6664 research outputs found
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A Systematic review into the application of ground-based Interferometric Radar Systems for bridge monitoring.
Full text linkGround-based interferometric radar (GBIR) is a powerful remote sensing technique used for infrastructure monitoring, particularly in the field of bridge structural health monitoring (SHM). Despite its high resolution and rapid data acquisition and the availability of various commercial systems, GBIR has not yet been fully recognised or routinely adopted in standard bridge monitoring practices. This study presents a comprehensive review of GBIR technologies and methods historically applied in bridge SHM. A total of 104 peer-reviewed papers were selected through a systematic review process, encompassing 128 monitored bridges assessed using a wide range of GBIR systems. The applications of GBIR across different bridge materials and operational conditions are discussed in detail. The review shows that 76% of GBIR applications focus on roadway and railway bridges. In terms of materials, steel and concrete bridges dominate the dataset, accounting for 95% of the total, while masonry bridges represent only 5%. The GBIR system types examined in this study are categorised into six main groups based on their technical specifications, with their key characteristics and capabilities analysed. The review also investigates bridge feature extraction techniques, revealing a predominant focus on identifying natural frequencies, while fewer studies explore the extraction of damping ratios and structural mode shapes. Furthermore, the integration of GBIR with other sensing technologies—particularly accelerometers—is explored, highlighting opportunities for complementary sensor fusion. Overall, this study provides a comprehensive overview of the current state of practice and identifies key areas for future research and technological development
A mixed-methods systematic review of the effectiveness, acceptability and safety of self-acupuncture studies
Full text linkIntroduction
Ongoing acupuncture is not recommended by the National Institute for Heath and Care Excellence for managing long-term conditions. Self-acupuncture (SA) may offer a solution. This mixed-methods systematic review aims to identify and appraise the quality of SA studies and evaluate the acceptability, effectiveness, and safety of SA.
Methods
CINAHL, Embase, Medline, and the Cochrane library databases were searched. We included studies evaluating SA for any condition, performed by a patient or their carer, written in any language and conducted at any time. We excluded studies where acupuncture needles were not inserted and where participants were not trained in SA. The methodological quality was appraised using the Mixed-Methods Assessment Tool. Data were extracted, categorised and synthesised.
Results
Twelve SA studies were identified, including 1 randomised, controlled trial; 1 mixed-methods feasibility study; 1 pilot of a randomised crossover study; 3 quantitative service reviews; 2 qualitative studies; 1 survey report; and 3 case reports, with a total of 378 participants. Four studies were of a high methodological quality. All studies assessing it found SA acceptable (n = 9) and effective (n = 9). Only one serious adverse effect was reported.
A strength of the review is that it is the first systematic review focused solely on SA. Limitations include the small number of studies and the lack of high-quality evidence.
Conclusions
There is a significant gap in high-quality SA research. Although SA appears acceptable and safe, more robust studies are needed to determine its effectiveness. If proven effective, SA could help patients manage long-term symptoms
Human-Led Robotic Transportation of Elastic Objects with Adaptive Control
No full textThe perception and transportation of soft objects are critical tasks in daily life, and recent sensor advancements enable robots to safely perform these tasks in human-shared environments. This study advances elastic linear object transportation by introducing a collaborative framework where a human leads, providing task-specific instructions to guide the robot. Unlike conventional methods focused on fixed object manipulation, our approach dynamically controls and converges feature points of the elastic object into desired configurations during transportation. We developed an online model estimation technique using a least-squares optimization algorithm, with an exponential forgetting mechanism to adaptively update the model under disturbances from sensors and human interaction. Validated in experiments with a 6-DOF robot and depth camera, our method demonstrated robustness across varied scenarios, achieving faster convergence and reduced positional fluctuation compared to conventional gradient descent approache
A qualitative study to examine hidden care burden for older adults with overweight and obesity in England
Full text linkObjectives
The study aimed to explore the phenomena related to formal and informal social care needs among overweight and obese older adults living in England.
Background
Despite the rising prevalence of obesity among older adults, its impact on social care needs remains underexplored. Existing research highlights significant unmet social care needs among older adults, yet the specific challenges faced by those who are overweight or obese have received limited attention. This study addresses this gap by exploring and understanding the social care experiences and needs of older adults in England who are overweight or obese.
Methods
Participants were recruited from a local National Health Service (NHS) health centre in London England using a purposive sampling strategy to the point of analytical saturation. A total of 45 participants were invited and of these 33 participants were eligible to take part. All participants in this study are either of British origin or immigrants to the UK from various nationalities. A semi-structured interview was conducted, and a qualitative structural narrative analysis was undertaken.
Results
The study found that older adults, who are overweight or obese, were more likely to have physical health problems and problems with mobility. They were more likely to have informal voluntary care and support rather than formal social care support. They also had a weaker social support network, were more isolated and frustrated, lacked housing adaptations, felt unsafe, felt they were a burden to their families and felt discriminated against by the wider community. Care and support needs if not met, then these are likely to generate or widen health inequalities over time.
Conclusions
This study provides a unique perspective on unmet care needs among overweight and obese older adults in England. It highlights the compounded challenges faced by this population, emphasising the importance of holistic social care approaches that address both health and psychosocial needs. Findings suggest that minimal yet targeted interventions, such as accessible support networks and public health policies promoting social engagement, could significantly improve wellbeing and reduce long-term health inequalities
Can an animation improve parents’ knowledge and how does it compare to written information? Development and survey evaluation of an animation for parents about prenatal sequencing
Full text linkObjective: To develop and evaluate an animation for parents about prenatal sequencing.
Methods: A total of 428 participants who had been pregnant, or whose partner had been pregnant, in the past 24 months. Parents, patient organisation representatives and clinicians co-designed the animation describing prenatal sequencing (pS). Participants were semi-randomly assigned to receive one of three interventions (leaflet, animation or both) and answered questions assessing their self-perceived and objective knowledge before (T1) and after the intervention (T2). Satisfaction with and ease of understanding of the information was assessed at T2.
Results: Survey respondents’ (leaflet (n = 130), animation (n = 153) and both leaflet and animation (n = 145)) self-perceived understanding and knowledge of genetics and objective knowledge of pS increased after all interventions. The leaflet and animation were equally effective in improving objective knowledge of pS [F(2, 421) = 2.48, p = .085.]. The animation was viewed positively. Preferences for information format were mixed.
Conclusion: Animated and written information can improve knowledge and understanding of pS. Our animation expands the available information resources for parents offered pS. Further research should evaluate the utility of the animation in a clinical setting
IoT-Enabled energy-efficient and long-range solution for remote patient monitoring using Bluetooth low energy 5.x
No full textThe Internet of Things (IoT) has revolutionized Remote Patient Monitoring (RPM) by enabling real-time data transfer. Traditional systems suffer from high energy usage and limited range, making them less suitable for long-term monitoring. This paper presents a novel wearable sensor node leveraging latest Bluetooth Low Energy (BLE) 5.0 features, such as long-range communication and energy-efficient extended advertising. The system integrates an ultra-low-power ARM M33 MCU, a motion sensor for activity tracking, and cloud connectivity for remote monitoring. The Physical Layer (PHY) modes, which determine on-air data transfer, significantly impact communication reliability. Challenges like packet loss are common, especially at extended ranges. Typical solutions involve increasing transmit power or implementing retransmission strategies, each with energy implications. The proposed system pioneers the evaluation of BLE modes—LE 1M and LE Coded PHY—on energy consumption and data transfer reliability of a broadcaster for sensor data transmission in real-time clinical settings. Experimental results reveal that while the conventional LE 1M reduces data transfer time by 84.92%, it increases Packet Loss Rates (PLR). In contrast, the latest LE Coded PHY reduces packet loss to just 2% at ranges upto 300 m but decreases battery life by 42.58%, still allowing a projected 2.6-year lifespan. To address power consumption, we propose a Dynamic PHY Switching Algorithm (DPSA) that adapts PHY modes. Results are validated on an IoT platform, providing insights for selecting BLE PHY for energy-efficient e-healthcare beacons
Natural disaster and medication preparedness among elderly: a scoping review
Full text linkIntroduction
The increasing number of older people and their vulnerability to disaster and medication preparedness as the primary elements of disaster mitigation are necessary to reduce the impact of a disaster. Nevertheless, research on natural disasters and medication preparedness in the elderly population is still lacking. This review aimed to explore natural disaster and medication preparedness among elderly.
Methods
This review was guided by the Arksey & O’Malley methodological framework and reporting accordance to PRISMA-ScR. A scoping review was performed using the following four databases: Scopus, PubMed, Sage, and Google Scholar. Screening was conducted using the following criteria: articles written in English, open access, and published between 2020 and 2024. Articles must discuss natural disasters and medication preparedness for elderly. In the data search, we input several keywords that include “elderly,” “natural disaster,” “preparedness,” and “medication.” Snowballing was then conducted to find articles on preparedness interventions. Data extraction and analysis were then performed.
Results
There were 20 articles used in this review and the results highlight that elderly face unique challenges in disaster preparedness including, mobility limitation, restricted access to medication, communication barriers, limited social and social support. Tailored interventions such as disaster education and elderly-focused technology are crucial to improve preparedness and ensure their safety during emergencies.
Conclusion
The findings from this literature review are the majority of studies showing that most elderly people are not well prepared in facing disasters; however, through various programs that have been implemented by either the government or community, the elderly show more preparation when they encounter any natural disaster
Classification of urban environments using state-of-the-art machine learning: a path to sustainability
Full text linkUrban green infrastructure plays a vital role in the sustainable development of cities. As
urban areas expand, green spaces are increasingly affected. The pressure from new developments
leads to a reduction in vegetation and raises new public health risks. Addressing
this challenge requires effective planning, maintenance, and continuous monitoring. To
enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations.
Publicly available large-scale data, combined with machine learning models, can
improve our understanding. We explore the potential of Sentinel-2 to classify and extract
meaningful features from urban landscapes. Using advanced machine learning techniques,
we aim to develop a robust and scalable framework for classifying urban environments.
The proposed models will assist in monitoring changes in green spaces across diverse
urban settings, enabling timely and informed decisions to foster sustainable urban growth
Factors influencing education for self-help in a public emergency among older adult migrants: a cross-sectional study in China’s Mainland
Full text linkBackground: The number of older adult migrants in China continues to grow. As a unique population characterized by both “mobility” and “aging,” they face heightened risks during public emergencies. Objective: This study investigated the current acceptance rate among these older adult migrants with respect to education for self-help in a public emergency (ESHPE) and analyzed influencing factors. Study design: A cross-sectional study. Methods: This study’s data were derived from the 2018 National Migrant Population Dynamic Monitoring Survey, conducted by the National Health Commission of China; overall, 5840 migrants were included in this study. SPSS 25.0 and RStudio 4.3.2 were utilized to analyze the selected sample, while Chi-square tests were conducted to perform univariate analysis on the acceptance rate of ESHPE among older adult migrants. A combination of the Random Forest model and binary logistic regression analysis was employed to assess the importance of statistically significant variables. Results: Overall, 1162 older adult migrants received ESHPE, representing an acceptance rate of 19.90 %. The acceptance rate was lower among those aged over 75 (Odds Ratio [OR] : 0.637, 95 % Confidence Interval [CI] : 0.454–0.893); residing in rural villages (OR : 0.757, 95 % CI : 0.616–0.931); with a migration duration of 11–15 years (OR : 0.679, 95 % CI : 0.540–0.853), 16–20 years (OR : 0.725, 95 % CI : 0.547–0.961), or over 20 years (OR : 0.708, 95 % CI : 0.531–0.943); who had migrated for family (OR : 0.646, 95 % CI : 0.544–0.768), social (OR : 0.559, 95 % CI : 0.434–0.718), or other reasons (OR : 0.364, 95 % CI : 0.191–0.691); and who had not
lished resident health records (OR : 0.693, 95 % CI : 0.582–0.825) or were unaware of or unclear about such records (OR : 0.494, 95 % CI : 0.388–0.630). Conclusions: The acceptance rate of ESHPE in this cohort remains relatively low. Therefore, targeted intervention measures tailored to their specific needs must be developed, and more focused educational resources for public emergencies must be created. Online interactive platforms should be established to enhance the self-help education content and strategies. Such measures should help improve the acceptance rate of ESHPE among older adult migrants
Improving energy efficiency in buildings with an IoT-based smart monitoring system
Full text linkWith greenhouse gas emissions and climate change continuing to be major global concerns, researchers are increasingly focusing on reducing energy consumption as a key strategy to address these challenges. In recent years, various devices and technologies have been developed for residential buildings to implement energy-saving strategies and enhance energy efficiency. This paper presents a real-time IoT-based smart monitoring system designed to optimize energy consumption and enhance residents’ safety through efficient monitoring of home conditions and appliance usage. The system is built on a Raspberry Pi Model 4B as its core platform, integrating various IoT sensors, including the DS18B20 for temperature monitoring, the BH1750 for measuring light intensity, a passive infrared (PIR) sensor for motion detection, and the MQ7 sensor for carbon monoxide detection. The Adafruit IO platform is used for both data storage and the design of a graphical user interface (GUI), enabling residents to remotely control their home environment. Our solution significantly enhances energy efficiency by monitoring the status of lighting and heating systems and notifying users when these systems are active in unoccupied areas. Additionally, safety is improved through IFTTT notifications, which alert users if the temperature exceeds a set limit or if carbon monoxide is detected. The smart home monitoring device is tested in a university residential building, demonstrating its reliability, accuracy, and efficiency in detecting and monitoring various home conditions