Status Information Research Articles

Joint RSU and agent vehicle cooperative localization using mmWave sensing

In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.

Forest plant invasions in the eastern United States: evidence of invasion debt in the wildland-urban interface

ContextNon-native invasive plants are a growing threat to forests. Meanwhile, the amount of forest within the wildland-urban interface (WUI) is increasing, with housing-associated disturbances enabling the spread of non-native plants.ObjectivesWe tested whether (1) WUI or non-WUI forests are more invaded, (2) WUI intermix forests (houses mingling with forest) are more invaded than interface forests (housing abutting forest), (3) invasion in WUI forests is delayed (invasion debt) following housing development, and (4) WUI forest invasion is associated with land cover context (a proxy for disturbance and propagule pressure).MethodsWe conducted statistical comparisons of plant invasion metrics using WUI status information intersected with ~ 45,000 forest inventory plots (collected ca. 2015). We evaluated potential drivers of invasion using an ensemble learning approach and adopted a mixed-effects modeling framework to assess relationships between drivers and invasion.ResultsOur analyses revealed that the degree of invasion in WUI forest plots was significantly higher. We found evidence for invasion debt, including greater invasion of plots in the WUI longer. WUI interface forests were more invaded than intermix forests. Agricultural and developed land cover at medium to large scales (~ 66 − 5,300 hectares) were most highly associated with WUI forest invasion.ConclusionsThese findings elucidate dynamic plant invasion patterns and processes occurring within the WUI. They underscore the importance of monitoring and managing forests that have recently entered the WUI, with an emphasis on sources of exotic plant propagules at relatively large scales, before the housing invasion debt comes due.

Underwater Network Time Synchronization Method Based on Probabilistic Graphical Models

In underwater clustering and benchmark networks, nodes need to reduce the rate and energy consumption of acoustic communication while ensuring synchronization accuracy. In large-scale networks, the improvement in the efficiency of existing network time synchronization often relies on the optimization of topological structures, and the improvement in efficiency within local areas is limited. This paper proposes a method to synchronize underwater time using the probability graph model. The method utilizes the positional and motion status information of sensor networks to construct a factor graph model for distributed network synchronization. By simplifying the marginal probability density function of the system clock difference, it can quickly calculate the clock difference parameters of nodes, thereby effectively improve the synchronization efficiency. The experimental results show that the method can complete global time synchronization within a cycle while achieving a clock difference correction accuracy higher than seconds, which significantly optimized the synchronization cycle and efficiency, and reduced the energy consumption of the acoustic communication.

Developing a jam-absorption strategy for mixed traffic flow at signalized intersections using deep reinforcement learning

ABSTRACT Jam-absorption driving (JAD) can effectively prevent the generation and propagation of traffic oscillation. To alleviate the traffic congestion in the signalized intersection with mixed traffic flow, including human driving vehicles (HDVs) and connected and automated vehicles (CAVs), this study provides a jam-absorption driving strategy based on the traffic delay prediction of the mixed platoon under traffic congestion. An online traffic congestion prediction method with the objective of JAD is proposed and focuses on the leaving state of the trajectory to achieve fast capture of congestion features. Then, with real-time status and prediction information, we develop a Jam-absorption driving strategy based on a deep reinforcement learning (DRL) model to improve adaptability to the mixed traffic environment. The results show that this strategy can suppress more than 70% of traffic oscillations with excellent execution efficiency, improving traffic safety and efficiency.

Group information enhances recognition of both learned and unlearned face appearances.

Are people better at recognizing individuals of more relevant groups, such as ingroup compared to outgroup members or high-status compared to low-status individuals? Previous studies that associated faces with group information found a robust effect of group on face recognition but only tested it using the same images presented during the learning phase. They therefore cannot tell whether group information enhances encoding of the specific image presented during learning or encoding of the person who appears in it, which should generalize to other images of that person. In addition, the measures used in these studies do not sufficiently distinguish between sensitivity and response bias. In this article, we addressed these limitations and examined in three experiments the effect of group membership (Experiments 1 and 2) and social status (Experiment 3) on face recognition. In all experiments, we assessed recognition of both learned and unlearned views of the learned faces. Our results show improved recognition of ingroup members compared to outgroup members and of individuals of high-status groups compared to low-status groups for both learned and unlearned views. These effects emerged also when we used measures of memory accuracy that adequately control for response bias. These findings highlight the importance of group and status information in person recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Association between dietary inflammatory index and adolescent myopia based on the National Health and Nutrition Examination Survey

The prevalence of adolescent myopia is remarkably increasing. Previous studies have indicated that an unhealthy diet is a risk factor for myopia. However, the link between diet-related inflammation and myopia is unclear. To explore their correlation, we used dietary inflammation index (DII) that is a parameter to quantify the inflammatory potential of diet, to reveal the relationship between DII and myopia in adolescents. We extracted sociodemographic data, information of diets and eye refractive status of adolescents from National Health and Nutrition Examination Survey (NHANES) for period 1999–2008. Dietary intake data was used to calculate DII scores, which were then categorized into quartiles. Multivariable regression models and subgroup analyses were conducted to investigate the association between DII and myopia. Subsequently, smoothed curve analyses were conducted to discern the trend of correlation between DII and myopia across diverse population. A total of 7191 juveniles aged at 12 to 18 years with complete information were included in our study, consisting 3367 participants with diagnosis of myopia. Among these participants, a trend towards an increasing prevalence of myopia was observed with a higher DII. After adjusting for all covariates, stratified logistic regression analyses showed that among the population aged in 16 to 18 years old or with 9-11th grade educational level, the prevalence of myopia was significantly increased with higher DII score (OR = 1.06, 95% CI = 1.01, 1.11, P = 0.006; OR = 1.06, 95% CI = 1.01, 1.11, P = 0.010). In the two subgroups, participants in the highest quartile of DII had a 31.00% higher risk of myopia and a higher 27.00% risk of myopia respectively, compared to those in the lowest quartile of DII. Our results revealed an increasing trend in the prevalence of myopia with increased DII score in adolescents. Particularly, DII was positively associated with the risk of myopia among the population aged in 16 to 18 years old and with 9-11th grade educational level.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Legal Implications of Building Use Rights Certificates Without Land Status Information: Consumer Protection and the Responsibility of the National Land Agency

Abstract: This research examines the legal uncertainties arising from the issuance of Building Use Rights (HGB) certificates on land with Management Rights (HPL) owned by the Special Capital Region of Jakarta. The lack of clarity in the National Land Agency's certificate, which does not explicitly state that the building use rights are based on land management rights, leads to confusion and a sense of deception among consumers, making this issue crucial. This issue holds significant importance as the National Land Agency's certificate, which does not explicitly state that building use rights are based on land management rights, leads to consumer confusion and feelings of deception. When consumers applied for a certificate extension 20 years after the National Land Agency issued the Certificate Extension Decision Letter, the agency abruptly retracted both the Decision Letter and the certificate. This was due to the BPN's recent realization that the land was under Land Management Rights. This research uses a normative juridical approach to analyze the legal responsibility of the National Land Agency in the issuance of certificates. The data collection technique uses document studies, while the analysis technique uses court decision studies. Research results indicate that Building Use Rights (HGB) certificates that do not specify the type of land—whether it is on state land, Land Management Rights, or ownership rights—have the potential to harm consumers and cause legal disputes. Therefore, this study recommends land policy reforms to ensure legal certainty and consumer rights protection in property transactions. Keywords: Building Use Rights, Land Management Rights, Land Certificate, Legal Certainty, National Land Agency

Analysis of the impact of periodontal disease management before the onset of type 2 diabetes mellitus on medical costs using administrative claims database

ObjectiveAlthough periodontal disease management has been shown to reduce the medical costs of diabetes mellitus, no studies have investigated management before the onset of type 2 diabetes mellitus. Therefore, this study examined the association between periodontal disease management before the onset of type 2 diabetes mellitus and medical costs. MethodsWe extracted information for periodontal disease management status from Japanese medical claims and specific health check-up databases among 4010 patients with periodontal disease and type 2 diabetes mellitus aged ≥ 30 years who had continued periodontal disease management for 2 years after diabetes onset. We divided patients into two groups: those who had received periodontal disease management for 2 years before and 2 years after onset and those who had received periodontal disease management for only 2 years after the onset of type 2 diabetes mellitus. ResultsThe HbA1c level in patients without periodontal disease management during the year of diabetes onset improved by 0.13 % in male and 0.24% in female 2 years later, while that in the group with periodontal disease management improved by 0.49 % and 0.74%, respectively. The medical costs were significantly lower in the group with periodontal disease management 2 years later than in the group with no periodontal disease management. ConclusionsRegular periodontal disease prevention and management under professional care before type 2 diabetes mellitus onset contribute to improved health and reduced medical costs.

Specialization of Physician Associates and Nurse Practitioners as Reflected in Workforce Projections.

Introduction The utilization of physician associates (PAs) and nurse practitioners (NPs) within the United States healthcare system has increased substantially since their inception in the 1960s. While many PAs and NPs are educated and work within specialty areas in their clinical practice, the federal government does not identify PAs and NPs by specialty in its workforce projections. Methods We obtained data from publicly available sources as well as professional organizations.Employment status, specialty, and location information for NPs and PAs were drawn from association census surveys. Results Outpatient or ambulatory care employment sites employ46.6% of NPs and 55.3% of PAs, whilealmost a quarter of each group(22.1% and 23.9%, respectively) workin general and surgical hospitals.About 9% of providers who are PAs and NPs are employed in Federally Qualified Health Centers, many of which are in health professional shortage areas (HPSAs). The number of PAs and NPs working in HPSAs hassignificantly increased from 72,000 PAs and NPs in2011 to 110,000 in 2023.There has been no growth of physicians in HPSAs during this same time. The Health Resources and Services Administration (HRSA) does not identify NPs and PAs by their specialty practice areaso the extent to which they providecare in specialty areas is unknown based on HRSA data. Conclusion It is essential to providedetailed information inclusive of PAs andNPs as well asphysicians in planning for healthcare workforce needs. The accurate projection of potential healthcare provider shortages requires knowledge of each type of provider along with their role and specialization to determine gaps in healthcare service delivery.

Design and Construction Of A River Water Level Monitoring System Based On IOT (Internet Of Things)

The prolonged rainy season due to extreme weather changes that occur in various regions in Indonesia means that people must be more alert to flood disasters. Many losses occurred due to flood disasters. Therefore, an effort is needed to minimize these losses, namely by creating a river level monitoring system based on IoT (Internet of Things). This river water level detection system uses an ultrasonic sensor (water level sensor), NodeMCU ESP8266 to read data and send it via the telegram application and a buzzer which functions to make a sound when the sensor detects water and the potential for flooding. The system will work automatically when water touches the sensor and sends flood detection status information via the telegram application. This system can be implemented in flood-prone areas. With this monitoring system, river water levels can be monitored and the risk of flooding can be reduced.

A Study on the Influence of Health Literacy of Chinese Elderly Populations Under the Background of the Elderly Care Industry

Aim: The present study aimed to evaluate the impact of health literacy, economic status, healthcare access, family support, and digital health information on the health behaviour of elderly individuals in China. Methodology: Data were collected from elderly populations in Beijing and Wuhan, with a sample size of 560 participants analysed using the JASP statistical software. Results: The results indicated that health literacy and economic status did not significantly influence the health behaviour of the elderly in China. In contrast, healthcare access, family support, and digital health information were found to have a substantial effect on health behaviour among this demographic. Conclusion: These findings offer both theoretical and practical contributions, enriching the existing body of knowledge and providing valuable insights for practitioners and policymakers.

Design and Development of An Integrated Monitoring System for Nebulizer and Infusion for Lung Disease Patients Based on IoT

This research develops an Internet of Things (IoT)-based monitoring system for nebulizers and infusion devices for patients with lung diseases, specifically Acute Respiratory Infections (ARI). The aim is to improve nursing efficiency and minimize the risk of complications due to delayed patient care by enabling remote monitoring and providing real-time information on the status of nebulizers and infusion devices via a website. The system is designed to stop the infusion fLow when the required amount is reached and to automatically turn off the nebulizer when there is no liquid. It uses an ESP32 microcontroller to control IR obstacle sensors, relays, and a servo motor for the infusion clamp, as well as an LCD display for digital information and feature displays. The system is also equipped with an alarm to indicate when the infusion liquid or nebulizer is depleted and a website to provide real-time status information to medical personnel. Testing shows that the system has a high accuracy of 98.7% and minimal error of approximately 1.82% in monitoring and controlling nebulizer and infusion fluids. In conclusion, the system successfully improves nursing efficiency and patient safety while reducing the workload of nurses through the automation of medical devices that were previously managed manually.

Discover the Factors of Longevity by Causal Discovery with PC Algorithm and Neural Networks

The study title has been to study the multilayered factors that govern a human prospect of living through the application of the PC Algorithm (Graph-Based Algorithm), logistic regression analysis, and the neural networks in combination. To realign the research purpose, a vast array of data can be used, which included socioeconomic status information alongside health and environmental aspects carefully chosen for their likely influence on how long people can live. This article will explain the longevity determinants by examining not only the conditions but also the environmental and health indicators and their combinatory effects. The PC algorithm can produce a directed acyclic graph (DAG) to see how elements like healthcare spending, education, and CO2 emissions are correlated with human longevity and how they influence each other. Moreover, the research takes other examples by analysis: researchers can examine hypothetical events. Neural networks were employed to depict the interrelationship among these variables which subsequently provided a user-appropriate understanding of their sudden joint influence. Consequently, a hypothesize was purposed that the potential interventions for the elongation of human life. Policy-making researchers can gain a better representation of the situation of health inequality and the new recommended solutions to tackle this problem and increase life expectancy by implementing complex analysis tools to blows away the criminal aspects of human longevity.

Risk factors for COVID-19 infection among the German older adult population

Abstract Background There are scarce analyses on COVID-19 infection risk factors for the older adult population. This study aims to examine COVID-19 infection risk factors in the older adult general population. Methods The data for this analysis came from the population-representative Study on Health of Older People in Germany (Gesundheit65+) conducted by the Robert Koch Institute from 10/2021 to 08/2022. We considered information on COVID-19 infection status (self-reported infection confirmed by PRC test at least once vs. never), socio-demographic factors, health risk factors, and different face-to-face interactions. The analysis was restricted to 2,976 individuals aged 65+ years with complete infection status information. We assessed weighted prevalences and used univariable and multivariable logistic regressions to evaluate infection risk factors. Results The overall prevalence of infection was 7.2% (95% confidence interval, 6.0-8.7). Infection prevalences were significantly higher (compared to their respective reference group) among participants living in assisted living facilities (24.7% vs. 6.7%), individuals who got help to perform basic activities of daily living (10.8% vs. 6.8%), COVID-19 unvaccinated individuals (25.2% vs. 6.9%), individuals who did not meet family or friends in the last month (12.7% vs. 6.7%), and participants who did not smoke (7.8% vs. 2.6%). No other variable was significantly related to COVID-19 infection. In multivariable analysis, variables independently associated with higher odds ratio (OR) of COVID-19 infection were: living in an assisted living facility OR 3.6 (1.8-7.2), not being vaccinated against COVID-19 OR 4.6 (1.5-14.1), and not smoking OR 1.6 (1.1-2.6). Conclusions The preliminary results of this study align with the prevalence of COVID-19 infection found in concomitant studies. A noteworthy element of our study is the vaccine’s protective effect. The pandemic had a particularly detrimental effect on residents of assisted living facilities Key messages • Improvement in quality of care and infection preventive measures in nursing homes is necessary. • Pandemic preparedness must consider the need for help in the daily life of the older population.

Synchronization of nonlinear neural networks with hybrid couplings and uncertain time-varying perturbations: A novel distributed-delay impulsive comparison principle

This paper investigates the synchronization of nonlinear drive-response neural networks subject to uncertain time-varying perturbations, non-delayed coupling, and distributed delay coupling. To address the influence of distributed and discrete delays on the system, we establish a novel impulsive comparison principle, extending the Halanay inequality. By leveraging Lyapunov stability theory, we derive sufficient conditions for the exponential synchronization of the neural networks using a delayed impulsive controller with historical status information. This approach relaxes the conventional constraint that impulsive delays must be smaller than impulsive intervals, thereby generalizing existing synchronization results for distributed delay networks. Numerical simulations for chaotic neural networks validate the theoretical results and demonstrate the sensitivity of the control gain matrix.

Exploring the role of project status information in effective code smell detection

Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection.

Connecting People over Distance Using Furry Mediators

Many people are separated from their families or friends by distance. They want to keep a feeling of connectedness with each other but fail due to distance, time zone differences, or busy lives. Besides, they find it hard to transfer implicit information using traditional ways of communicating. This research concentrates on connecting people over distance using furry mediators. These mediators can share users presence and status information with their remote families or friends using different gestures. A lab study with 7 participants was conducted to explore whether the different gestures can be successfully interpreted by the users. After that, a diary study with 4 participants was conducted to investigate users perception of the furry mediators. Our findings illustrate the potential for using furry mediators as a user interface to connect remote people. The furry mediators increased users communication frequency and provided them with a subtle and ambiguous communication channel.

Health locus of control, perceived health competence, and satisfaction of patients with rheumatological conditions versus periodic health examination patients

ABSTRACT Important health management goals include building patient loyalty, predicting patient behavior and adherence, and understanding treatment outcomes. This study focused on health locus of control (HLC), perceived health competence, and satisfaction with care in an outpatient sample by comparing chronic care patients and periodic health examination (PHE) patients in the same clinic. The study sample consisted of 146 adult patients from a rheumatology outpatient clinic. Participants responded to validated measures of psychosocial variables and answered a sociodemographic and health status information form. The findings showed that while both patient groups were similarly satisfied with care, chronic care patients perceived lower levels of health competence and internal HLC and higher levels of chance HLC than PHE patients. Satisfaction with care was negatively associated with chance HLC in the chronic care group, while health competence was negatively associated with chance HLC in the PHE group. The findings of the current study suggest that although chronic care patients were satisfied with care, they had stronger chance beliefs and weaker beliefs about internal factors. Hence, for better management of rheumatological conditions, it is important to focus on improving patients’ coping skills through effective health education strategies.

Candidate Selection in Teams: Be the Best or Surround Yourself with the Best?

ABSTRACT This study investigates, via an experiment, how the decentralization of a firm’s selection process affects the caliber of the chosen candidate in a team-based environment. We predict and find that, when decision makers have comprehensive and unambiguous candidate-specific information regarding who is the best for the job, the quality of the selected candidates is lower under a decentralized versus centralized selection process. We also find that nonpecuniary status concerns drive the effect. Results of two boundary conditions reveal that, as the clarity of information regarding who is the best candidate for the job decreases (due to decision-makers having weaker or mixed signals about job candidates), the quality of selected candidates becomes no worse under a decentralized than under a centralized selection process. Overall, our results indicate that nonpecuniary status considerations and information environment can influence candidate selection decisions in organizations. Data Availability: Data are available from the authors upon request.