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• Truck drivers' working conditions: from working schedules to time away from family. • The load represents a main safety concern imposing careful storage on the vehicle. • Truck driver's attitudes and behaviours related to in-vehicle Technologies. • Truck Platooning and the new skills imposing new training needs. The platooning technology allows for two or more trucks running in convoy at a pre-defined distance between each other, being virtually connected using connectivity technology and automated driving support systems. It is recognized that truck platooning systems bring economical and environmental advantages. Thus, it is time for a transition from the existing truck freight activity towards truck platooning systems. This requires an important improvement in terms of in-vehicle technology, together with infrastructure improvement and truck drivers' acquisition of new technology-related skills. A holistic approach is previewed to identify both the requirements for the development of truck platooning services and the requests for their safe deployment in the real world. Then, qualitative data were collected from truck drivers working for two different Portuguese freight companies using Focus Groups (FG). Thus, three FG sessions were organized and carried out with a total of 22 truck drivers. Considering that age and experience on the job are important factors to take into consideration for technological changes on the job, their potential impact on truck drivers' activity was addressed on the focus group discussions. Anyway, the potential users' attitudes regarding any innovation on the job were addressed as a prevention of further negative attitudes or misuse. Having safety in mind as a permanent attitude toward on job innovation is actually the most important factor toward success. [ABSTRACT FROM AUTHOR]

Clinical tuberculosis research, both within research groups and across research ecosystems, is often undertaken in isolation using bespoke data collection platforms and applying differing data conventions. This failure to harmonise clinical phenotype data or apply standardised data collection and storage standards in turn limits the opportunity to undertake meta-analyses using data generated across multiple research projects for the same research domain. We have developed the Tuberculosis DataBase Template (TBDBT), a template for the well-supported, free and commonly deployed clinical databasing platform, REDCap. This template can be used to set up a new tuberculosis research database with a built-in set of standardised data conventions, to ensure standardised data capture across research projects and programs. A modular design enables researchers to implement only the modules of the database template that are appropriate for their particular study. The template includes core modules for informed consent data, participant demographics, clinical symptoms and presentation, diagnostic imaging and laboratory tests. Optional modules have been designed for visit scheduling and calendar functionality, clinical trial randomisation, study logistics and operations, and pharmacokinetic data. Additional fields can be added as needed. This REDCap template can facilitate collection of high-quality data for tuberculosis research, providing a tool to ensure better data harmonisation, analysis and meta-analysis. [ABSTRACT FROM AUTHOR]

Objectives: Little is known about the longitudinal course of pediatric acute-onset neuropsychiatric syndrome (PANS) because existing literature is primarily cross-sectional. To begin to address this gap, two digital platforms were used to prospectively monitor neuropsychiatric symptoms in children with PANS. The aim was to identify baseline clinical characteristics that would predict the course of neuropsychiatric symptoms over 12 weeks. We compared relative compliance between two electronic data acquisition platforms and evaluated agreement between parent-child ratings of symptoms. Methods: For 12 weeks, 20 children with PANS and their parents completed weekly rating scales of neuropsychiatric symptoms on Research Electronic Data Capture (REDCap) and concurrently parents completed tri-weekly ratings on My Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) Chart, a symptom monitoring website. Longitudinal data were analyzed by using regression analyses. Results: Greater duration of time between onset of PANS and study enrollment was associated with worsening of parent-rated neuropsychiatric symptoms over 12 weeks (p = 0.05). Higher scores on parents' Caregiver Burden Inventory at baseline predicted that children would report more severe symptoms over the 12-week period (p = 0.01). Compliance rates for parents were 86.3% for the weekly REDCap PANS Symptoms Rating Scale compared with 53.8% for the tri-weekly My PANDAS Chart ratings. There was moderate agreement between children and parents on the PANS Symptom Rating Scale (r = 0.55, p < 0.0001). Conclusion: Our study highlights the utility of electronic methods for tracking longitudinal symptoms in children with PANS and suggests that particular baseline characteristics (e.g., delay in identification and treatment of PANS, greater caregiver burden) may be indicative of a differential trajectory of PANS course, with more severe symptoms over the short term. clinicaltrials.gov NCT04382716. [ABSTRACT FROM AUTHOR]

This article presents an approach to use complex adaptive systems thinking to construct a theory of change to plan and evaluate transformational interventions. The article draws on the concepts of domains, scales, agents, and emergence to build a model of the system targeted by the intervention, to identify the chains of causality driving the system, and to identify the most influential enabling conditions affecting the system's development trajectory. Using the case study of the Indonesia SMART-Fish project, the article illustrates how to use such a theory of change to understand how a program interacts with the phenomena and how to assess the extent to which a program contributes to a development trajectory consistent with the intended long term transformational objectives. The article also illustrates how to use network analysis tools and simple data visualization techniques in ways that engage stakeholders in evaluation design, data collection and analysis. [ABSTRACT FROM AUTHOR]

Large-scale panel data is ubiquitous in many modern data science applications. Conventional panel data analysis methods fail to address the new challenges, like individual impacts of covariates, endogeneity, embedded low-dimensional structure, and heavy-tailed errors, arising from the innovation of data collection platforms on which applications operate. In response to these challenges, this paper studies large-scale panel data with an interactive effects model. This model takes into account the individual impacts of covariates on each spatial node and removes the exogenous condition by allowing latent factors to affect both covariates and errors. Besides, we waive the sub-Gaussian assumption and allow the errors to be heavy-tailed. Further, we propose a data-driven procedure to learn a parsimonious yet flexible homogeneity structure embedded in high-dimensional individual impacts of covariates. The homogeneity structure assumes that there exists a partition of regression coefficients where the coefficients are the same within each group but different between the groups. The homogeneity structure is flexible as it contains many widely assumed lowdimensional structures (sparsity, global impact, etc.) as its special cases. Non-asymptotic properties are established to justify the proposed learning procedure. Extensive numerical experiments demonstrate the advantage of the proposed learning procedure over conventional methods especially when the data are generated from heavy-tailed distributions. [ABSTRACT FROM AUTHOR]

Linking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such, linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key considerations are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data. [ABSTRACT FROM AUTHOR]

With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed. [ABSTRACT FROM AUTHOR]

Optimal milking management leads to an increase in production efficiency while respecting animal welfare and health. Not all farms can equip themselves with high level technological systems for economic, generational and structural reasons. The aim of the study was to assess milking performances, cow behaviour and microclimatic condition in farm areas using an integrated data collection system based on surveys and low-cost sensors. The use of video recordings and mobile electronic flowmeters highlighted significant differences between farms that milked two and three times a day. Milking efficiency parameters (cow/hour; cows/stall per hour; milk/hour; milk/stall per hour) were calculated. In particular, the milk/stall per hour parameter emerged as the most interesting. Animal stay time, defined as the time running from when cows are moved from the housing to the beginning of the milking, was calculated and the highest value (76.3 minutes) was observed in farms with more than 10 stalls/row. Hobo pendant sensors were used in order to evaluate cows lying behaviour: no differences were observed between farms that milked two and three times a day (11.3 vs 11.0 h/d of lying). Microclimatic conditions were influenced by both the season (p <.001) and farm areas (p <.05) (holding area, milking parlour, housing area). Light intensity in milking parlour emerged as a critical point; further research is needed to understand the relation with udder hygiene and health. The integration of manual measurements with the use of low-cost sensors allowed to monitor the efficiency of the process and microclimatic condition in farm areas. The integration of manual measurements and low-cost sensors was tested Different parameters can be used to monitor milking efficiency Light intensity in milking parlour emerged as a critical point [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) have been increasingly popular in rehabilitation research. However, despite their accessibility and potential advantages, their uptake and acceptance by health professionals remain a big challenge. The development of an IMU-based clinical tool must bring together engineers, researchers and clinicians. This study is part of a developmental process with the investigation of clinicians' perspectives about IMUs. Clinicians from four rehabilitation centers were invited to a 30-minute presentation on IMUs. Then, two one-hour focus groups were conducted with volunteer clinicians in each rehabilitation center on: 1) IMUs and their clinical usefulness, and 2) IMUs data analysis and visualization interface. Fifteen clinicians took part in the first focus groups. They expressed their thoughts on: 1) categories of variables that would be useful to measure with IMUs in clinical practice, and 2) desired characteristics of the IMUs. Twenty-three clinicians participated to the second focus groups, discussing: 1) functionalities, 2) display options, 3) clinical data reported and associated information, and 4) data collection duration. Potential influence of IMUs on clinical practice and added value were discussed in both focus groups. Clinicians expressed positive opinions about the use of IMUs, but their expectations were high before considering using IMUs in their practice. [ABSTRACT FROM AUTHOR]

Mixed methods surveys using multilingual assistants in areas without clear pre-defined sampling units are often difficult to manage and unpredictable, thereby threatening data quality. More so when such surveys are executed in unfamiliar territories where cultural and language barriers prevent direct communication between the principal researcher and the respondents, and field assistants act as cultural/language brokers. This paper provides a method to enhance data quality in such contexts through a reflection on the research design and data collection experience in our study on flood damage mitigation processes among households and businesses in Kampala. Associated field challenges included different disciplinary and language skills levels of assistants, their multiple positionalities when translating concepts from the inquiry language to the source language and vice-versa, low settlement permeability/accessibility, multiple households per sampling unit, and socio-psychological issues among respondents and research assistants. The available literature lacks comprehensive systematic methods to address the challenges above, using detailed research fieldwork management experiences in which data collection is outsourced from field assistants. Our application of an extended version of the briefing and debriefing technique closes this gap. As a guide to apply this approach, we conceptualize social science research into four inter-related dimensions – paradigm, people, process, and presentation. [ABSTRACT FROM AUTHOR]

Big data has the characteristics of rapid data flow, massive data scale, dynamic data system, and various data types, and it has become increasingly apparent in improving innovation and entrepreneurship data analysis, trend prediction, and decision support. In this paper, the authors analyze the economic function data and entrepreneurship analysis based on machine learning. The support vector pair is very sensitive to the choice of parameters, and the parameters obtained using the genetic algorithm will greatly improve the accuracy of the model prediction. When using the genetic algorithm to find parameters, the cv method is used for verification. By applying big data technologies and platforms, it can provide strong data support to establish entrepreneurship education; integrate and integrate various types of innovation and entrepreneurship data, improve the quality of data collection.At the same time, through big data mining and analysis, accurately determine market demand hotspots and innovation and entrepreneurship trends, and promote scientific planning of innovation and entrepreneurship strategies. The research results show that this research model can be applied to actual projects in the future, and help investors better understand the changes of market economy. [ABSTRACT FROM AUTHOR]

Knowing the spatiotemporal distribution of precipitation is undoubtedly important for planning various economic/social activities, such as agriculture, livestock, and energy production. The coarse observation density over certain regions may significantly compromise the quality of precipitation products interpolated by only surface observations. To minimize the lack of observations over certain regions, the Centre for Weather Forecast and Climate Studies (CPTEC) of National Institute for Space Research (INPE) developed two types of blended precipitation products, namely, the Combined Scheme (CoSch) and MERGE, which combine observed precipitation data with satellite estimates on a daily scale. To understand how different blending methodologies impact the final results, a comparison of each algorithm with independent rain gauges was performed with a focus over the Brazilian territory. Both products were generated at a 10-km horizontal resolution using input data from the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG-Early) for product (Version 5) in conjunction with surface observations from Surface Synoptic Observations (SYNOP), data collection platforms (DCPs) and data from regional meteorological centres. The cumulative 24-hour precipitation was evaluated for the period from June 2014 to June 2017. The results show that both products reliably characterize the precipitation regimes over most of the study regions, although MERGE and CoSch tend to over- and underestimate the amount of precipitation, respectively. However, the magnitude of the Bias achieved by MERGE is smaller than that achieved by CoSch. Overall, MERGE outperforms CoSch when analysing rain/no rain and light to moderate rainfall (0.5 to 20.0 mm). For heavy precipitation (>35.0 mm), the performance of both products is similar. The most significant differences between the two products occur over the Northeast Region of Brazil (R3 and R4), where CoSch tends to encounter difficulties characterizing the precipitation regime during the northeastern wet period (April – November). In R3 and R4, MERGE relies more on surface observations, whereas CoSch relies on GPM-IMERG-Early, which could be associated with the deficiency of GPM-IMERG-Early in estimating the amount of precipitation associated with warm clouds. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. Wireless sensor networks are relatively simple, scalable networks with many applications in research. They can provide many benefits, including ad hoc distribution, lower costs, and higher flexibility. In a scenario where time is of the essence and dedicated base stations cannot be established, mobile sinks must be used to gather data. IoT systems are based on a collective organization in which devices collaborate to provide better and more accurate decisions. It is important to ensure that the information being shared is legitimate to avoid any significant degradation in system performance because of false or inaccurate information. Building trust—the "assurance" between two devices that the information being shared can be used with confidence that it is accurate—will create a trustworthy, secure system in which all devices are identified and no information is accepted from any unauthorized device. The key contribution of this work is a new, dynamic, trust-based clustering mechanism by which nodes can securely connect to one another and begin transmitting data to a sink while it is available. To demonstrate the utility of this mechanism, we examine two possible attacks on a trust-based network and present a heuristic solution for minimizing the negative effects of such attacks in an energy-efficient way. Our results show improved network performance through reduction of the number of cycles required to isolate or mitigate the effect of malicious nodes in the network, thus reducing the energy consumption in the network with a concomitant increase in its lifespan. Our cluster methodology also has the effect of spreading energy consumption among nodes, thereby reducing early fall-off of nodes and network holes. [ABSTRACT FROM AUTHOR]

With the development of the resource exploration and environmental science drilling, strict and scientific requirements are put forward for the samples taken from drilling. It is significant to keep the original appearance of the core and obtain the in situ core for the analysis of deep geological fluid and the exploration of the law of geological disasters caused by large-scale geological exploitation. To achieve the high-fidelity in situ core of deep rock, the development of the corresponding deep in situ fidelity coring device should involve the insulation coring device. The development of deep in situ fidelity coring device is a typical sophisticated product design. There are many problems in the design process, such as multimodules, multidisciplinary, crossdomain, and high coupling, which makes it more difficult for users to participate in product design and understand the product design intention. Digital twin technology, such as time data collection, accelerated iterative optimization, and high-fidelity rendering, provides users with an immersive experience and deepens their understanding of the product design intention. The exploration of the novel design model combined digital twin technology with innovative design theory. Digital twin innovative design of the deep in situ insulation coring device is based on the innovative design method, which built a digital connection between the pre-research test platform and the corresponding simulation models. This digital twin to help users participate in product design and understand the product design process. Finally, the TOPSIS evaluation model was used to calculate the user's score on the design scheme, which increased by 27.64%, which improves the overall efficiency of product design. This paper provides a practical design method and technical means for the design of the deep in situ insulation coring device based on the geological mechanism and control theory of thermal insulation core. [ABSTRACT FROM AUTHOR]

This paper presents an exemplar of blending the principles of participatory action research and elements of grounded theory in a disaster risk reduction education case study. It illustrates and describes a modified methodological approach that was used during the needs' assessment and analysis phase of a multiphase study on teaching of disaster risk reduction in science among public schools in Biliran Province, the Philippines. The approach was conceived upon considering the overarching aim of the study which is the effective, efficient, inclusive, and proactive teaching of disaster risk reduction in science, the complex nature of the disaster risk reduction education, and the multiple stakeholders involved in disaster risk reduction among public schools. Results revealed that the modified methodological approach provided a co-learning environment for both participants and researchers, created an opportunity to maximize participation toward generating knowledge, prioritizing problems, and conceptualizing solutions, strengthened the data collection and analysis process, hence ensuring quality in the entire research process, and addressed the participation issues pointed out in grounded theory studies. The modified methodological approach may be relevant and applicable to similar studies that are complex and emerging like the teaching of disaster risk reduction in science. [ABSTRACT FROM AUTHOR]

This article aims to compose an analytical framework about the production of contemporary Brazilian geographers who have the indigenous theme as one of their central aspects, based on the research and data collection in texts and articles produced on this theme from 2004 to 2016, in congresses in this area. We work with the texts of the "Encontro Nacional de Geógrafos (ENG)" and the "Simpósio Internacional de Geografia Agrária (SINGA)", from which we organize quantitative data, in addition to analyzing which concepts and theoretical-methodological perspectives Brazilian geography has been addressing these issues. We identified an increase in production on indigenous themes in geography, but we highlight the need for greater convergence between studies in this area. This text constitutes a starting point in the sense of knowing and making visible the geographical productions in Brazil in the face of indigenous themes, as well as the protagonism and acting of geographers in this field in the last decades. [ABSTRACT FROM AUTHOR]

The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of "communicating concretes," which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions. [ABSTRACT FROM AUTHOR]

This study examines the research methods, topics, and trends of empirical MOOC research to gain a comprehensive understanding of the MOOC phenomenon through reviewing 541 empirical MOOCs research published from 2009 to 2019. The results indicate that: (1) the majority of studies adopted quantitative research methods followed by mixed research methods and qualitative research methods, (2) the most frequently adopted data collection method was survey, followed by platform database and interviews, (3) almost half of the studies used at least two data collection methods such as survey and interview, (4) the majority of researchers adopted descriptive statistics for data analysis, followed by inferential statistics and content analysis, (5) the research topics primarily focused on students, followed by design-focused, context and impact-focused, and instructor-focused. Among these student-focused topics, learner retention, learning experience, social learning, and engagement were the most mentioned, and (6) the affiliations of the first authors of the MOOC studies were mainly from the U.S. followed by China and Spain. Implications and future research were discussed. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming. Purpose: We utilized a machine learning (ML)-based natural language processing (NLP) approach to extract clinical terms from unstructured radiology reports. Additionally, we investigate the prognostic value of the extracted data in predicting all-cause mortality (ACM) in HF patients. Materials and methods: This observational cohort study utilized 122,025 thoracoabdominal computed tomography (CT) reports from 11,808 HF patients obtained between 2008 and 2018. 1,560 CT reports were manually annotated for the presence or absence of 14 radiographic findings, in addition to age and gender. Thereafter, a Convolutional Neural Network (CNN) was trained, validated and tested to determine the presence or absence of these features. Further, the ability of CNN to predict ACM was evaluated using Cox regression analysis on the extracted features. Results: 11,808 CT reports were analyzed from 11,808 patients (mean age 72.8 ± 14.8 years; 52.7% (6,217/11,808) male) from whom 3,107 died during the 10.6-year follow-up. The CNN demonstrated excellent accuracy for retrieval of the 14 radiographic findings with area-under-the-curve (AUC) ranging between 0.83–1.00 (F1 score 0.84–0.97). Cox model showed the time-dependent AUC for predicting ACM was 0.747 (95% confidence interval [CI] of 0.704–0.790) at 30 days. Conclusion: An ML-based NLP approach to unstructured CT reports demonstrates excellent accuracy for the extraction of predetermined radiographic findings, and provides prognostic value in HF patients. [ABSTRACT FROM AUTHOR]

Summary: In smart city scenarios, data collected by sensors are required to be conveyed to central controllers for processing. Thus, efficient data collection mechanisms considering the urban environment characteristics should take place. To that end, the data mules approach is proved to be efficient. However, being applied to urban environment leads to high data delivery latency due to the characteristics of this environment. Motivated by the need to reduce the collection latency of such an approach, we propose to provide each road segment with a data mule to create a process of distributed data collection. Our scheme leverages opportunistic inter‐vehicular communications for assigning the mule role to adequate vehicles. The simulation in realistic mobility settings reveals that our scheme can provide more than 80% of the area of interest with data mules in less than 1 minute. In addition, the data collection latency can be reduced from 24 hours to few minutes. [ABSTRACT FROM AUTHOR]

Qualitative researchers face unique opportunities and challenges as a result of the disruption of COVID-19. Although the pandemic represents a unique opportunity to study the crisis itself, social distancing mandates are restricting traditional face-to-face investigations of all kinds. In this article, we describe options and resources for researchers who find themselves needing to alter their study designs from face-to-face qualitative data collection to a "socially distant" method. Although technologies are constantly changing, we review the latest videoconferencing services available to researchers and provide guidance on what services might best suit a project's needs. We describe options for various platforms and applications including information about enhanced security applications for researchers collecting sensitive patient health information. Concerns about these technologies including security of the platform and logistical needs such as computer equipment are also discussed. Special attention is given to ethical issues when transitioning research efforts to online venues. [ABSTRACT FROM AUTHOR]

Context: Inpatient toxicology services undertake remote as well as inpatient management of poisoned patients. The aim of this study is to describe the introduction of a tablet-based electronic data collection tool allowing data to be captured on inpatient and remote consultations. Methods: Retrospective review of all cases entered in the database from 1 March 2014 to 28 February 2016. Data collected included demographics (age, sex), clinical details (exposure category), presentation facility and disposition. Results: The database included 3616 cases: 59 (1.6%) were excluded due to inadequate details, 122 (3.4%) had no electronic medical record available, 1985 (54.9%) presented to the inpatient unit facility and 1450 (40.1%) were external consultations. Of these 1450, 223 (6.2%) were paediatric (aged less than 12 years), 395 (10.9%) adolescent (12–17 years) and 832 (23.0%) adults (18 years and over). The proportion of paediatric cases (median age 2 y; 45.7% females) with pharmaceutical ingestions was 122 (54.7%; 95% confidence intervals (CIs): 48.2–61.1) compared with 345 (87.3%; 95% CI: 83.7–90.3) in adolescents (median age 15 y; 79.5% females). Of the adult presentations, 659 (18.2%) were metropolitan/regional facility presentations and 173 (4.8%) rural facilities with 125 (3.4%) adults subsequently transferred to the inpatient facility. Median age was 38 years (interquartile range (IQR) 35–52) with 338 (51.4%) females in the metropolitan group and 37 years (IQR 26–48) with 51 (30.5%) females in the rural group. There were more bites and stings in the rural group, 41 (23.7%; 95% CI: 18.0–30.6) versus 54 (8.2%; 95% CI: 6.3–10.5), more recreational substance exposures 27 (15.6%; 95% CI: 11.0–21.8) versus 40 (6.1%; 95% CI: 4.5–8.2) and less pharmaceutical exposures 93 (53.8%; 95% CI: 46.3–61.0) versus 462 (70.1%; 95% CI: 66.5–73.5). Conclusions: The tablet based database provided useful information on populations of poisoned patients not accessible previously. It demonstrated important differences in the types of patients presenting to rural versus metropolitan hospitals [ABSTRACT FROM AUTHOR]

Background: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline.Methods: We implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities.Results: The citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx.Conclusions: Initial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships. [ABSTRACT FROM AUTHOR]

Collecting data from businesses faces ever-larger challenges, some of them calling for an overhaul of underlying methodology, e.g. motivation for participating is low; technology is shaping data collection processes; response processes within businesses are imperfectly understood while alternative data sources originating from digitalization processes push the response process (thus also response quality) further out of our sight. The paper reviews these challenges, discusses them in light of new developments in the field, and proposes directions for future research. This review may help those that collect data from businesses (e.g. national statistical institutes, academia, and private statistical agencies) to reconsider their current approaches in light of what promises to work (or not) in today's environment and to build their toolkit of business data collection methods. [ABSTRACT FROM AUTHOR]

In the Asian region, no international organ transplantation registry exists. Individual centers maintain their own database, or some countries developed a national registration system. To promote collaboration among Asian transplantation societies, the Asian Society of Transplantation (AST) has developed an international transplantation registry for the Asian countries that has been named as the Asian Society Transplant Registry (ASTREG). In 2017, the AST council formed a registry committee to develop 2 kinds of databases: ASTREG-N (nationwide level), which collects yearly aggregated data of participating countries, and ASTREG-H (hospital level), which collects the data of transplant recipients and donors from individual centers. ASTREG-N collects each country's aggregate data of solid-organ transplantation, such as the total number of transplantations and deceased donors. ASTREG-H collects 5 transplant domains, namely recipient baseline characteristics, immunosuppression, post-transplant event, annual post-transplant evaluation, and donor traits. For the ASTREG-H project, South Korea, Philippines, Mongolia, and Myanmar are the current participants. A web-based secure data entry platform with real-time data visualization and automated data verification systems is currently available. Any participating centers can run this platform as their own data collection system. The ASTREG is a collaborative project that will be the representative solid-organ transplantation database in the Asian region. It can aid in the harmonization of transplantation data in the Asian region. [ABSTRACT FROM AUTHOR]

Inadequate sanitation can lead to exposure to fecal contamination through multiple environmental pathways and can result in adverse health outcomes. By understanding the relative importance of multiple exposure pathways, sanitation interventions can be tailored to those pathways with greatest potential public health impact. The SaniPath Exposure Assessment Tool allows users to identify and quantify human exposure to fecal contamination in low-resource urban settings through a systematic yet customizable process. The Tool includes: a project management platform; mobile data collection and a data repository; protocols for primary data collection; and automated exposure assessment analysis. The data collection protocols detail the process of conducting behavioral surveys with households, school children, and community groups to quantify contact with fecal exposure pathways and of collecting and analyzing environmental samples for E. coli as an indicator of fecal contamination. Bayesian analyses are used to estimate the percentage of the population exposed and the mean dose of fecal exposure from microbiological and behavioral data. Fecal exposure from nine pathways (drinking water, bathing water, surface water, ocean water, open drains, floodwater, raw produce, street food, and public or shared toilets) can be compared through a common metric–estimated ingestion of E. coli units (MPN or CFU) per month. The Tool generates data visualizations and recommendations for interventions designed for both scientific and lay audiences. When piloted in Accra, Ghana, the results of the Tool were comparable with that of an in-depth study conducted in the same neighborhoods and highlighted consumption of raw produce as a dominant exposure pathway. The Tool has been deployed in nine cities to date, and the results are being used by local authorities to design and prioritize programming and policy. The SaniPath Tool is a novel approach to support public-health evidence-based decision-making for urban sanitation policies and investments. [ABSTRACT FROM AUTHOR]

The teaching of professional roles in medical education is an interdisciplinary concern. However, surgeons require specific standards of professionalism for certain context-based situations. In addition to communication, studies require collaboration, leadership, error-/conflict-management, patient-safety and decision-making as essential competencies for surgeons. Standards for corresponding competencies are defined in special chapters of the German National Competency-based Learning Objectives for Undergraduate Medical Education (NKLM; chapter 8, 10). The current study asks whether these chapters are adequately taught in surgical curricula. Eight German faculties contributed to analysing mapping data considering surgical courses of undergraduate programs. All faculties used the MERlin mapping platform and agreed on procedures for data collection and processing. Sub-competency and objective coverage, as well as the achievement of the competency level were mapped. Overall counts of explicit citations were used for analysis. Collaboration within the medical team is a strongly represented topic. In contrast, interprofessional cooperation, particularly in healthcare sector issues is less represented. Patient safety and dealing with errors and complications is most emphasized for the Manager/Leader, while time management, career planning and leadership are not addressed. Overall, the involvement of surgery in teaching the competencies of the Collaborator and Manager/Leader is currently low. However, there are indications of a curricular development towards explicit teaching of these roles in surgery. Moreover, implicitly taught roles are numerous, which indicates a beginning awareness of professional roles. [ABSTRACT FROM AUTHOR]

The understanding of the benefit risk profile, and relative effectiveness of a new medicinal product, are initially established in a circumscribed patient population through clinical trials. There may be uncertainties associated with the new medicinal product that cannot be, or do not need to be resolved before launch. Postlicensing or postlaunch evidence generation (PLEG) is a term for evidence generated after the licensure or launch of a medicinal product to address these remaining uncertainties. PLEG is thus part of the continuum of evidence development for a medicinal product, complementing earlier evidence, facilitating further elucidation of a product's benefit/risk profile, value proposition, and/or exploring broader aspects of disease management and provision of healthcare. PLEG plays a role in regulatory decision making, not only in the European Union but also in other jurisdictions including the USA and Japan. PLEG is also relevant for downstream decision‐making by health technology assessment bodies and payers. PLEG comprises studies of different designs, based on data collected in observational or experimental settings. Experience to date in the European Union has indicated a need for improvements in PLEG. Improvements in design and research efficiency of PLEG could be addressed through more systematic pursuance of Scientific Advice on PLEG with single or multiple decision makers. To date, limited information has been available on the rationale, process or timing for seeking PLEG advice from regulators or health technology assessment bodies. This article sets out to address these issues and to encourage further uptake of PLEG advice. [ABSTRACT FROM AUTHOR]

This article makes the case for a new regional mechanism to coordinate the development of data standards. Data standards are necessary to achieve digital cooperation. We need standards to create data value chains that string together data collection through collaborative platforms in order to generate insights and solve longstanding problems. Organizations of all sizes, whether public, private, or not-for-profit, need a suite of data governance standards to manage issues such as data ownership and use, security, residency, privacy, and the protection of fundamental rights.1 [ABSTRACT FROM AUTHOR]

Children's school transport has become a major research focus over the past 20 years, particularly within the fields of planning, transport geography, and children's geographies. However, this work has rarely considered children living with disability. This article presents the pilot component of a visual ethnographic study about school transport within families living with childhood disability. It explains why we piloted the study, details the pilot process, and presents lessons learned from piloting the study with two families of children living with different mobility needs. The lessons largely concern the design of interview guides and the interviewing process, as well as the development of an adaptive photo kit to enable children's independent participation. The article shows how piloting qualitative studies, particularly those involving disabled children, can be invaluable in terms of improving data collection technologies and logistics, as well as enabling inclusive participation. [ABSTRACT FROM AUTHOR]

Background: The utilization of routine health information is an essential factor of the structural capacity of health departments and public health performance depends on the effectiveness of information use for routine and programed decisions. Considerable research has been conducted in health data collection and ways to improve data quality, but little is known about utilization of routine health information among health workers in Ethiopia in general and in the study area in particular. Objectives: The aim of this study was to assess level of utilization of routine health information and associated factors among health workers in Hadiya zone, Southern Ethiopia, 2019. Methods and materials: Facility-based cross-sectional study design with both quantitative and qualitative data collection methods was employed at the Hadiya zone from March 10–25, 2019. A total of 480 health workers were included in the study and systematic random sampling was employed to select the health care workers in the study. The results were analyzed and presented in tables and graphs. Finally, the binary logistic regression was used to examine independent predictors. Result: Good level of utilization of routine health information was reported by 301 (62.7%, 95% CI: 58.5%-66.9%) of the health workers. Training [AOR = 8.12; 95% CI: (4.33–15.23)], supportive supervision [AOR = 2.34; 95% CI:(1.40–3.92)], good perceived culture of health information [AOR = 5.05; 95% CI: (2.99–8.50], having a standard set of indicators (AOR = 2.05 95%; CI: (1.23–3.41) and competence on routine health information tasks [AOR = 5.85; 95% CI: (3.41–10.02)] were independent predictors. Conclusion: Good level of utilization of routine health information was noted in less than two-third of the study participants. Further training, supportive supervision, perceived culture of health information, having standard set of indicators and competence on routine health information task were factors that improve routine health information utilization. [ABSTRACT FROM AUTHOR]

Currently, data classification is one of the most important ways to analysis data. However, along with the development of data collection, transmission, and storage technologies, the scale of the data has been sharply increased. Additionally, due to multiple classes and imbalanced data distribution in the dataset, the class imbalance issue is also gradually highlighted. The traditional machine learning algorithms lack of abilities for handling the aforementioned issues so that the classification efficiency and precision may be significantly impacted. Therefore, this paper presents an improved artificial neural network in enabling the high-performance classification for the imbalanced large volume data. Firstly, the Borderline-SMOTE (synthetic minority oversampling technique) algorithm is employed to balance the training dataset, which potentially aims at improving the training of the back propagation neural network (BPNN), and then, zero-mean, batch-normalization, and rectified linear unit (ReLU) are further employed to optimize the input layer and hidden layers of BPNN. At last, the ensemble learning-based parallelization of the improved BPNN is implemented using the Hadoop framework. Positive conclusions can be summarized according to the experimental results. Benefitting from Borderline-SMOTE, the imbalanced training dataset can be balanced, which improves the training performance and the classification accuracy. The improvements for the input layer and hidden layer also enhance the training performances in terms of convergence. The parallelization and the ensemble learning techniques enable BPNN to implement the high-performance large-scale data classification. The experimental results show the effectiveness of the presented classification algorithm. [ABSTRACT FROM AUTHOR]

The increasing use of social networking platforms has raised the need to develop automated multi-classifications on document network. In this paper, we propose a supervised relation topic model that leverages the links between documents to learn the latent content of documents and enhance performance of prediction. Our model takes advantage of Bayesian generative model to exploit the relation between word feature and link feature in a document network. We evaluate our model on large-scale data collections that include scientific citation community and medical article network. We demonstrate its effectiveness and efficiency on document classification with SLDA model and collective classification approaches. [ABSTRACT FROM AUTHOR]

Background: The Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practice-based research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators. CIMDRN aims to develop evidence to improve health outcomes for children with inherited metabolic diseases (IMD). We describe the development of our clinical data collection platform, discuss our data quality management plan, and present the findings to date from our data quality assessment, highlighting key lessons that can serve as a resource for future clinical research initiatives relating to rare diseases.Methods: At participating centres, children born from 2006 to 2015 who were diagnosed with one of 31 targeted IMD were eligible to participate in CIMDRN's clinical research stream. For all participants, we collected a minimum data set that includes information about demographics and diagnosis. For children with five prioritized IMD, we collected longitudinal data including interventions, clinical outcomes, and indicators of disease management. The data quality management plan included: design of user-friendly and intuitive clinical data collection forms; validation measures at point of data entry, designed to minimize data entry errors; regular communications with each CIMDRN site; and routine review of aggregate data.Results: As of June 2019, CIMDRN has enrolled 798 participants of whom 764 (96%) have complete minimum data set information. Results from our data quality assessment revealed that potential data quality issues were related to interpretation of definitions of some variables, participants who transferred care across institutions, and the organization of information within the patient charts (e.g., neuropsychological test results). Little information was missing regarding disease ascertainment and diagnosis (e.g., ascertainment method - 0% missing).Discussion: Using several data quality management strategies, we have established a comprehensive clinical database that provides information about care and outcomes for Canadian children affected by IMD. We describe quality issues and lessons for consideration in future clinical research initiatives for rare diseases, including accurately accommodating different clinic workflows and balancing comprehensiveness of data collection with available resources. Integrating data collection within clinical care, leveraging electronic medical records, and implementing core outcome sets will be essential for achieving sustainability. [ABSTRACT FROM AUTHOR]

Lymphomas are a large, heterogeneous group of neoplasms with well-defined characteristics, and this heterogeneity highlights the importance of epidemiological data. Knowledge of local epidemiology is essential to optimise resources, design clinical trials, and identify minority entities. Given there are few published epidemiological data on lymphoma in Spain, the Spanish Lymphoma and Autologous Bone Marrow Transplant Group created the RELINF project. The aim of this project is to determine the frequencies and distribution of lymphoid neoplasms in Spain and to analyse survival. We developed an online platform for the prospective collection of data on newly diagnosed cases of lymphoma in Spain between January 2014 and July 2018; 11,400 patients were registered. Diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL) were the most frequent lymphomas in our series. Marginal B cell lymphoma frequency was higher than that reported in other studies, representing more than 11% of mature B cell lymphomas. Peripheral T cell lymphoma not otherwise specified (PTCL-NOS) was the most common subtype of T cell lymphoma, and NK/T cell lymphomas were more frequent than expected (5.4% of total). Hodgkin's lymphoma accounted for 12% of lymphoproliferative syndromes. Overall survival was greater than 90% at 2 years for indolent B cell lymphomas, and approximately 60% for DLBCL, somewhat lower than that previously reported. Survival was poor for PTCL-NOS and angioimmunoblastic T cell lymphoma, as expected; however, it was somewhat better than that in other studies for anaplastic large cell anaplastic lymphoma kinase lymphomas. This is the first prospective registry to report the frequencies, distribution, and survival of lymphomas in Spain. The frequencies and survival data we report here are globally consistent with that reported in other Western countries. These updated frequencies and survival statistics are necessary for developing appropriate management strategies for neoplasias in the Spanish population. [ABSTRACT FROM AUTHOR]

We have carried out an intensive study of photometric (Kepler Mission) and spectroscopic data on the system Kepler-2 (HAT-P-7A) using the dedicated software WinFitter 6.4. The mean individual data-point error of the normalized flux values for this system is 0.00015, leading to the model's specification for the mean reference flux to an accuracy of ∼0.5 ppm. This testifies to the remarkably high accuracy of the binned data-set, derived from over 1.8 million individual observations. Spectroscopic data are reported with the similarly high-accuracy radial velocity amplitude measure of ∼2 m s−1. The analysis includes discussion of the fitting quality and model adequacy. Our derived absolute parameters for Kepler-2 are as follows: M p (Jupiter) 1.80 ± 0.13; R ⋆ 1.46 ± 0.08 × 10 6 km; R p 1.15 ± 0.07 × 10 5 km. These values imply somewhat larger and less condensed bodies than previously catalogued, but within reasonable error estimates of such literature parameters. We find also tidal, reflection and Doppler effect parameters, showing that the optimal model specification differs slightly from a 'cleaned' model that reduces the standard deviation of the ∼3600 binned light curve points to less than 0.9 ppm. We consider these slight differences, making comparisons with the hot-Jupiter systems Kepler-1 (TrES-2) and 13. We confirm that the star's rotation axis must be shifted towards the line of sight, though how closely depends on what rotation velocity is adopted for the star. From joint analysis of the spectroscopic and photometric data we find an equatorial rotation speed of 11 ± 3 km s−1. A slightly brighter region of the photosphere that distorts the transit shape can be interpreted as an indication of the gravity effect at the rotation pole; however we note that the geometry for this does not match the spectroscopic result. We discuss this difference, rejecting the possibility that a real shift in the position of the rotation axis in the few years between the spectroscopic and photometric data-collection times. Alternative explanations are considered, but we conclude that renewed detailed observations are required to help settle these questions. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost, which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost, which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs. [ABSTRACT FROM AUTHOR]

Contrary to many previous studies on population protocols using the uniformly random scheduler, we consider a more general non-uniform case. Here, pair-wise interactions between agents (moving and communicating devices) are assumed to be drawn non-uniformly at random. While such a scheduler is known to be relevant for modeling many practical networks, it is also known to make the formal analysis more difficult. This study concerns data collection , a fundamental problem in mobile sensor networks (one of the target networks of population protocols). In this problem, pieces of information given to the agents (e.g., sensor readings) should be eventually delivered to a predefined sink node without loss or duplication. Following an idea of the known deterministic protocol TTF solving this problem, we propose an adapted version of it and perform a complete formal analysis of execution times in expectation and with high probability (w.h.p.). We further investigate the important issue of energy consumption. The goal is to improve TTF in terms of energy complexity, while still keeping good time complexities (in expectation and w.h.p.). Namely, we propose a new parameterized protocol for data collection, called lazy TTF, and present a study showing that an appropriate choice of the protocol parameters can considerably improve energy performances (compared to TTF), at a slight expense of time performance. [ABSTRACT FROM AUTHOR]

Survey data collection costs have risen to a point where many survey researchers and polling companies are abandoning large, expensive probability-based samples in favor of less expensive nonprobability samples. The empirical literature suggests this strategy may be suboptimal for multiple reasons, among them that probability samples tend to outperform nonprobability samples on accuracy when assessed against population benchmarks. However, nonprobability samples are often preferred due to convenience and costs. Instead of forgoing probability sampling entirely, we propose a method of combining both probability and nonprobability samples in a way that exploits their strengths to overcome their weaknesses within a Bayesian inferential framework. By using simulated data, we evaluate supplementing inferences based on small probability samples with prior distributions derived from nonprobability data. We demonstrate that informative priors based on nonprobability data can lead to reductions in variances and mean squared errors for linear model coefficients. The method is also illustrated with actual probability and nonprobability survey data. A discussion of these findings, their implications for survey practice, and possible research extensions are provided in conclusion. [ABSTRACT FROM AUTHOR]

• Individual FTG rates can give a good characterization of the zone. • Zonal FTG rates can be obtained from individual FTG rates. • Number of employees was found to be the only representative variable for models. • Combining the stated methodologies lead to a suitable FTG framework for Latin America. Ever-growing urban areas and global population movement towards urbanization lead to major concerns regarding urban logistics and last mile operations. In Latin America, the problem becomes critical since volatile emerging economies and unstable political situations, which are common in the region, introduce additional limitations for strong logistics solutions. In the city of Quito (Ecuador), traffic regulations only consider time-schedule restrictions for vehicle mobility without any other policies that benefit urban goods movement. This is in part because there is a lack of knowledge on freight flows, mainly related to the difficulties to retrieve data to that purpose. This paper proposes a freight trip generation analysis in Quito, based on a methodology included in "MIT's Better Cities for Logistics Toolkit", which defines specific zones observation and data collection campaigns. More precisely, a procedure combining observation-based and declarative data collection processes is proposed. First, the opportunities of combining both observed and declared data to characterize freight trip generation are addressed on the basis of a literature review. Main issues of combining establishment based surveys and observations are addressed to generalize the proposed framework. Finally, application implications in a transferability perspective to other Latin American countries are addressed. [ABSTRACT FROM AUTHOR]

Pavement condition data are collected by agencies to support pavement management system (PMS) for decision-making purpose as well as to construct performance model. The cost of pavement data collection increases with the increase of survey frequencies. However, a lower monitoring frequency could lead to unreliable maintenance decisions. It is necessary to understand the influence of monitoring frequencies on maintenance decision by considering the reliability of performance prediction models. Because of different maintenance conditions of urban roads and highways, their performance show different trends. In this paper, the influence of pavement monitoring frequency on the pavement performance models was investigated. The results indicate that low collection frequencies may result in delay in maintenance action by overestimating pavement performance. The collection frequency for Pavement Condition Index (PCI) can be reduced without compromising the accuracy of performance model, more work should be done to ensure the PCI data quality, thus to guarantee the rationality of maintenance decisions. Effect of frequency reduction on pavement performance (IRI) models of urban roads seems greater than on pavement performance (IRI) models of highways, which may lead to heavier monitoring work for urban roads management. This paper provided an example which demonstrated how a comparative analysis can be performed to determine whether the current data collection plan can provide sufficient data for time series analysis. [ABSTRACT FROM AUTHOR]

Background: Researchers and clinicians use text messages to collect data with the advantage of real time capture when compared with standard data collection methods. This article reviews project setup and management for successfully collecting patient-reported data through text messages.Methods: We review our experience enrolling over 2600 participants in six clinical trials that used text messages to relay information or collect data. We also reviewed the literature on text messages used for repeated data collection. We classify recommendations according to common themes: the text message, the data submitted and the phone used.Results: We present lessons learned and discuss how to create text message content, select a data collection platform with practical features, manage the data thoughtfully and consistently, and work with patients, participants and their phones to protect privacy. Researchers and clinicians should design text messages to include short, simple prompts and answer choices. They should decide whether and when to send reminders if participants do not respond and set parameters regarding when and how often to contact patients for missing data. Data collection platforms send, receive, and store messages. They can validate responses and send error messages. Researchers should develop a protocol to append and correct data in order to improve consistency with data handling. At the time of enrollment, researchers should ensure that participants can receive and respond to messages. Researchers should address privacy concerns and plan for service interruptions by obtaining alternate participant contact information and providing participants with a backup data collection method.Conclusions: Careful planning and execution can reward clinicians and investigators with complete, timely and accurate data sets. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis. [ABSTRACT FROM AUTHOR]

Crowdsensing has been recognized as a promising data collection paradigm, in which a platform outsources sensing tasks to a large number of users. However, requesting users to report raw data may give rise to many practical concerns, such as a significant overhead of communication and central processing, besides users’ privacy concerns. In many scenarios (e.g, advertising and recommendation), the data collector directly benefits from statistical aggregation of raw data. Thus motivated, we consider the data collection problem based on user's local histograms, which is intimately related to the fundamental trade-off between the platform's accuracy and users’ privacy. Because of users’ social relationship, their data are often correlated, indicating that users’ privacy may be leaked from others’ data. To tackle this challenge, we first utilize Gaussian Markov random fields to model the correlation structure embedded in users’ data. The data collection is modeled as a Stackelberg game where the platform decides its reward policy and users decide their noise levels while taking into account the social coupling among users. For the reward policy design, we first establish the relationship between users’ Nash equilibrium and the payment mechanism, and then optimize the platform's accuracy under a budget constraint. Further, since the noise levels are users’ private information, they may use falsified noise levels to achieve higher payoffs, which in turn impairs the crowdsensing performance. It turns out that with the insight into the correlation structure among users’ data, the information asymmetry can be overcome based on peer prediction. We revisit the payment mechanism to guarantee dominant truthfulness of each user's strategy. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed mechanism. [ABSTRACT FROM AUTHOR]

As mobile and wireless technologies continually improve and become more affordable, reliable, powerful and user-friendly, Computer Assisted Personal Interviewing (CAPI) is expected to become the one of the effective approaches in field-based census data collection even in those countries where access to infrastructure for information and communication technologies is limited. African countries are at such a case. The introduction of electronic data capture into the business cycle of the census provides cost and time savings, and also allows users to take advantage of added features that can be programmed into mobile devices or linked to the data collection process. These features include, among others, integrated maps and Global Positioning System (GPS) and real time monitoring of fieldwork. This paper shows how the Italian National Institute of Statistics (Istat) has supported the Ethiopia Central Statistics Agency (CSA) in designing and implementing such a monitoring system, which has been fully integrated with the census data collection process managed by CSPro, the public domain software package developed by the U.S. Census Bureau (USCB). The proposed architecture is generalised and provides a simple solution for monitoring electronic data collection operations, particularly in cases where the technical and financial resources to implement such a system from the ground up are lacking. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to identify details of technological, organizational and people (TOP) factors affecting maintenance technicians' use of computerized maintenance management systems (CMMS) in manual collection of asset data. Design/methodology/approach: In addition to TOP factor details, results from six case studies in Finland, India and the Caribbean are presented. Interviews and observations clarify the role of TOP factors in CMMS use in industrial maintenance. Findings: In total, 17 detailed TOP factors are identified and criteria for analyzing CMMS contexts with them are defined. Analyzing the cases with these factors reveals that technicians who collect good quality data have received good training and instructions for the CMMS, are competent, and understand how manually collected data benefits them in their own work. However, even these sites struggle with the usability of the CMMS. Research limitations/implications: The 17 TOP factors and the criteria for CMMS evaluation extend understanding on context and usability in manual data collection. Case study method does not imply the relative importance of the TOP factors, which calls for future research using quantitative methods. Practical implications: Management can use the criteria to analyze the context of manual data collection for improvements, e.g., in CMMS usability. Originality/value: Insights from industrial environments and a new way of studying contextual factors of CMMS use are presented. The results extend a data quality research framework with details to manual data collection and define the TOP factors in CMMS context. [ABSTRACT FROM AUTHOR]