-
Vaccines May 2024Measles seroprevalence data have potential to be a useful tool for understanding transmission dynamics and for decision making efforts to strengthen immunization... (Review)
Review
BACKGROUND
Measles seroprevalence data have potential to be a useful tool for understanding transmission dynamics and for decision making efforts to strengthen immunization programs. In this study, we conducted a systematized review and bias assessment of all primary data on measles seroprevalence in low- and middle-income countries (as defined by World Bank 2021 income classifications) published from 1962 to 2021.
METHODS
On 9 March 2022, we searched PubMed for all available data. We included studies containing primary data on measles seroprevalence and excluded studies if they were clinical trials or brief reports, from only health-care workers, suspected measles cases, or only vaccinated persons. We extracted all available information on measles seroprevalence, study design, and seroassay protocol. We conducted a bias assessment based on multiple categories and classified each study as having low, moderate, severe, or critical bias. This review was registered with PROSPERO (CRD42022326075).
RESULTS
We identified 221 relevant studies across all World Health Organization regions, decades, and unique age ranges. The overall crude mean seroprevalence across all studies was 78.0% (SD: 19.3%), and the median seroprevalence was 84.0% (IQR: 72.8-91.7%). We classified 80 (36.2%) studies as having severe or critical overall bias. Studies from country-years with lower measles vaccine coverage or higher measles incidence had higher overall bias.
CONCLUSIONS
While many studies have substantial underlying bias, many studies still provide some insights or data that could be used to inform modelling efforts to examine measles dynamics and programmatic decisions to reduce measles susceptibility.
PubMed: 38932314
DOI: 10.3390/vaccines12060585 -
Vaccines May 2024Understanding the motivations and decisions behind COVID-19 vaccine acceptance is crucial for designing targeted public health interventions to address vaccine...
Understanding the motivations and decisions behind COVID-19 vaccine acceptance is crucial for designing targeted public health interventions to address vaccine hesitancy. We conducted a qualitative analysis to explore COVID-19 vaccine acceptance among diverse ethnic subgroups of Black Americans in the United States. This study investigates the 2021-2022 responses of 79 African American, Afro-Caribbean, and African respondents over the age of 18 in Washington State and Texas. Respondents were asked "Do you plan to get the COVID-19 vaccination?" Qualitative responses were analyzed by content category and ethnic subgroup. Of the 79 responses, 60 expressed favorable perceptions, 16 expressed unfavorable perceptions, and 3 expressed neutral perceptions. Dominant categories among participants in favor of the vaccine included personal health (26), concern for health of family/or community members (13), and desire to protect others (11). Among the 42 vaccinated African American respondents, the primary motivation was personal health (20). The 12 unvaccinated African American respondents cited fear of side effects as their dominate motivation. Caribbean respondents cited family or elders as motivation for their decision. African respondents were nearly unanimous in taking the vaccine (13/16), citing trust in health care, protecting friends and family, and personal health as reasons. Community and personal relationships were critical decision-making factors in accepting the COVID-19 vaccine, with African Americans having the strongest hesitancy.
PubMed: 38932300
DOI: 10.3390/vaccines12060571 -
Sensors (Basel, Switzerland) Jun 2024Information that comes from the environment reaches the brain-and-body system via sensory inputs that can operate outside of conscious awareness and influence decision...
Information that comes from the environment reaches the brain-and-body system via sensory inputs that can operate outside of conscious awareness and influence decision processes in different ways. Specifically, decision-making processes can be influenced by various forms of implicit bias derived from individual-related factors (e.g., individual differences in decision-making style) and/or stimulus-related information, such as visual input. However, the relationship between these subjective and objective factors of decision making has not been investigated previously in professionals with varying seniority. This study explored the relationship between decision-making style and cognitive bias resistance in professionals compared with a group of newcomers in organisations. A visual "picture-picture" semantic priming task was proposed to the participants. The task was based on primes and probes' category membership (animals vs. objects), and after an animal prime stimulus presentation, the probe can be either five objects (incongruent condition) or five objects and an animal (congruent condition). Behavioural (i.e., accuracy-ACC, and reaction times-RTs) and self-report data (through the General Decision-Making Scale administration) were collected. RTs represent an indirect measure of the workload and cognitive effort required by the task, as they represent the time it takes the nervous system to receive and integrate incoming sensory information, inducing the body to react. For both groups, the same level of ACC in both conditions and higher RTs in the incongruent condition were found. Interestingly, for the group of professionals, the GDMS-dependent decision-making style negatively correlates with ACC and positively correlates with RTs in the congruent condition. These findings suggest that, under the incongruent decision condition, the resistance to cognitive bias requires the same level of cognitive effort, regardless of seniority. However, with advancing seniority, in the group of professionals, it has been demonstrated that a dependent decision-making style is associated with lower resistance to cognitive bias, especially in conditions that require simpler decisions. Whether this result depends on age or work experience needs to be disentangled from future studies.
Topics: Humans; Decision Making; Reaction Time; Male; Cognition; Adult; Female; Workplace; Semantics; Bias; Middle Aged
PubMed: 38931785
DOI: 10.3390/s24123999 -
Sensors (Basel, Switzerland) Jun 2024With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and... (Review)
Review
With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.
PubMed: 38931740
DOI: 10.3390/s24123957 -
Sensors (Basel, Switzerland) Jun 2024The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate...
The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate foot-end contact signals can provide robust support for robot motion control and decision-making processes. The shape and uncertain rotation of the wheel-legged robot foot end represent a significant challenge to sensing the robot foot-end contact state, which current foot-end sensing schemes cannot solve. This paper presents a sensing method for the tire stress field of wheel-legged robots. A finite element analysis was conducted to study the deformation characteristics of the foot-end tire under force. Based on this analysis, a heuristic contact position estimator was designed that utilizes symmetrical deformation characteristics. Strain sensors, arranged in an array, extract the deformation information on the inner surface of the tire at a frequency of 200 Hz. The contact position estimator reduces the dimensionality of the data and fits the eigenvalues to the estimated contact position. Using support vector regression, the force estimator utilizes the estimated contact position and sensor signal to estimate the normal reaction force, designated as F. The sensing system is capable of detecting the contact position on the wheel circumference (with a root mean square error of 1.150°), as well as the normal force of 160 N on the Z axis (with a root mean square error of 6.04%). To validate the efficacy of the sensor detection method, a series of randomized and repeated experiments were conducted on a self-constructed test platform. This novel approach offers a promising avenue for perceiving contact states in wheel-legged robots.
PubMed: 38931739
DOI: 10.3390/s24123956 -
Sensors (Basel, Switzerland) Jun 2024Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge's natural frequency over...
Various approaches have been proposed for bridge structural health monitoring. One of the earliest approaches proposed was tracking a bridge's natural frequency over time to look for abnormal shifts in frequency that might indicate a change in stiffness. However, bridge frequencies change naturally as the structure's temperature changes. Data models can be used to overcome this problem by predicting normal changes to a structure's natural frequency and comparing it to the historical normal behaviour of the bridge and, therefore, identifying abnormal behaviour. Most of the proposed data modelling work has been from long-span bridges where you generally have large datasets to work with. A more limited body of research has been conducted where there is a sparse amount of data, but even this has only been demonstrated on single bridges. Therefore, the novelty of this work is that it expands on previous work using sparse instrumentation across a network of bridges. The data collected from four in-operation bridges were used to validate data models and test the capabilities of the data models across a range of bridge types/sizes. The MID approach was found to be able to detect an average frequency shift of 0.021 Hz across all of the data models. The significance of this demonstration across different bridge types is the practical utility of these data models to be used across entire bridge networks, enabling accurate and informed decision making in bridge maintenance and management.
PubMed: 38931663
DOI: 10.3390/s24123879 -
Sensors (Basel, Switzerland) Jun 2024The present pilot study aimed to propose an innovative scale-independent measure based on electroencephalographic (EEG) signals for the identification and quantification...
OBJECTIVE
The present pilot study aimed to propose an innovative scale-independent measure based on electroencephalographic (EEG) signals for the identification and quantification of the magnitude of chronic pain.
METHODS
EEG data were collected from three groups of participants at rest: seven healthy participants with pain, 15 healthy participants submitted to thermal pain, and 66 participants living with chronic pain. Every 30 s, the pain intensity score felt by the participant was also recorded. Electrodes positioned in the contralateral motor region were of interest. After EEG preprocessing, a complex analytical signal was obtained using Hilbert transform, and the upper envelope of the EEG signal was extracted. The average coefficient of variation of the upper envelope of the signal was then calculated for the beta (13-30 Hz) band and proposed as a new EEG-based indicator, namely Piq, to identify and quantify pain.
MAIN RESULTS
The main results are as follows: (1) A Piq threshold at 10%, that is, Piq ≥ 10%, indicates the presence of pain, and (2) the higher the Piq (%), the higher the extent of pain.
CONCLUSIONS
This finding indicates that Piq can objectively identify and quantify pain in a population living with chronic pain. This new EEG-based indicator can be used for objective pain assessment based on the neurophysiological body response to pain.
SIGNIFICANCE
Objective pain assessment is a valuable decision-making aid and an important contribution to pain management and monitoring.
Topics: Humans; Electroencephalography; Pilot Projects; Male; Female; Adult; Chronic Pain; Pain Measurement; Middle Aged; Signal Processing, Computer-Assisted; Young Adult
PubMed: 38931657
DOI: 10.3390/s24123873 -
Sensors (Basel, Switzerland) Jun 2024The transition to fully autonomous roadways will include a long period of mixed-autonomy traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs)...
The transition to fully autonomous roadways will include a long period of mixed-autonomy traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs) which use conservative driving behaviours to safely negotiate complex scenarios. This can lead to congestion and collisions with human drivers who are accustomed to more confident driving styles. In this work, an explainable multi-variate time series classifier, Time Series Forest (TSF), is compared to two state-of-the-art models in a priority-taking classification task. Responses to left-turning hazards at signalized and stop-sign-controlled intersections were collected using a full-vehicle driving simulator. The dataset was comprised of a combination of AV sensor-collected and V2V (vehicle-to-vehicle) transmitted features. Each scenario forced participants to either take ("go") or yield ("no go") priority at the intersection. TSF performed comparably for both the signalized and sign-controlled datasets, although all classifiers performed better on the signalized dataset. The inclusion of V2V data led to a slight increase in accuracy for all models and a substantial increase in the true positive rate of the stop-sign-controlled models. Additionally, incorporating the V2V data resulted in fewer chosen features, thereby decreasing the model complexity while maintaining accuracy. Including the selected features in an AV planning model is hypothesized to reduce the need for conservative AV driving behaviour without increasing the risk of collision.
PubMed: 38931644
DOI: 10.3390/s24123859 -
Sensors (Basel, Switzerland) Jun 2024Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time...
Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the family and two models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the and models boasting a mean of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.
PubMed: 38931511
DOI: 10.3390/s24123726 -
Sensors (Basel, Switzerland) Jun 2024Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent...
Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model's explainability.
PubMed: 38931500
DOI: 10.3390/s24123712