metrics pertaining to time spent on the internet, and the diversity and intensity of use, predict participants' (n = 8,094) desires to spend more or less time online. Across all six metrics, we did not find evidence for a relationship between browser usage metrics and participants wanting to spend more or less time online. This finding was robust across various analytical pathways. The study highlights a number of considerations and concerns that need to be addressed in future industry-academia collaborations that draw on trace data or usage telemetry.

Pricing and sample set strategies of data providers under quality information asymmetry

Current data transactions are usually driven by data providers, and data buyers have little information about the data they will buy. In turn, this information asymmetry impacts the data providers’ sales decisions. In this paper, we consider a data provider with private quality information to sell a data set to multiple potential data buyers, and the data provider signals quality information to the potential buyers by setting the price and sample set size. We study how the data provider decides the optimal trading strategy in terms of price and sample set size based on different characteristics of data buyers through a signalling game. It is found that the degree of information asymmetry and the cost of providing a sample set affect the data provider’s choice of whether to signal data quality truthfully or to hide quality information. The joint signal of price and sample set is a better signalling option for data providers to achieve higher profits than the price signal alone. Tracing data trading back to the production process of datasets, the increased information asymmetry and inefficient data production make data providers more inclined to generate low-quality datasets, which adversely affects the data trading market.