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BACKGROUND: Data dashboards have become more widely used for the public communication of health-related data, including in maternal health. OBJECTIVE: We aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States. METHODS: Through systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience. RESULTS: Most reviewed dashboards reported state-level data (58/80, 72%) and were hosted on a state health department website (48/80, 60%). Most dashboards reported data from only 1 (33/80, 41%) or 2 (23/80, 29%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12%) and severe maternal morbidity rate (12/80, 15%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81%), geography (65/80, 81%), and race or ethnicity (55/80, 69%). Among dashboards that identified their audience (30/80, 38%), legislators or policy makers and public health agencies or organizations were the most common audiences. CONCLUSIONS: While maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design.

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This paper focuses on the characterization of radio propagation, and data communication in a marine environment. More specifically, we consider signal propagation when three different sub-gigahertz industrial, scientific, and medical (ISM) bands, i.e., 169 MHz, 434 MHz, and 868 MHz, are used. The main focus of the paper is to evaluate the path loss (PL), i.e., the power loss that a propagation radio wave would experience when communication occurs between a sail boat and a buoy. We describe the measurement results obtained performing three different radio power measurement campaigns, at the three different aforementioned ISM sub-gigahertz bands. We also want to correlate the radio propagation quality with the weather conditions present in the measurement areas. The obtained results show that higher distances are achieved by transmitting at lower frequencies, i.e., 169 MHz, and, on average, the propagation is directly dependent from the dew point index.

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In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.

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Piece selection policy in dynamic P2P networks play crucial role and avoid the last piece problem. BitTorrent uses rarest-first piece selection mechanism to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. The problem of piece section is multiple objectives. A novel fuzzy programming approach is introduced in this article to solve the multiple objectives piece selection problem in P2P network, in which some of the factors are fuzzy in nature. Piece selection problem has been prepared as a fuzzy mixed integer goal programming piece selection problem that includes three primary goals such as minimizing the download cost, time, maximizing speed and useful information transmission subject to realistic constraints regarding peer's demand, capacity and dynamicity. The proposed approach has the ability to handle practical situations in a fuzzy environment and offers a better decision tool to each peer to select optimal pieces to download from other peers in dynamic P2P network. Extensive simulations are carried out to demonstrate the effectiveness of the proposed model. It is proved that proposed system outperforms existing with respect to download cost, time and meaningful exchange of useful information.

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Scientific publications in the life sciences regularly include image data to display and communicate revelations about cellular structure and function. In 2016, a set of guiding principles known as the 'FAIR Data Principles' were put forward to ensure that research data are findable, accessible, interoperable and reproducible. However, challenges still persist regarding the quality, accessibility and interpretability of image data, and how to effectively communicate microscopy data in figures. This Perspective article details a community-driven initiative that aims to promote the accurate and understandable depiction of light microscopy data in publications. The initiative underscores the crucial role of global and diverse scientific communities in advancing the standards in the field of biological images. Additionally, the perspective delves into the historical context of scientific images, in the hope that this look into our past can help ongoing community efforts move forward.

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In China, several problems were common in the telemedicine systems, such as the poor network stability and difficult interconnection. A new telemedicine system jointly driven by multinetwork integration and remote control has been designed to address these problems. A multilink aggregation algorithm and an overlay network for telemedicine system (ONTMS) were developed to improve network stability, and a non-intervention remote control method was designed for Internet of Things (IoT) devices/systems. The authors monitored the network parameters, and distributed the questionnaire to participants, for evaluating the telemedicine system and services. Under a detection bandwidth of 8 Mbps, the aggregation parameters of Unicom 4G, Telecom 4G, and China Mobile 4G were optimal, with an uplink bandwidth, delay, and packet loss ratio (PLR) of 7.93 Mbps, 58.80 ms, and 0.06%, respectively. These parameters were significantly superior to those of China Mobile 4G, the best single network (p < 0.001). Through the ONTMS, the mean round-trip transporting delay from Beijing to Sanya was 76 ms, and the PLR was 0 at vast majority of time. A total of 1988 participants, including 1920 patients and 68 doctors, completed the questionnaires. More than 97% of participants felt that the audio and video transmission and remote control were fluent and convenient. 96% of patients rated the telemedicine services with scores of 4 or 5. This system has shown robust network property and excellent interaction ability, and satisfied the needs of patients and doctors.

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This study demonstrates the implementation of the Hamming code using DNA-based nanostructures for error detection and correction in communication systems. The designed DNA nanostructures conduct logical operations to compute check codes and identify and correct erroneous data based on fluorescence signals. The execution of intricate DNA logic operations requires individuals with specialized training. By interpretation of the fluorescence signals generated by the DNA nanostructures, binary language can be extracted, effectively protecting data security. The findings highlight the potential of DNA as a versatile platform for reliable data transmission.

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With the rapid development of digital transformation, paper forms are digitalized as electronic forms (e-Forms). Existing data can be applied in predictive maintenance (PdM) for the enabling of intelligentization and automation manufacturing. This study aims to enhance the utilization of collected e-Form data though machine learning approaches and cloud computing to predict and provide maintenance actions. The ensemble learning approach (ELA) requires less computation time and has a simple hardware requirement; it is suitable for processing e-form data with specific attributes. This study proposed an improved ELA to predict the defective class of product data from a manufacturing site's work order form. This study proposed the resource dispatching approach to arrange data with the corresponding emailing resource for automatic notification. This study's novelty is the integration of cloud computing and an improved ELA for PdM to assist the textile product manufacturing process. The data analytics results show that the improved ensemble learning algorithm has over 98% accuracy and precision for defective product prediction. The validation results of the dispatching approach show that data can be correctly transmitted in a timely manner to the corresponding resource, along with a notification being sent to users.

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Chronic studies in the fasting and fed states of conscious subjects are fundamental for understanding the pathophysiological significance of functional gastrointestinal (GI) disorders and motility dysfunctions. To study the electrophysiology of the GI tract in the long term, the development of gastric implants is essential. This paper presents the development of an implantable system capable of monitoring the bioelectrical activity of the gastric system and modulating the activity in freely behaving rodents. The system consists of a miniature-sized implantable unit (IU), a stationary unit (SU) that communicates with the IU over a 2.4 GHz far-field radio frequency (RF) bidirectional link, and a charging unit (CU) that establishes an inductive 13.56 MHz near-field communication (NFC) with the IU, implementing an adaptive wireless power transfer (WPT). The CU can generate an adjustable power between +20 dBm and +30 dBm, and, in the presence of body movements and stomach motility, can deliver a constant rectified voltage to the IU. The live subject's exposure to the electromagnetic WPT in the developed system complies with the RF energy absorption restrictions for health and safety concerns. The system can be utilized to investigate the relationship between functional GI disorders and dysrhythmias in the gastric bioelectrical activity and study the potential of electroceutical therapies for motility dysfunctions in clinical settings.

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The purpose of this study was to identify the most vulnerable group among older Korean adults regarding information literacy. Once that was identified, the study aimed to provide basic data for developing strategies to improve information literacy by investigating the factors that influence the ability to utilize the Data Communication Equipment (DCE). The subjects included 10,073 older adults from the 10,299 participants of the 2017 Korean National Survey of Older Adults. The mean age of the older people was 74.06 years from a range of 65 to 106 years old. This study excluded the 216 individuals who did not complete the survey. The data were analyzed using the SPSS 18.0 program. A univariate analysis was performed to identify the most vulnerable group with regard to DCE competence. To investigate the factors that influence DCE competence, a logistic regression analysis was performed on the significant variables in the univariate analysis, while the nominal variables were treated as dummies. Senior citizens in Korea were less able to utilize DCE when they had higher ages, lower education levels, were women, lived alone, lower incomes, decreased sensory function, decreased cognitive function, negative value of learning, no lifelong learning, and smaller social networks. The factors influencing DCE competence in older adults were as follows: age, education level, income level, health status, cognitive function, social networks, lifelong learning, and the value of learning. For DCE competence in older adults to be effectively improved, adequate support must be provided to the vulnerable group. Furthermore, support for personalized DCE utilization seems essential and should consider a person's age, education level, income, health status, cognitive function, social networks, lifelong learning and the value of learning.

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Online communication with participants, including online recruitment, electronic informed consent, and data communication, is one of the fields to which information and communication technology (ICT) has been applied in medical research. Online communication provides various benefits, especially for genome research and rare disease research. However, ethical challenges that are derived from or exacerbated by online communication need to be addressed. Here, we present an overview of such ethical issues and provide practical guidance for the ethical implementation of ICT. We specify the ethical issues in the context of using online communication for medical research by an analysis based on the eight ethical principles for clinical research. Informed by this ethical context, we then develop a novel framework for the governance of medical research involving ICT, which consists of eight categories: five research processes (ie, design of research, recruitment, informed consent, data communication, and dissemination and return of results) and three overarching perspectives related to multiple processes of research (ie, access to research and online dialog, community involvement, and independent review). Finally, we present a practical guidance chart for researchers, patient partners, independent reviewers, and funding agencies. We believe that our study will contribute to the ethical implementation of online communication in medical research.

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The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

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Biosensors devices have attracted the attention of many researchers across the world. They have the capability to solve a large number of analytical problems and challenges. They are future ubiquitous devices for disease diagnosis, monitoring, treatment and health management. This review presents an overview of the biosensors field, highlighting the current research and development of bio-integrated and implanted biosensors. These devices are micro- and nano-fabricated, according to numerous techniques that are adapted in order to offer a suitable mechanical match of the biosensor to the surrounding tissue, and therefore decrease the body's biological response. For this, most of the skin-integrated and implanted biosensors use a polymer layer as a versatile and flexible structural support, combined with a functional/active material, to generate, transmit and process the obtained signal. A few challenging issues of implantable biosensor devices, as well as strategies to overcome them, are also discussed in this review, including biological response, power supply, and data communication.

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We implemented a concurrent triangulation mixed-methods evaluation of an air pollution data report-back to study participants in Chelsea, Massachusetts. We aimed to determine whether the report-back was effective in the following three ways: engagement, understandability, and actionability for the participants. We also evaluated participants' valuation of the report-back information and process. The evaluation involved both qualitative components, such as ethnographic observation, and quantitative components, such as closed-ended questionnaires and demographic data. The participants who engaged in the report-back process were significantly different from those who did not engage both in terms of their demographics, and in their indoor air pollutant concentrations. Participant understanding generally corresponded with the intended meaning of the research team, suggesting successful data communication. Additionally, many of the participants reported that they were inspired to take action in order to reduce their indoor air pollutant exposure as a result of the report-back process and information provided. These results identify areas of improvement for engagement, particularly regarding populations that may have higher exposures. This work outlines a framework with which to contextualize and evaluate the success of engagement with report-back efforts. Such evaluations can allow research teams to assess whether they are providing information that is equitably useful and actionable for all participants.

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The Licklider transmission protocol is a point-to-point communication protocol designed for space links, which commonly involve extreme delays, disruptions, and lossy transmissions. The protocol sends application data in blocks, which in turn are sent in segments. It achieves reliable block delivery through multiple transmission rounds, each one re-sending the segments lost during the previous round. This retransmission process drives protocol performance. We derive exact and approximate methods to find the average number of rounds per block. Then, we estimate the block delivery time and other metrics using this value. We found that the common practice of matching segment lengths to the maximum transfer unit of the link layer may lead to suboptimal performance. The models provide accurate protocol performance prediction, which can help to optimize protocol parameters for specified operating conditions.

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High-resolution (HR) mapping of the gastrointestinal (GI) bioelectrical activity is an emerging method to define the GI dysrhythmias such as gastroparesis and functional dyspepsia. Currently, there is no solution available to conduct HR mapping in long-term studies. We have developed an implantable 64-channel closed-loop near-field communication system for real-time monitoring of gastric electrical activity. The system is composed of an implantable unit (IU), a wearable unit (WU), and a stationary unit (SU) connected to a computer. Simultaneous data telemetry and power transfer between the IU and WU is carried out through a radio-frequency identification (RFID) link operating at 13.56 MHz. Data at the IU are encoded according to a self-clocking differential pulse position algorithm, and load shift keying modulated with only 6.25% duty cycle to be back scattered to the WU over the inductive path. The retrieved data at the WU are then either transmitted to the SU for real-time monitoring through an ISM-band RF transceiver or stored locally on a micro SD memory card. The measurement results demonstrated successful data communication at the rate of 125 kb/s when the distance between the IU and WU is less than 5 cm. The signals recorded in vitro at IU and received by SU were verified by a graphical user interface.

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Voice-based interfaces have become one of the most popular device capabilities, recently being regarded as one flagship user experience of smart consumer devices. However, the lack of common coordination mechanisms might often degrade the user experience, especially when interacting with multiple voice-enabled devices located closely. For example, a hotword or wake-up utterance such as "hi Bixby" or "ok Google" frequently triggers redundant responses by several nearby smartphones. Motivated by the problem of uncoordinated react of voice-enabled devices especially in a multiple device environment, in this paper, we discuss the notion of an ephemeral group of consumer devices in which the member devices and the transient lifetime are implicitly determined by an external event (e.g., hotword detection) without any provisioned group structure, and specifically we concentrate on the time-constrained leader election process in such an ephemeral group. To do so: (i) We first present the sound-based multiple device communication framework, namely tailtag, that leverages the isomorphic capability of consumer devices for the tasks of processing hotword events and transmitting data over sound, and thus renders both the tasks confined to the same room area and enables the spontaneous leader election process in a unstructured group upon a hotword event. (ii) To improve the success rate of the leader election with a given time constraint, we then develop the adaptive messaging scheme especially tailored for sound-based data communication that inherently has low data rate. Our adaptive scheme utilizes an application-specific score that is individually calculated by a member device for each event detection, and employs score-based scheduling by which messages of a high score are scheduled first and so unnecessary message transmission can be suppressed during the election process. (iii) Through experiments, we also demonstrate that, when a hotword is detected by multiple smartphones in a room, the framework with the adaptive messaging scheme enables them to successfully achieve a coordinated response under the given latency bound, yielding an insignificant non-consensus probability, no more than 2%.

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Approximately 6.3 million fractures occur each year in the United States alone. Accurately monitoring the progression of fracture healing is essential to be able to advise patients when it is safe to return to normal activity. The most common method used to confirm and monitor fracture healing is the acquisition of multiple radiographic images over the many months required for healing. This imaging method uses large expensive equipment and exposes patients to high levels of ionizing radiation. In the study described here, we tested another technology for monitoring fracture healing that could minimize the need for multiple radiographic images. We tested a piezoelectric transducer fixed to the surface of a bone that uses electromechanical impedance spectroscopy to measure simulated fractures and transmits the measurement data to an acoustic receiver located externally on the skin surface.

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PURPOSE: Data errors caught late in treatment planning require time to correct, resulting in delays up to 1 week. In this work, we identify causes of data errors in treatment planning and develop a software tool that detects them early in the planning workflow. METHODS: Two categories of errors were studied: data transfer errors and TPS errors. Using root cause analysis, the causes of these errors were determined. This information was incorporated into a software tool which uses ODBC-SQL service to access TPS's Postgres and Mosaiq MSSQL databases for our clinic. The tool then uses a read-only FTP service to scan the TPS unix file system for errors. Detected errors are reviewed by a physicist. Once confirmed, clinicians are notified to correct the error and educated to prevent errors in the future. Time-cost analysis was performed to estimate the time savings of implementing this software clinically. RESULTS: The main errors identified were incorrect patient entry, missing image slice, and incorrect DICOM tag for data transfer errors and incorrect CT-density table application, incorrect image as reference CT, and secondary image imported to incorrect patient for TPS errors. The software has been running automatically since 2015. In 2016, 84 errors were detected with the most frequent errors being incorrect patient entry (35), incorrect CT-density table (17), and missing image slice (16). After clinical interventions to our planning workflow, the number of errors in 2017 decreased to 44. Time savings in 2016 with the software is estimated to be 795 h. This is attributed to catching errors early and eliminating the need to replan cases. CONCLUSIONS: New QA software detects errors during planning, improving the accuracy and efficiency of the planning process. This important QA tool focused our efforts on the data communication processes in our planning workflow that need the most improvement.

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In this manuscript, we describe the process of establishing partnerships for community-based environmental exposure research, the tools and methods implemented for data report-back to community members, and the results of evaluations of these efforts. Data discovery and report-back materials developed by Statistics for Action (SFA) were employed as the framework to communicate the environmental data to community members and workshops. These data communication and research translation efforts are described in detail and evaluated for effectiveness based on feedback provided from community members who attended the workshops. Overall, the methods were mostly effective for the intended data communication.

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