Search

This study aims to determine the composition and the weight of individual single-use items (SUIs) generated in the selected fast-food restaurants (FFRs) in Ho Chi Minh City (HCMC), Vietnam. A semi-structured questionnaire was used to collect data of SUIs consumed per day from 126 FFRs covering six popular fast food companies (FFCs). At the same time, waste from 30 FFRs was collected, and its composition and weight were determined. Consequently, the amount and composition of the waste varied among the studied FFCs and is dependent on the food menu, the number of franchises for each FFC, customer number, size, and the type of SUIs used at these restaurants. Total waste collected across the six FFCs was 6 t.d-1 and was equivalent to 1560 t.yr-1 in HCMC. Of which, singleuse plastic items (SUPIs) waste and single-use paper items (SUPaIs) waste accounted for 39% and 28%, respectively. The total weight of unnecessary SUPIs (condiment containers, straws, and forks) generated was about 44 t.yr-1. The results suggest that the necessity of standardizing the type and size of the SUIs used at the FFRs, phasing out the use of unnecessary SUPIs, improving the local waste management practices through material recovery and recycling. [ABSTRACT FROM AUTHOR]

Isotope ratio mass spectrometry (IRMS) using carbon isotopes has previously been shown to be a robust and discriminating technique for the comparison of document papers. This study aims to examine the inter and intra sample variability for oxygen isotopes measured in standard 80gsm white document papers, to inform the comparison of document papers in forensic casework. 123 paper samples collected from Australia and New Zealand over a 24-month period were measured for their bulk oxygen isotopic abundance and were found to sit within a range of 15‰. A homogeneity study was undertaken which included examining the variability of samples at the sheet, ream and brand source levels. The results of this study were used to construct guidelines for sample comparison and as such, 95% confidence intervals were observed to be inappropriate for use given the high intra sample variability. Instead, a 1.4‰ discrimination range (0.7‰ either side of the measured value) was defined for use as a benchmark for discrimination when samples were measured in the same sequence. Utilising this value, 82% of the samples could be discriminated using a paired comparison, demonstrating a strong potential for use within forensic casework. [ABSTRACT FROM AUTHOR]

The expansion of available data for research has transformed empirical labor economics over the past generation. This paper briefly highlights some of the changes and describes a few examples of papers that illustrate the advances. It also documents the changing ways data have been used in the Journal of Labor Economics over the past 30 years, including a trend toward a higher fraction of papers using any data and, among those papers using any data, a higher fraction using nonpublic data, a higher fraction using international data, and more frequent use of multiple data sources. Finally, this paper describes work that came out of the recent Princeton Data Improvement Initiative—a program that considers and furthers improved data collection. [ABSTRACT FROM AUTHOR]

Industry 4 (I4) was a revolutionary new stage for technological progress in manufacturing which promised a new level of interconnectedness between a diverse range of technologies. Sensors, as a point technology, play an important role in these developments, facilitating human–machine interaction and enabling data collection for system-level technologies. Concerns for human labour working in I4 environments (e.g., health and safety, data generation and extraction) are acknowledged by Industry 5 (I5), an update of I4 which promises greater attention to human–machine relations through a values-driven approach to collaboration and co-design. This article explores how engineering experts integrate values promoted by policy-makers into both their thinking about the human in their work and in their writing. This paper demonstrates a novel interdisciplinary approach in which an awareness of different disciplinary epistemic values associated with humans and work guides a systematic literature review and interpretive coding of practice-focussed engineering papers. Findings demonstrate evidence of an I5 human-centric approach: a high value for employees as "end-users" of innovative systems in manufacturing; and an increase in output addressing human activity in modelling and the technologies available to address this concern. However, epistemic publishing practices show that efforts to increase the effectiveness of manufacturing systems often neglect worker voice. [ABSTRACT FROM AUTHOR]

This study was motivated by earlier observations. It is a systematic examination of the adequacy of reporting of information (metadata) necessary to understand x-ray photoelectron spectroscopy (XPS) data collection and data analysis in the scientific literature. The information for this study was obtained from papers published in three high-quality journals over a six-month period in 2019 and throughout 2021. Each paper was evaluated to determine whether the authors had reported (percentages of the papers properly providing the information are given in parentheses) the spectrometer (66%), fitting software (15%), x-ray source (40%), pass energy (10%), spot size (5%), synthetic peak shapes in fits (10%), backgrounds in fits (10%), whether the XPS data are shown in the main body of the paper or in the supporting information (or both), and whether fitted or unfitted spectra were shown (80% of published spectra are fit). The Shirley background is the most widely used background in XPS peak fitting. The Al Kα source is the most widely used x-ray source for XPS data collection. casaxps is the most widely used fitting program for XPS data analysis. There is good agreement between the results gathered during the two years of our survey. There are some hints the situation may be improving. This study also provides a list of the information/parameters that should be reported when XPS is performed. [ABSTRACT FROM AUTHOR]

The article focuses on legality of metadata collection in the U.S. under the U.S PATRIOT Act. Topics include the legal justification for the investigation of Americans' private telephone records, the data collection program implemented by the U.S. National Security Administration under U.S. President Barack Obama, and if such practices are a violation of privacy.

Objective: The purpose of this study is to perform a scoping review of the literature to evaluate evidence showing the role of reinforcement strategies on student learning in medical education. Methods: coping review design represents a methodology that allows assessment of emerging evidence, as well as a first S step in research development. This scoping review includes research papers describing any teaching learning strategy or educational intervention that used Reinforcement of learning at any time during the learning process. Online databases were used to identify articles in the years 2009-2019, from which 10 publications from Canada and the United States and 6 from other nations were selected. Data collected from the sources were charted with the help of a self-developed form, inclusive of names of authors and year of publication, type of article, country of origin, sample size, objectives, and key findings of study. Results: This review shows that reinforcement strategies still have a high impact on student learning. Reinforcing the taught material in medical education enhances student learning and retention and a strengthening of knowledge, skills and attitudes contributes to enhanced learning throughout the continuum of medical education and plays a vital role in developing internally motivated lifelong learners. Conclusion: Evidence from literature suggests that there is extensive application of reinforcement strategies for enhancing learning in the field of medical education. Reinforcement of taught material has a positive impact on student learning and is an effective approach to medical education. [ABSTRACT FROM AUTHOR]

Background: During the scaling-up of a national Norwegian take-home naloxone (THN) program, data collection methods shifted from paper-based to electronic. The aim of this study was to explore staff preferences towards the shift in data collection.Methods: In January-February 2020, a survey was sent out via email to personnel involved with the THN program (n = 200). The survey included 17 questions, and covered staff demographics, experiences distributing THN, preferences towards data collection (both paper and electronically), and an open response section. Descriptive statistics were performed for the survey results. The open response section was recorded from each questionnaire and was coded into major themes by the authors.Results: In total, 122 staff completed the survey. Of these, 62% had experience with both electronic and paper-based forms, and there was a near unanimous preference towards electronic data collection over paper-based forms. From the free-text responses, staff found the electronic form to be a useful tool for conversation and overdose prevention education, and that the electronic form was easier to manage than the paper forms.Conclusion: The shift towards electronic data collection was necessary for the feasibility of the Norwegian national THN program. This study found that staff not only tolerated the shift, but in most cases preferred this organizational change. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective. [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

Digital Pathology Systems (DPS) are dynamic, image-based computer systems that enable the acquisition, management, and interpretation of pathology information generated from digitized glass slides. This article provides a roadmap for (1) qualification of a whole slide scanner (WSS) during a validation project, (2) validation of software required to generate the whole slide image (WSI), and (3) an introduction to visual digital image evaluation and image analysis. It describes a validation approach that can be utilized when validating a DPS. It is not the intent of this article to provide guidance on when validation of DPS is required. Rather, the article focuses on technical aspects of validation of the WSS system (WSS, IT infrastructure, and associated software) portion of a DPS and covers the processes of setting up the WSS for scanning a glass slide through saving a WSI on a server. Validation of a computerized system, such as a DPS, for use in a regulated nonclinical environment is governed by Code of Federal Regulations (CFR) Title 21 part 11: Electronic Records; Electronic Signature and predicate rules associated with Good Laboratory Practices documents including 21 CFR part 58. Similar regulation and predicate rules apply in the European Union and Japan. [ABSTRACT FROM AUTHOR]

The article explores how registry data can improve outcomes from joint replacement. It discusses the aim to make hip replacement safer and more reliable for patients and to evaluate the performance of surgeons and implants, along with information on the costs of the register and the expense of data analysis.

Crashes involving large trucks have a significant association with increasing fatalities or serious injuries. Identifying the factors affecting large trucks crashes can be helpful in reducing the number of crashes and corresponded consequences. The purpose of this review was to appraise the published papers on factors affecting large truckinvolved crashes. Data were compiled through searching Google Scholar, Scopus, Science Direct, and Web of Science using keywords included "large truck and accident severity", "large truck and accident", "large truck and crash severity", "large truck and crash", "large truck and fatal accident", "large truck and fatal crash", "large truck and injury", and "large truck and collide". The accomplishment process had two steps: excluding unrelated studies and reviewing related ones to select papers meeting the inclusion criteria. According to data collected, 31 papers achieved the inclusion criteria. The results indicated that speed, driver age, location type, collision type, seat belt wearing, light condition, vehicle technical condition, time, road surface condition, and accident type were common factors in the included papers. From the perspective of our study, speed, location type, and light condition seem to be the most frequent factors in crashes involving large trucks. The present study helps to access the studies on factors affecting the large truck-involved crashes. Thereby, the experts can perform measures to decrease the probability and severity of traffic accidents. [ABSTRACT FROM AUTHOR]

Background: Roadside observational studies play a fundamental role in designing evidence-informed strategies to address the pressing global health problem of road traffic injuries. Paper-based data collection has been the standard method for such studies, although digital methods are gaining popularity in all types of primary data collection.Objective: This study aims to understand the reliability, productivity, and efficiency of paper vs digital data collection based on three different road user behaviors: helmet use, seatbelt use, and speeding. It also aims to understand the cost and time efficiency of each method and to evaluate potential trade-offs among reliability, productivity, and efficiency.Methods: A total of 150 observational sessions were conducted simultaneously for each risk factor in Mumbai, India, across two rounds of data collection. We matched the simultaneous digital and paper observation periods by date, time, and location, and compared the reliability by subgroups and the productivity using Pearson correlations (r). We also conducted logistic regressions separately by method to understand how similar results of inferential analyses would be. The time to complete an observation and the time to obtain a complete dataset were also compared, as were the total costs in US dollars for fieldwork, data entry, management, and cleaning.Results: Productivity was higher in paper than digital methods in each round for each risk factor. However, the sample sizes across both methods provided a precision of 0.7 percentage points or smaller. The gap between digital and paper data collection productivity narrowed across rounds, with correlations improving from r=0.27-0.49 to 0.89-0.96. Reliability in risk factor proportions was between 0.61 and 0.99, improving between the two rounds for each risk factor. The results of the logistic regressions were also largely comparable between the two methods. Differences in regression results were largely attributable to small sample sizes in some variable levels or random error in variables where the prevalence of the outcome was similar among variable levels. Although data collectors were able to complete an observation using paper more quickly, the digital dataset was available approximately 9 days sooner. Although fixed costs were higher for digital data collection, variable costs were much lower, resulting in a 7.73% (US $3011/38,947) lower overall cost.Conclusions: Our study did not face trade-offs among time efficiency, cost efficiency, statistical reliability, and descriptive comparability when deciding between digital and paper, as digital data collection proved equivalent or superior on these domains in the context of our project. As trade-offs among cost, timeliness, and comparability-and the relative importance of each-could be unique to every data collection project, researchers should carefully consider the questionnaire complexity, target sample size, implementation plan, cost and logistical constraints, and geographical contexts when making the decision between digital and paper. [ABSTRACT FROM AUTHOR]

Background: Electronic data collection is increasingly being used for discrete choice experiments (DCEs).Objectives: To study whether paper or electronic administration results in measurement effects.Methods: Respondents were drawn from the same sample frame (an Internet panel) and completed a nearly identical DCE survey either online or on paper during the same period. A DCE on preferences for basic health insurance served as a case study. We used panel mixed logit models for the analysis.Results: In total, 898 respondents completed the survey: 533 respondents completed the survey online, whereas 365 respondents returned the paper survey. There were no significant differences with respect to sociodemographic characteristics between the respondents in both samples. The median response time was shorter for the online sample than for the paper sample, and a smaller proportion of respondents from the online sample were satisfied with the number of choice sets. Although some willingness- to-pay estimates were higher for the online sample, the elicited preferences for basic health insurance characteristics were similar between both modes of administration.Conclusions: We find no indication that online surveys yield inferior results compared with paper-based surveys, whereas the price per respondent is lower for online surveys. Researchers might want to include fewer choice sets per respondent when collecting DCE data online. Because our findings are based on a nonrandomized DCE that covers one health domain only, research in other domains is needed to support our findings. [ABSTRACT FROM AUTHOR]

A database of lightning-related fatalities and injuries in Turkey was constructed by collecting data from the Turkish State Meteorological Service, newspaper archives, European Severe Weather Database, and the internet. The database covers January 1930 to June 2014. In total, 742 lightning incidents causing human fatalities and injuries were found. Within these 742 incidents, there were 895 fatalities, 149 serious injuries, and 535 other injuries. Most of the incidents (89%) occurred during April through September, with a peak in May and June (26 and 28%) followed by July (14%). Lightningrelated fatalities and injuries were most frequent in the afternoon. Most of the incidents (86%) occurred in the rural areas, with only 14% in the urban areas. Approximately, two thirds of the victims with known gender were male. Because of the unrepresentativeness of the historical data, determining an average mortality rate over a long period is not possible. Nevertheless, there were 31 fatalities (0.42 per million) in 2012, 26 fatalities (0.35 per million) in 2013, and 25 fatalities (0.34 per million) in 2014 (as of June). There were 36 injuries (0.49 per million) in each of 2012 and 2013, and 62 injuries (0.84 per million) in 2014 (as of June). [ABSTRACT FROM AUTHOR]

Study protocol: the DUALITY trial-a register-based, randomized controlled trial to investigate dual-mobility cups in hip fracture patients. Poor knee-specific and generic patient-reported outcome measure scores at 6 months are associated with early revision knee arthroplasty: a study from the Australian Orthopaedic Association National Joint Replacement Registry. The seemingly large variation in changes in PROM scores across hip and knee arthroplasty registries was partially explained by differences in age, gender, and importantly, the patients' preoperative scores [[8]]. [Extracted from the article]

The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy. [ABSTRACT FROM AUTHOR]

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods. [ABSTRACT FROM AUTHOR]

Robotic grasping is a common operation scenario in industry and agriculture, in which the force sensing function is a significant factor to achieve reliable grasping. Existing force sensing methods of flexible grippers require intelligent materials or force sensors embedded in the flexible gripper, which causes such problems of higher manufacturing requirements and contact surface properties changing. In this paper, a novel rope-driven force sensing flexible gripper is designed based on the fin-shaped gripper structure, which can realize the grasping sensing functions of contact nodes and contact forces without the need for force sensors. Firstly, the rope-driven force sensing flexible gripper is designed, including the driving unit, the transmission part, the gripper unit, and the force sensing unit. The force sensing unit and the gripper unit are connected by rope, and the prototype of the rope-driven force sensing flexible gripper is completed. Secondly, a force sensing algorithm and control system based on finite element method and grasping geometric relationship are designed to realize the rope-driven force sensing flexible gripper grasping control and sensor data acquisition and processing. Finally, the experimental system of the rope-driven force sensing flexible gripper is built, and the grasping experimental tests of objects with different diameters and different contact nodes are carried out to verify the force sensing function of the rope-driven force sensing flexible gripper. The force sensing flexible gripper designed in this paper can provide a new idea for the design and force sensing method of intelligent robotic grasping system in robotic teaching, scientific research, and industrial applications. [ABSTRACT FROM AUTHOR]

Detrital zircon U–Pb chronology database of Precambrian deposits provides a context for the interpretation of the origin and evolution of ancient crust. Here, we tried to summarize the published literature containing detrital zircon U–Pb ages and Hf isotope data of Precambrian deposits in South China and then established a database, which contains details of information, such as reference, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages and εHf(t) values. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, 610 samples with 38,278 U–Pb ages and 8,798 Lu‐Hf isotope data were collected from 136 papers, and these data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. The purpose of the establishment of the dataset is to provide guidance and convenience for the research direction of future generations in South China and to improve the previous studies through the integrated data to avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is lacking in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. [ABSTRACT FROM AUTHOR]

As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency. [ABSTRACT FROM AUTHOR]

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

Grand challenges such as achieving sustainability goals and managing aging infrastructure are creating an unprecedented demand for deconstruction. However, deconstructing aging infrastructure is inherently risky, repetitive, and costly, necessitating effective project planning. Virtual reality (VR) technology offers the potential for planners to improve deconstruction project risk, time, safety, and cost. We propose that planners leverage VR early in the planning phase to compare alternative feasible tools and processes. Planners can create low-fidelity models of various deconstruction process alternatives and collect metrics on their suitability with VR-capable three-dimensional (3D) game engines. We present and formalize a methodology for conducting comparisons of candidate deconstruction processes by modeling candidates in VR, conducting trials, and collecting analytics data from the VR engine. We present a case study that demonstrates our approach to a cutting and waste packing process for nuclear power plant (NPP) decommissioning. As a novel contribution in this paper, we show that VR simulations can efficiently produce detailed insights useful for critically analyzing and comparing deconstruction process alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we study the Greek wiretappings scandal, which was revealed in 2022 and attracted significant attention from the press and citizens. Specifically, we propose a methodology for collecting data and analyzing patterns of online public discussions on Twitter. We apply our methodology to the Greek wiretappings use case and present findings related to the evolution of the discussion over time, its polarization, and the role of the Media. The methodology can be of wider use and replicated to other topics. Finally, we publicly provide an open dataset and online resources with the results. [ABSTRACT FROM AUTHOR]

Aims: To describe a reflexive account of a mental health nurse as researcher undertaking a mixed methods study as part of a doctoral degree, with women in a secure forensic hospital. Design: A discursive paper that draws upon the primary author's experience of dual roles as a mental health nurse and a novice researcher, and relevant literature pertaining to positionality and reflexivity in the context of conducting mixed methods research. Methods: A mixed methods study was conducted within a secure forensic hospital in Australia, to identify factors associated with the frequency and duration of seclusion for women and, to explore their experiences of seclusion. Notes and a reflective diary were used to demonstrate the reflexive approach and strategies used throughout the study design and data collection stages of the research. Results: Women in the secure forensic hospital setting often have complex histories and experiences and are considered a vulnerable group, which can add additional challenges when undertaking research with this population. As a doctoral student with clinical experience in mental health undertaking research in the forensic hospital setting with women, positionality and the connection with reflexivity requires exploration to understand self and the influence on research. This understanding along with identified strategies to enhance reflexivity, supports the management of the dual nurse as researcher roles to enhance all stages of the research process. Conclusion: Doctoral nursing candidates undertaking clinical research in their area of clinical practice and at the location of previous employment, may experience challenges in relation to identity and the dichotomy of roles. Further challenges can be posed when research involves complex populations and/or controversial practices. Support to balance role conflict and maintain reflexivity is critical to understanding the role of self in research and to enhance credibility. Implications for the Profession and/or Clinical Practice: Women are a complex, yet minority population in forensic mental health settings, however the numbers of women being admitted to services is increasing. Future research may consider the use of the strategies explored here to enhance the reflexive process, and support future researchers in this field to conduct research that aims to support an often‐misheard population to better support health outcomes. Patient or Public Involvement: There was no patient or public contribution to this paper, however the study in which this paper is based on, is grounded in the experience of patients (women). [ABSTRACT FROM AUTHOR]

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

Background: Hip fractures are a common and debilitating condition posing not only a huge health care but also socioeconomic burden. Surgical management for a neck of femur fracture is typically with arthroplasty in the form of total hip arthroplasty or hip hemiarthroplasty. Serial radiographs are typically performed routinely as part of follow-up to look for complications, although their clinical utility in asymptomatic patients is yet to be validated. Our paper therefore aims to review the utility and necessity of radiographic follow-up following arthroplasty for NOF fractures. Materials and Methods: Patients who underwent operative management for acute fragility neck of femur fractures in the year from 1st January 2018 to 31st December 2018 at the author's institution were identified. All patients who underwent surgery, and had at least one pre and one post-operative plain film radiograph of the affected hip were included in this study. Exclusion criteria included patients who had undergone surgery for chronic fractures, avascular necrosis of the femoral head, mortality within 1 year, peri-prosthetic fractures, pathological fractures from metastases, had concomitant injuries, or had inaccessible or incomplete records. Clinical records were assessed for the number of visits, an abnormal presenting history or clinical examination, as well as changes in management of the patient. The number and type of radiographs were also assessed, and each radiograph analyzed for abnormal findings. Results: A total of 157 patients were included in our study with a mean age of 79.5 at the time of surgery, and a mean follow up of 17.3 months. Data was collected from 626 clinical visits and a total of 973 radiographs. The 3 abnormal radiographic series identified with a corresponding normal consult did not result in a change of management for the patient. A negative change in management was only observed in 1 patient with an abnormal consult and a corresponding normal radiograph. Conclusion: Post-operative complications following arthroplasty for NOF fractures are likely to result in a symptomatic presentation of the patient. Routine radiographic follow-up provides limited utility in asymptomatic patients and should only be performed if clinically indicated. [ABSTRACT FROM AUTHOR]

Background: Collection of detailed dietary data is labor intensive and expensive, harmonization of existing data sets has been proposed as an effective tool for research questions in which individual studies are underpowered. Methods: In this paper, we describe the methodology used to retrospectively harmonize nutritional data from multiple sources, based on the individual participant data of all available studies, which collected nutritional data in Israel between 1963 and 2014. This collaboration was established in order to study the association of red and processed meat with colorectal cancer. Two types of nutritional questionnaires, the Food Frequency Questionnaires (FFQ) and the 24-h dietary recall (24HR recall), and different food composition tables, were used by the participating studies. The main exposure of interest included type of meat (total meat, red meat, and poultry) and level of processing. Results: A total of 29,560 Israeli men and women were enrolled. In studies using FFQ,the weighted mean intakes of total, red, processed meat, and poultry were 95, 27, 37 and 58 gr/day and 92, 25, 10, and 66 gr/day in studies using 24HR recall, respectively.. Despite several methodological challenges, we successfully harmonized nutritional data from the different studies. Conclusions: This paper emphasizes the significance and feasibility of harmonization of previously collected nutritional data, offering an opportunity to examine associations between a range of dietary exposures and the outcome of interest, while minimizing costs and time in epidemiological studies. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors' operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. [ABSTRACT FROM AUTHOR]

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]

In order to understand the application of iot Big data information acquisition for heating control systems, a research on iot Big data information acquisition for thermal storage heating control systems is proposed. This paper first analyzes the shortcomings of the traditional heating system, the heating data is scattered and difficult to collect. The IoT technology is applied to the heating platform through data acquisition, data visualization, data mining, data management and other aspects. Secondly, the application of the IoT in heating system data acquisition is studied, including the types of heating data collected, data acquisition process, data acquisition standards, etc. Finally, this paper takes the energy-saving transformation of 26 boilers in a heating enterprise as an example. The research results show that the boiler efficiency obtained in real time by using the online monitoring system of this project is 95.36%, and the reverse balance boiler efficiency in the paper report of the special inspection office is 94.88%, with a deviation of 1.48%. The application of the IoT technology can obtain the boiler indicators in real time and effectively, and can realize the comprehensive analysis of the monitoring data, and realize the evaluation of the energy efficiency operation state of the gas boiler and the fault diagnosis and early warning. The IoT technology is an effective means of fine energy-saving management in heating enterprises. [ABSTRACT FROM AUTHOR]

Combining online and offline data collection during six months of ethnographic fieldwork in Southern Mozambique in early 2021, this paper explores how media and its everyday use in the Global South are impacting on, and are in turn being transformed by China. It specifically looks into the creative and personalised ways in which Chinese-made garments are sold and promoted in Mozambique via the messenger service WhatsApp. Examining how Mozambican and West African traders use WhatsApp groups and status updates to mediate the fashionability of these Chinese goods, this paper shows that the consequences of Chinese interactions with the Global South on the global media landscape transcend the mere spread of Chinese hardware and software. Going beyond dominant, state-centred narratives of Chinese influence abroad, this paper demonstrates the key role of individuals and their agency in shaping the digital impact of Global China. By doing so, it also highlights the importance of analysing media practices in their specific local contexts. [ABSTRACT FROM AUTHOR]

Background: As a result of the ten years (2012-2022) work under the critical revision of the genera of Reticulariaceae, a set of papers was published. Collection data of hundreds of specimens, used as a material for these studies, were provided as supplements of corresponding papers, but remained unpublished in biodiversity databases. New information: Here, we represent an occurrence dataset "Barcoded Reticulariaceae of the World", published in GBIF. It includes data on 523 myxomycete collections (including 36 types) gathered from five continents and spanning 24 countries. The dataset encompasses 43 distinct species and one subspecies of myxomycetes, including rare, endemic, and recently-described taxa. Species included to the database mainly belong to the genera Alwisia, Lycogala, Reticularia, Siphoptychium, Thecotubifera and Tubifera (Reticulariaceae), but as well Lindbladia and Licaethalium (Cribrariaceae). Nearly all of the research material, with the exception of several old collections, underwent molecular barcoding, primarily involving the 18S rDNA gene, but also the elongation factor 1α gene and mitochondrial cytochrome oxidase subunit I gene. For those sequences that are stored in the NCBI GenBank, accession numbers are provided in the dataset. Newly-described species make up a significant part of the studied herbarium collections; many of them can be characterised as common for their region. A particularly high level of taxonomic novelty is observed in Australia, which may be explained by the endemism of the local myxomycete biota. [ABSTRACT FROM AUTHOR]

Background: The development of a set of questions is a central element of examination development, with the validity of an examination resting to a large extent on the quality of the questions that it comprises. This paper reports on the methods and findings of a project that explores how educational examination question writers engage in the practice of question setting. Although the practice of examination question writing is ubiquitous, limited research has been carried out on how writers actually draft individual examination questions when writing complete examination papers. Purpose: The main aim of this study was to understand more deeply the process of examination question writing. Sample: The study involved 12 experienced educational examination question writers. Design and methods: The first data collection phase involved observing writers whilst they wrote the first draft of a question paper (using a 'think aloud' process). Following the observation, the writers took part in an hour-long interview. This led to the construction of a model of question writing. Phase 2 of the data collection process involved validating the proposed question writing model with six additional question writers by means of telephone interviews. Analysis and findings: A model was developed that outlined the key stages of writing that all of the writers moved through in the completion of their question writing task. The question writing model situates the process of writing individual questions in relation to the broader cognitive tasks required in writing a complete paper. It also situates the writing process within the social dimensions of question writing. Conclusions: Iteration in the writing process appears to have an important function. These iterations represent a form of decision-making that is common to the examination question writing culture and appears to be logical and rule bound. Whilst the recognition of iterative behaviour reveals a highly personalised element of question writing, it also reveals a common expectation that the ideas across a subject area are highly inter-related, and that an examination paper should reflect this. [ABSTRACT FROM AUTHOR]

B Correction: BMC Medical Research Methodology 23, 10 (2023) b B https://doi.org/10.1186/s12874-023-01833-8 b Following publication of the original article [[1]], the author requested to correct the word "primiparous" to "multiparous" in the Abstract results and under the paragraph "Representativeness of respondents" in the main results section. Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push-to-web surveys and a concurrent social media survey. [Extracted from the article]

In this activity, students are given the opportunity to combine skills in math and geometry for a biology lesson in the cell cycle. Students utilize the data they collect and analyze from an online onion-root-tip activity to create a paper-plate time clock representing a 24-hour cell cycle. By dividing the paper plate into appropriate phases of the cell's cycle on the basis of the data they collected, they can visualize the data, hypothesize, and predict how the time spent in each of the phases in the cycle might change in abnormal situations, such as in cancer or other diseases that affect control of the cell cycle. [ABSTRACT FROM AUTHOR]

Background: Electronic applications are increasingly being used in hospitals for numerous purposes. Objective: Our aim was to assess differences in the characteristics of patients who choose paper versus electronic questionnaires and to evaluate the data quality of both approaches. Methods: Between October 2012 and June 2013, 136 patients participated in a study on diagnosis-induced stress and anxiety. Patients were asked to fill out questionnaires at six different moments during the diagnostic phase. They were given the opportunity to fill out the questionnaires on paper or electronically (a combination of tablet and Web-based questionnaires). Demographic characteristics and completeness of returned data were compared between groups. Results: Nearly two-thirds of patients (88/136, 64.7%) chose to fill out the questionnaires on paper, and just over a third (48/136, 35.3%) preferred the electronic option. Patients choosing electronic questionnaires were significantly younger (mean 47.3 years vs mean 53.5 in the paper group, P=.01) and higher educated (P=.004). There was significantly more missing information (ie, at least one question not answered) in the paper group during the diagnostic day compared to the electronic group (using a tablet) (28/88 vs 1/48, P<.001). However, in the week after the diagnostic day, missing information was significantly higher in the electronic group (Web-based questionnaires) compared to the paper group (41/48 vs 38/88, P<.001). Conclusions: Younger patients and patients with a higher level of education have a preference towards filling out questionnaires electronically. In the hospital, a tablet is an excellent medium for patients to fill out questionnaires with very little missing information. However, for filling out questionnaires at home, paper questionnaires resulted in a better response than Web-based questionnaires. [ABSTRACT FROM AUTHOR]

In this paper, we take the lessons learned from designing a survey and collecting validity evidence and prepare to administer the survey for research. We focus specifically on how researchers can reach individuals in the target population, methods of contact and engagement, evidence‐informed factors that enhance participation, and recommendations for follow‐up with nonrespondents. We also discuss the challenges of survey administration and provide guidance for navigating low response rates. Surveys are a common tool used to evaluate educational initiatives and collect data for all types of research. However, many clinician educators conducting survey‐based evaluation and research may struggle to efficiently administer their survey. As a result, they often struggle to obtain appropriate response rates and thus may have difficulty publishing their survey results. Previous papers in this series focused on the initial steps of survey development and validation, but it is equally important to understand how best to administer your survey to obtain meaningful responses from a representative sample. [ABSTRACT FROM AUTHOR]

Background: Survey response rates have been declining over the past decade. The more widespread use of the Internet and Web-based technologies among potential health survey participants suggests that Web-based questionnaires may be an alternative to paper questionnaires in future epidemiological studies. Objective: To compare response rates in a population of parents by using 4 different modes of data collection for a questionnaire survey of which 1 involved a nonmonetary incentive. Methods: A random sample of 3148 parents of Danish children aged 2-17 years were invited to participate in the Danish part of the NordChild 2011 survey on their children's health and welfare. NordChild was conducted in 1984 and 1996 in collaboration with Finland, Iceland, Norway, and Sweden using mailed paper questionnaires only. In 2011, all countries used conventional paper versions only except Denmark where the parents were randomized into 4 groups: (1) 789 received a paper questionnaire only (paper), (2) 786 received the paper questionnaire and a log-in code to the Web-based questionnaire (paper/Web), (3) 787 received a log-in code to the Web-based questionnaire (Web), and (4) 786 received log-in details to the Web-based questionnaire and were given an incentive consisting of a chance to win a tablet computer (Web/tablet). In connection with the first reminder, the nonresponders in the paper, paper/Web, and Web groups were also present with the opportunity to win a tablet computer as a means of motivation. Descriptive analysis was performed using chi-square tests. Odds ratios were used to estimate differences in response rates between the 4 modes. Results: In 2011, 1704 of 3148 (54.13%) respondents answered the Danish questionnaire. The highest response rate was with the paper mode (n=443, 56.2%). The other groups had similar response rates: paper/Web (n=422, 53.7%), Web (n=420, 53.4%), and Web/tablet (n=419, 53.3%) modes. Compared to the paper mode, the odds for response rate in the paper/Web decreased by 9% (OR 0.91, 95% CI 0.74-1.10) and by 11% (OR 0.89, 95% CI 0.73-1.09) in the Web and Web/tablet modes. The total number of responders for NordChild declined from 10,291 of 15,339 (67.09%) in 1984 and 10,667 of 15,254 (69.93%) in 1996 to 7805 of 15,945 (48.95%) in 2011 with similar declines in all 5 Nordic countries. Conclusions: Web-based questionnaires could replace traditional paper questionnaires with minor effects on response rates and lower costs. The increasing effect on the response rate on participants replying for a nonmonetary incentive could only be estimated within the 2 Web-based questionnaire modes before the first reminder. Alternative platforms to reach higher participation rates in population surveys should reflect the development of electronic devices and the ways in which the population primarily accesses the Internet. [ABSTRACT FROM AUTHOR]