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We present a browser-based App for smartphones that is freely available to end-users for collecting geotagged and oriented photos depicting vegetation biomass and fuel characteristics. Our solution builds on advantages of smartphones, allowing their use as easy sensors to collect data by imaging forest ecosystems. The strength and innovation of the proposed solution is based on the following points: (i) using a low memory footprint App, streaming images and data with as little data-volume and memory as needed; (ii) using JavaScript APIs that can be launched from both a browser or as an installed App, as it applies features such as service workers and Progressive Web App; (iii) storing both image and survey data (geolocation and sensor orientation) internally to the device on an indexed database, and synchronizing the data to a cloud-based server when the smartphone is online and when all other safety tests have been successfully passed. The goal is to achieve properly positioned and oriented photos that can be used as training and testing data for future estimation of the surface fuel types based on automatic segmentation and classification via Machine Learning and Deep Learning.

Vector-borne diseases are among the most burdensome infectious diseases worldwide with high burden to health systems in developing regions in the tropics. For many of these diseases, vector control to reduce human biting rates or arthropod populations remains the primary strategy for prevention. New vector control interventions intended to be marketed through public health channels must be assessed by the World Health Organization for public health value using data generated from large-scale trials integrating epidemiological endpoints of human health impact. Such phase III trials typically follow large numbers of study subjects to meet necessary power requirements for detecting significant differences between treatment arms, thereby generating substantive and complex datasets. Data is often gathered directly in the field, in resource-poor settings, leading to challenges in efficient data reporting and/or quality assurance. With advancing technology, mobile data collection (MDC) systems have been implemented in many studies to overcome these challenges. Here we describe the development and implementation of a MDC system during a randomized-cluster, placebo-controlled clinical trial evaluating the protective efficacy of a spatial repellent intervention in reducing human infection with Aedes-borne viruses (ABV) in the urban setting of Iquitos, Peru, as well as the data management system that supported it. We discuss the benefits, remaining capacity gaps and the key lessons learned from using a MDC system in this context in detail.

The Rapid Assessment of Avoidable Blindness (RAAB) is a population-based cross-sectional survey methodology used to collect data on the prevalence of vision impairment and its causes and eye care service indicators among the population 50 years and older. RAAB has been used for over 20 years with modifications to the protocol over time reflected in changing version numbers; this paper describes the latest version of the methodology–RAAB7. RAAB7 is a collaborative project between the International Centre for Eye Health and Peek Vision with guidance from a steering group of global eye health stakeholders. We have fully digitised RAAB, allowing for fast, accurate and secure data collection. A bespoke Android mobile application automatically synchronises data to a secure Amazon Web Services virtual private cloud when devices are online so users can monitor data collection in real-time. Vision is screened using Peek Vision’s digital visual acuity test for mobile devices and uncorrected, corrected and pinhole visual acuity are collected. An optional module on Disability is available. We have rebuilt the RAAB data repository as the end point of RAAB7’s digital data workflow, including a front-end website to access the past 20 years of RAAB surveys worldwide. This website (https://www.raab.world) hosts open access RAAB data to support the advocacy and research efforts of the global eye health community. Active research sub-projects are finalising three new components in 2024-2025: 1) Near vision screening to address data gaps on near vision impairment and effective refractive error coverage; 2) an optional Health Economics module to assess the affordability of eye care services and productivity losses associated with vision impairment; 3) an optional Health Systems data collection module to support RAAB’s primary aim to inform eye health service planning by supporting users to integrate eye care facility data with population data.

With the bulk availability of mobile sensors, the data collected from them mustn't be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. This study lays the foundation for future explorations, envisioning potential applications in diverse fields, including zoology. By adapting and expanding the proposed methodology, researchers in zoology could harness mobile sensor data to study animal behavior, offering an innovative approach to understanding and monitoring wildlife activities. Such interdisciplinary bridges highlight the versatility of technological advancements, where tools developed for criminal investigations may find unexpected yet valuable applications in the study of zoological phenomena. [ABSTRACT FROM AUTHOR]

Abstract Vector-borne diseases are among the most burdensome infectious diseases worldwide with high burden to health systems in developing regions in the tropics. For many of these diseases, vector control to reduce human biting rates or arthropod populations remains the primary strategy for prevention. New vector control interventions intended to be marketed through public health channels must be assessed by the World Health Organization for public health value using data generated from large-scale trials integrating epidemiological endpoints of human health impact. Such phase III trials typically follow large numbers of study subjects to meet necessary power requirements for detecting significant differences between treatment arms, thereby generating substantive and complex datasets. Data is often gathered directly in the field, in resource-poor settings, leading to challenges in efficient data reporting and/or quality assurance. With advancing technology, mobile data collection (MDC) systems have been implemented in many studies to overcome these challenges. Here we describe the development and implementation of a MDC system during a randomized-cluster, placebo-controlled clinical trial evaluating the protective efficacy of a spatial repellent intervention in reducing human infection with Aedes-borne viruses (ABV) in the urban setting of Iquitos, Peru, as well as the data management system that supported it. We discuss the benefits, remaining capacity gaps and the key lessons learned from using a MDC system in this context in detail.

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space. [ABSTRACT FROM AUTHOR]

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the “best decision” possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and “better” environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.

Elevated electricity consumption during summer heat poses significant challenges for urban energy management. This study employs a novel data-driven bottom-up machine learning approach to predict electricity consumption and identify influential building-related characteristics in Beijing, China. Through mobile survey campaigns, we collected comprehensive electricity consumption data (24,439 records) and detailed building information for 2,087 buildings in 209 neighborhoods. Our models achieved high accuracy, with R2 of 0.80 (RMSE 11.77 kWh, MAE 8.70 kWh) at the household level and R2 of 0.95 (RMSE 4.56 kWh, MAE 3.13 kWh) at the building level. We identified specific building characteristics associated with higher electricity demand, including housing sizes of 86–221 m2, floors 10–25, >3 households per floor per unit, buildings over 19 years old, and higher housing prices. At the neighborhood level, a building density of 21.7%–22.3% and low road network density were linked to higher electricity demand. Notably, summer electricity consumption was 20.08% higher on workweeks and 21.29% higher on weekends compared to autumn. This comprehensive approach provides valuable insights for targeted energy efficiency strategies and urban planning. • Two mobile survey campaigns were conducted to collect data on electricity consumption and building information. • The electricity consumption data contains 24,439 samples. • Total electricity consumption was predicted for 2087 residential buildings in an administrative District, Beijing. • During summer heat, electricity consumption was 20.08% higher on weekdays and 21.29% higher on weekends compared to autumn. [ABSTRACT FROM AUTHOR]

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

Background: An interventional study named Malakit was implemented between April 2018 and March 2020 to address malaria in gold mining areas in French Guiana, in collaboration with Suriname and Brazil. This innovative intervention relied on the distribution of kits for self-diagnosis and self-treatment to gold miners after training by health mediators, referred to in the project as facilitators. Objective: This paper aims to describe the process by which the information system was designed, developed, and implemented to achieve the monitoring and evaluation of the Malakit intervention. Methods: The intervention was implemented in challenging conditions at five cross-border distribution sites, which imposed strong logistical constraints for the design of the information system: isolation in the Amazon rainforest, tropical climate, and lack of reliable electricity supply and internet connection. Additional constraints originated from the interaction of the multicultural players involved in the study. The Malakit information system was developed as a patchwork of existing open-source software, commercial services, and tools developed in-house. Facilitators collected data from participants using Android tablets with ODK (Open Data Kit) Collect. A custom R package and a dashboard web app were developed to retrieve, decrypt, aggregate, monitor, and clean data according to feedback from facilitators and supervision visits on the field. Results: Between April 2018 and March 2020, nine facilitators generated a total of 4863 form records, corresponding to an average of 202 records per month. Facilitators' feedback was essential for adapting and improving mobile data collection and monitoring. Few technical issues were reported. The median duration of data capture was 5 (IQR 3-7) minutes, suggesting that electronic data capture was not taking more time from participants, and it decreased over the course of the study as facilitators become more experienced. The quality of data collected by facilitators was satisfactory, with only 3.03% (147/4849) of form records requiring correction. Conclusions: The development of the information system for the Malakit project was a source of innovation that mirrored the inventiveness of the intervention itself. Our experience confirms that even in a challenging environment, it is possible to produce good-quality data and evaluate a complex health intervention by carefully adapting tools to field constraints and health mediators' experience. [ABSTRACT FROM AUTHOR]

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the ‘freshness’ of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay $B$ , e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than $B$. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio $4\frac {4}{7}$ for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average. [ABSTRACT FROM AUTHOR]

Data collection with mobile elements can improve energy efficiency and balance load distribution in wireless sensor networks (WSNs). However, complex network environments bring about inconvenience of path design. This work addresses the network environment issue, by presenting an objective-variable tour planning (OVTP) strategy for mobile data gathering in partitioned WSNs. Unlike existing studies of connected networks, our work focuses on disjoint networks with connectivity requirement and serves delay-hash applications as well as energy-efficient scenarios respectively. We first design a converging-aware location selection mechanism, which macroscopically converges rendezvous points (RPs) to lay a foundation of a short tour. We then develop a delay-aware path formation mechanism, which constructs a short tour connecting all segments by a new convex hull algorithm and a new genetic operation. In addition, we devise an energy-aware path extension mechanism, which selects appropriate extra RPs according to specific metrics in order to reduce the energy depletion of data transmission. Extensive simulations demonstrate the effectiveness and advantages of the new strategy in terms of path length, energy depletion, and data collection ratio. [ABSTRACT FROM AUTHOR]

This paper examines the potential for using Mobile Data Collection (MDC) as an effective database supported technology to substantially improve forest mensuration practices. Open source Open Data Kit (ODK) procedures and tools were used during a survey campaign that initiated a local forest monitoring process in the Marganai forest (Sardinia). The ODK suite is practical to use and its procedures allow authoring and use of digital survey forms without users needing software development expertise. Form design enables a high degree of customization to be achieved by means of specifying a wide range of data flow control mechanisms. ODK has proved to be a valid tool for data coherence and completeness improvements. As forestry’s contribution to regional Gross Domestic Product has dramatically decreased, forest mensuration practices have been reduced. Meeting the increased need to monitor environmental assets such as forests requires these practices to be re-evaluated. If regional public institutions took an active part in the process of enhancing forest mensuration, by contributing with open database systems acting as repositories and knowledge engines, support for MDC tools like ODK would potentially be a great opportunity to disseminate the use of the system and boost its development.

In traditional wireless sensor networks (WSN), data is transmitted to base stations through multiple-hop. However, multi-hop transmission will cause problems such as energy holes, uneven energy consumption, and unreliable data transmission. Aiming at the defects of traditional networks, this paper proposes a scheme for collaborative data collection using multiple mobile nodes (MN) as sink nodes. In this scenario, an important issue is how to reasonably plan the data collection path of each mobile node under a series of constraints such as energy. This paper studies routing strategy and path planning. Firstly, a dynamic clustering algorithm is used to cluster the randomly arranged sensor nodes, and then a sensor node with higher energy is manually arranged at the virtual cluster center generated by the clustering algorithm as a cluster head node to establish a data collection cluster. Then the monitoring area is divided into several parts according to the number of MN, so that the MN traverses the cluster head nodes in the respective monitoring areas for data collection. At the same time, in order to enable the MN to complete data collection within the energy limit and improve the path balance, a path-based path equalization algorithm (PEABR) is proposed to adjust the path of the MN, further reduce and equalize the path length of the MN to satisfy the constraint conditions, and optimize the path planning scheme. Finally, simulation was carried out by using Matlab simulator, and the simulation experiments which were performed in a laboratory environment verified the feasibility and effectiveness of the algorithm.

Wireless sensor networks enable monitoring remote areas with limited human intervention. However, the network connectivity between sensor nodes and the base station (BS) may not be always possible due to the limited transmission range of the nodes. In such a case, one or more mobile data collectors (MDCs) can be employed to visit nodes for data collection. If multiple MDCs are available, it is desirable to minimize the energy cost of mobility while distributing the cost among the MDCs in a fair manner. Despite availability of various clustering algorithms, there is no single fits all clustering solution when different requirements and performance metrics are considered. Depending on the available wireless communication technology, the MDCs may or may not be required to visit the BS to forward the collected data. This paper considers both cases and suggests clustering algorithms for various performance metrics including the energy consumption and the maximum travel time. [ABSTRACT FROM AUTHOR]

Digital methods provide archaeologists with ever-increasing opportunities to collect more data about the past in new formats. These larger evidentiary datasets, in turn, help us to address questions about the human past with increasing precision. To take full advantage of these opportunities, archaeologists must develop digital literacy skills and learn how to lead digital projects. Here, we describe seven digitally-based projects we have undertaken at the University of Pennsylvania in order to create new tools for archaeological data collection and sharing, as well as to test collaborative models for the digital humanities programming process. In these projects, archaeology students work directly with engineering students. Through this interface, the students from both areas gain valuable transdisciplinary experience while experimenting with new ways to accomplish programming goals and to collect archaeological data. The learning potential for these students was a key motivation for our initiative. Our projects have already led to several websites and digital applications that are available as open source downloads. We present our impressions of this collaborative process with the goal of encouraging other archaeologists to form similar digital humanities partnerships.

The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios. On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments. On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far. Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems. Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios. The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework. In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts.

In order to improve the sensing efficiency of the sensor network, a type of energy-balanced tree-based data collection strategy with mobile Sink (ETDC) is proposed. Based on the minimum cost data gathering tree, several sub-trees with similar scales and small differences in energy consumption are built for data gathering. Moreover, the roots of these sub-trees are regarded as the traversal points, and the moving trajectory of Sink is obtained with the help of these points. In addition, to further reduce energy consumption on communication and the end-to-end delay, roles of some traversal nodes and relay nodes are exchanged. The experimental results show that, compared with the typical mobile Sink based data collection methods such as VGDD and VGDR, ETDC performs well in reducing path length, enhancing the success rate of data transmission, and prolonging the network lifetime.

Mobile Health Data Collection Systems (MHDCS) use electronic forms for data collection in place of paper surveys. Data is entered into the electronic forms by community health workers with smartphones. These systems are used in clinical trials and continuous monitoring of participants. Data collected in MHDCS is diverse with varying sensitivity levels. This therefore calls for the application of different security mechanisms to protect the data. In this paper, a data sensitivity classification model is proposed. It determines the sensitivity levels of form attributes depending on the context and sensitive parameters. The electronic form is the starting point of the data collection process. We augment the form with sensitivity levels and a mapping of security mechanisms to the sensitivity levels is made. Through threat modeling, we identify potential threats and possible mitigations. We demonstrate the feasibility of our approach by prototyping the proposed model into the Open Data Kit tool suite. Tests show that the security mitigations specified during form design are executed once the secure form is loaded on the mobile device during data collection. Our ultimate aim is to facilitate the addition of security requirements into the data collection processes right from the start of the design process. [ABSTRACT FROM AUTHOR]

Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study. Key features of our proposed GEMA architecture include: utilization of widely used smartphones to make GEMA studies practical; alleviation of the burden of activities on participants by designing clients (mobile applications) that are very lightweight and servers that are heavyweight in terms of functionality; utilization of at least one positioning sensor to determine EMA contexts marked with locations; and communication through the Internet. We believe that our proposed GEMA architecture, with the illustrated foundation for GEMA studies in our exemplar study (PMOMS), will help researchers from any field conduct GEMA studies efficiently and effectively. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are expected to be important components in the upcoming wireless communication field, which are increasingly used as data collectors to gather sensory data from Wireless Sensor Networks (WSNs) due to their high mobility, flexible deployment. Since the storage capacity and lifetime of sensors are increasing with the development of science and technology, sensors can store more and more sensing data about the monitoring area. However, due to the energy limitation of UAVs and a large amount of data carried by sensors, we can not collect all data from WSN within the limited time. Therefore, in this paper, we investigate two problems: (1) without the energy limitation of UAV, how to optimize the trajectory of UAV to minimize the sum of traveling time and data transmission time of UAV while guaranteeing the amount of data collected from each sensor reaches to a certain proportion of the original data, which is called the Minimizing Transportation and Communication Latency (MTCL) problem; (2) given the limited budget of UAV, how to find the optimal trajectory of UAV to maximize the minimum ratio of the collected data to the stored data among all sensors, which is called the Maximizing Data Collection Proportion (MDCP) problem. We first prove that both the problems are NP-Complete. Then we study a special case of the MTCL problem, which is called the MTCL-disjoint problem, in which any pair of data collection areas are disjoint, and we propose an approximation algorithm to solve the MTCL-disjoint problem. Based on the MTCL-disjoint problem, we propose an approximation algorithm for the general MTCL problem. Afterward, an approximation algorithm for the MDCP problem is proposed on the basis of the algorithm for the MTCL problem. Finally, we present numerical results in different scenarios to assess the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Participatory Sensing is a process whereby individuals and communities use mobile phones and web services to observe, analyze, and present personal and environmental artifacts, events and experiences. In this technical report we describe a community data campaign that made use of smartphone based participatory sensing for environmental needs assessment. Community organizers defined the content of the participatory sensing campaign. 68 individuals participated over the course of 6 weeks, uploading over 450 mini-surveys, including over 700 images.

Digitalisation has been advocated as a possible strategy to improve the competitiveness of tool, die, and mould-making (TDM) companies in the 21st century. The recent rise of digital technologies, such as Internet of Things devices, now makes digitalisation an achievable reality. This paper focuses on the digitalisation of shop-floor operations in the South African TDM industry through the development of a novel mobile data collection (MDC) tool known as a shop-floor management system (SMS). The developed SMS was deployed to, and validated in, a selected tooling company for various products. The developed system improved the shop-floor’s real-time data collection.

Abstract Background The ePartogram is a tablet-based application developed to improve care for women in labor by addressing documented challenges in partograph use. The application is designed to provide real-time decision support, improve data entry, and increase access to information for appropriate labor management. This study’s primary objective was to evaluate the feasibility and acceptability of ePartogram use in resource-constrained clinical settings. Methods The ePartogram was introduced at three facilities in Zanzibar, Tanzania. Following 3 days of training, skilled birth attendants (SBAs) were observed for 2 weeks using the ePartogram to monitor laboring women. During each observed shift, data collectors used a structured observation form to document SBA comfort, confidence, and ability to use the ePartogram. Results were analyzed by shift. Short interviews, conducted with SBAs (n = 82) after each of their first five ePartogram-monitored labors, detected differences over time. After the observation period, in-depth interviews were conducted (n = 15). A thematic analysis of interview transcripts was completed. Results Observations of 23 SBAs using the ePartogram to monitor 103 women over 84 shifts showed that the majority of SBAs (87–91%) completed each of four fundamental ePartogram tasks—registering a client, entering first and subsequent measurements, and navigating between screens—with ease or increasing ease on their first shift; this increased to 100% by the fifth shift. Nearly all SBAs (93%) demonstrated confidence and all SBAs demonstrated comfort in using the ePartogram by the fifth shift. SBAs expressed positive impressions of the ePartogram and found it efficient and easy to use, beginning with first client use. SBAs noted the helpfulness of auditory reminders (indicating that measurements were due) and visual alerts (signaling abnormal measurements). SBAs expressed confidence in their ability to interpret and act on these reminders and alerts. Conclusions It is feasible and acceptable for SBAs to use the ePartogram to support labor management and care. With structured training and support during initial use, SBAs quickly became competent and confident in ePartogram use. Qualitative findings revealed that SBAs felt the ePartogram improved timeliness of care and supported decision-making. These findings point to the ePartogram’s potential to improve quality of care in resource-constrained labor and delivery settings.

Nowadays, data collection has become an activity inherent in the emergence of any organization. The digital age has enabled the development of mobile data collection apps that are becoming increasingly common around the world. But faced with the growing number of apps offered, Data Managers are often challenged by with the choice of the solution that best suits their case. This study meets this need by providing clear, precise and verified information on each of the selected solutions. The study presents, analyzes and compares four mobile data collection solutions. To achieve an effective comparison, we first chose to collect and select papers on each of the solutions, and then to install and test each of them by executing a data collection process, all the way from the form creation to the visualization of collected data. The comparison presented in this paper was based on technical aspects but also on other important aspects to help users make a good decision. [ABSTRACT FROM AUTHOR]

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The sink hole problem is a critical issue for data collection in wireless sensor networks. Recent research has indicated that the mobility of actuators in wireless sensor actuator networks (WSANs) alleviates the sink hole problem. However, mobile actuator-based data collection may result in unsatisfactory data gathering delay, which is not acceptable for delay-sensitive real time applications. Thus, planning the optimal path for mobile collector is a critical challenge in WSANs to balance between the prolongation of network lifetime and the reduction of the data gathering delay. To address this issue, we propose a mobile collector-based cost balancing algorithm to achieve uniform data gathering delay and energy consumption based on the adaptive region partitioning and dynamic polling point selection. The proposed algorithm uses multiple mobile collectors and partitions the sensing area into several regions that are monitored by the mobile collectors according to the distribution of cost in the area. It also dynamically determines moving trajectory of each mobile collector by selecting the polling points to visit considering the distance to travel and the nodes’ energy consumption. The superiority of the proposed algorithm is validated by theoretical analysis and simulation results. [ABSTRACT FROM AUTHOR]

PURPOSE: The purpose of the study was to assess the feasibility of a mobile data collection app for use in trichiasis surgical audits in the Melfi and Mangalme districts of the Guera region of the Republic of Chad and to perform a cost analysis to determine if the auditing mechanism could be implemented nationally. MATERIALS AND METHODS: Patients who underwent trichiasis surgery 6 months prior and who had follow-up 7–14 days after surgery were included in the study. Each surgeon had a sample of 20% of operated eyelids; nine surgeons with data for ≥20 eyelids were included. A trichiasis recurrence rate of ≥25% suggested that the surgeon needed retraining. Smartphones captured data using the data collection app, which transmitted data to an online server. Direct costs and supervision costs were collated and summed. RESULTS: There were 916 eyelids operated on; 170 patients (269 eyelids, 29% follow-up rate) participated in the audit. Twenty participants (11.8%) had recurrence. The mean recurrence rate among surgeons was 8.3% (standard deviation: 0.07%; range: 0%–17.9%). None had a recurrence rate of ≥25%; thus, no retraining was necessary. The total cost of the audit was US$15,111.25 ($12,882.28 in direct costs and $2,228.97 in supervision costs). CONCLUSIONS: The simple, easy-to-use, and low-cost mobile auditing mechanism is a practical solution for conducting surgical audits in remote and resource-limited settings and is undergoing national scale-up by the Chadian trachoma elimination program. [ABSTRACT FROM AUTHOR]

The Rapid Assessment of Avoidable Blindness (RAAB) is a population-based cross-sectional survey methodology used to collect data on the prevalence of vision impairment and its causes and eye care service indicators among the population 50 years and older. RAAB has been used for over 20 years with modifications to the protocol over time reflected in changing version numbers; this paper describes the latest version of the methodology-RAAB7. RAAB7 is a collaborative project between the International Centre for Eye Health and Peek Vision with guidance from a steering group of global eye health stakeholders. We have fully digitised RAAB, allowing for fast, accurate and secure data collection. A bespoke Android mobile application automatically synchronises data to a secure Amazon Web Services virtual private cloud when devices are online so users can monitor data collection in real-time. Vision is screened using Peek Vision's digital visual acuity test for mobile devices and uncorrected, corrected and pinhole visual acuity are collected. An optional module on Disability is available. We have rebuilt the RAAB data repository as the end point of RAAB7's digital data workflow, including a front-end website to access the past 20 years of RAAB surveys worldwide. This website ( https://www.raab.world) hosts open access RAAB data to support the advocacy and research efforts of the global eye health community. Active research sub-projects are finalising three new components in 2024-2025: 1) Near vision screening to address data gaps on near vision impairment and effective refractive error coverage; 2) an optional Health Economics module to assess the affordability of eye care services and productivity losses associated with vision impairment; 3) an optional Health Systems data collection module to support RAAB's primary aim to inform eye health service planning by supporting users to integrate eye care facility data with population data., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 McCormick I et al.)

Recent years have witnessed the flourish of new promising technologies to prolong the lifetime of wireless sensor networks (WSNs). Employing mobile vehicles for wireless charging and data collection is able to balance the load of WSNs, and meanwhile, provide a reliable energy replenishment for sustainable network operations. Different from traditional charging scheduling policies where sensor nodes passively wait for the arrival of mobile vehicles, a novel dynamic clustering based mobile-to-cluster (M2C) scheme is proposed to optimize the service process for both sensor nodes and the vehicle in an active way. Requiring only local residual energy information, M2C refreshes the cluster-based network topology to ensure no sensor nodes will run out of energy before getting charged. By estimating the energy consumption of being a cluster head, the sensor nodes with residual energy close to the estimation are actively elected as head nodes. More head nodes with low residual energy will be visited, reducing the travel distance along with higher energy efficiency of charging. Furthermore, the convergence and worst-case performance of M2C are theoretically analyzed. Performance evaluation results show that compared with the state-of-the-art schemes, our design reduces the travel distance and charging latency by about 10% and 50%, respectively. [ABSTRACT FROM AUTHOR]

Recently, Wireless sensor networks (WSNs) have become very popular research topics which are applied to many applications. They provide pervasive computing services and techniques in various potential applications for the Internet of Things (IoT). An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism (ACMDGTM) algorithm is proposed which would mitigate the problem of "hot spots" among sensors to enhance the lifetime of networks. The clustering process takes sensors' location and residual energy into consideration to elect suitable cluster heads. Furthermore, one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself. Related experimental results display that the presented method can avoid long distance communicate between sensor nodes. Furthermore, this algorithm reduces energy consumption effectively and improves package delivery rate. [ABSTRACT FROM AUTHOR]

However, we were able to identify three major categories for the contributions: I Experience Sampling, Multi-modal Data Fusion i , and I Reviews and Meta-Analyses i , particularly showing two objectives of the research field of mobile data collection when being used for neuroscience questions. Keywords: mobile data collection; ecological momentary assessments; reviews; meta-analyses; multi-modal data fusion; experience sampling; digital phenotyping; mobile crowdsensing EN mobile data collection ecological momentary assessments reviews meta-analyses multi-modal data fusion experience sampling digital phenotyping mobile crowdsensing 1 3 3 05/27/21 20210525 NES 210525 The Covid-19 pandemic demonstrates both the potential of mobile technology in medicine and the need to exploit this potential (e.g., Zhang et al., [3]). Summary The articles in this special topic provide an overview about the rapidly growing area of mobile data collection in clinical neuroscience. [Extracted from the article]