Condition Assessment Research Articles

Noise-robust automated sudden damage detection using blind source separation enhanced by variational mode decomposition and support vector machine based on shapelet transform

Compared to parametric counterparts, non-parametric (aka, model-free) damage detection methods have no requirements of accurate models, with the potential of autonomous monitoring of various complex structures. However, noises, or low signal-to-noise ratio (SNR), are one of the main challenges. This study is aimed at improving blind source separation (BSS)-based damage detection method, one of the most advanced non-parametric methods, in both aspects of noise robustness and autonomous operation. In particular, the measured acceleration responses are processed by variational mode decomposition (VMD) and wavelet transform (WT) in sequential, acting as the input for a BSS model. The BSS is then solved by independent component analysis (ICA), which approves to be more noise-robust compared to the state-of-the-art counterparts. Furthermore, shapelet transform is applied to extract the universal shape-based spike-like feature from the BSS model for training a support vector machine (SVM) model, applicable to different structures; it finally automates the sudden damage detection process and enables online monitoring. The effectiveness of the proposed method is illustrated by a numerical example and an experimental test, and demonstrated by a real-world seismic-excited structure. The results show that both single and multiple sudden damages can be automatically detected with high accuracy. Compared with the existing BSS methods, the proposed BSS method is more capable to detect small damages at relatively low SNR. In addition, the classification accuracy of SVM is also improved when shapelet-based feature is used for training, which reduces the malfunction of automated damage detection as shown by the numerical example. Therefore, the proposed strategy has the potential for rapid condition assessment of structures during rare/extreme events, before engineers are sent for further post-event inspection.

Comparative Analysis of Image Stitching Algorithms for Bridge Inspection Imaging Systems

This paper discusses an innovative vehicle-mounted device designed for efficient inspection of bridge undersides, aiming to address the challenges of manual inspections, such as being labor-intensive and inefficient. The device uses high-precision cameras to capture detailed images of bridge bottoms, facilitating a thorough condition assessment. It evaluates three feature point extraction algorithms for image stitching—Scale-Invariant Feature Transform (SIFT), Harris corner detection, and Speeded Up Robust Features (SURF)—to create complete visual representations of bridge undersides. Field tests on a prefabricated I girder bridge demonstrated the device's effectiveness in gathering and stitching images, with the SIFT algorithm performing best for girder bottoms, Harris corner algorithm for web and flange surfaces, and SURF for rapid image processing. This research provides a foundation for the rapid identification of visible defects on bridge superstructures, significantly benefiting bridge maintenance and safety evaluations.

Assessment of Power Equipment Operational Safety in the Sustainable Management of Residual Lifespan

Introduction. Ensuring the safe operation of energy facilities is a critical issue at all life cycle stages. It is associated with the adoption of management decisions when rescheduling and assessing the remaining resources, which are made based on a comprehensive assessment of the technical condition of the equipment, taking into account the economic efficiency and environmental requirements for further operation, which is the basis for technical and economic assessments. Aim and tasks. This study aims to develop a technical and economic assessment of the safe operation of power equipment to ensure sustainable management of the remaining service lifespan. Results. An algorithm for assessing pipeline system elements by the coolant environment is proposed, which allows for the pipeline load and changes in erosion and corrosion caused by coolant movement. It was proposed that the equipment be systematised by safety class and coolant. When conducting a technical and economic assessment, these parameters and the costs of extending the service life and upgrading the existing equipment were considered in the standard calculations of a certain type of equipment. For this purpose, mathematical models for calculating the residual life for each type, obtained and confirmed based on empirical studies, are proposed. Research findings into ensuring the reliability and safety of operation with various heat carriers have shown that it is necessary to consider low-cycle and high-cycle loads and erosion processes. It was found that the erosive wear rate is 0.3-0.4 mm/year for bending sections of the main circulation pipeline, where maximum loads occur relative to low-cycle and vibration loads, and the stress is 212.5 MPa when assessing the residual life of 7 years. Conclusions. The cost-effectiveness of reassigning the service life is reasonable, as it does not require additional investment in upgrading or replacing equipment subject to annual technical diagnostics. The systematisation of pipelines, improved codifiers, lifespan assessment models, and algorithms for evaluating the remaining life under various loadings enhance the regulatory framework for safe nuclear power plant operations and support technical and economic evaluations for extending their lifespan.

Application of vibration signals in railway track diagnostics using a mobile railway platform

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.

Issues of classification and examination of lemons in the course of forensic commodity expertise

Identification and examination of export-import goods, in particular lemons, plays a significant role in facilitating the detection of customs violations and crimes, as well as in ensuring preventive measures to eliminate them. The article analyses the peculiarities of examination of citrus fruits, in particular lemons, with a view to their classification under classifier of goods Commodity Nomenclature of Foreign Economic Activity. The methodological approaches to the classification of citrus fruits within the framework of commodity expertise are considered. The main diagnostic tasks to be solved by a forensic expert in the course of lemon examination are identified: determination of product quality, chemical analysis, phytopathological analysis, toxicological analysis, organoleptic analysis, microbiological analysis, identification of origin, assessment of storage and transportation conditions. Modern analytical methods allow to conduct a comprehensive study of lemons at various levels – molecular, chemical and morphological – to obtain objective data on their quality, safety and other important characteristics. This data can be useful in legal proceedings to determine the origin, compliance with quality standards and dispute resolution. The article summarises information on the typical defects of lemons, namely: mechanical damage, diseases and lesions, growth and development defects, skin defects, parasites and pests, physiological defects, chemical defects (these defects are of natural origin). Defects inherent in low-quality products with signs of deterioration include mechanical damage, rot and mould, discolouration, wrinkling, shape defects, pests, disease damage, additional chemical treatment, loss of aroma and taste, improper storage and transportation, black spots, and skin tears. The mechanism for conducting a forensic examination of lemons is described in detail.

Satisfaction Survey of Medical Staff for Telemedicine after the COVID-19 Pandemic: A Comparison Between the COVID-Pandemic and Post-COVID Pandemic Periods.

Introduction: The role of telemedicine should be reassessed in the post-COVID-19 pandemic era. This study aimed to evaluate the perception and satisfaction of medical staff with telemedicine after the COVID-19 pandemic and compare these findings with those during the COVID-19 pandemic. Methods: Between January and February 2023, a questionnaire regarding the utilization, safety, and satisfaction with telemedicine was administered to 200 medical staff members (100 doctors and 100 nurses). Additionally, open-ended questions were included to gather insights on the strengths, weaknesses, and prerequisites of telemedicine. The satisfaction levels of the medical staff were compared with those from a previous study conducted during the COVID-19 pandemic. Results: The response rate among medical staff was 60.0% (50 doctors and 70 nurses). Concerning patient safety, 83.3% of respondents indicated a risk of worsening symptoms in chronic patients, and 68.3% expressed concerns about emergencies related to patient safety. Eighty-two percent of doctors preferred in-person visits over telemedicine, while 48.6% of nurses reported equal or greater satisfaction with telemedicine (p = 0.003). The reported strengths of telemedicine included the convenience of outpatient clinics (67%), while its primary weakness was the incomplete assessment of patient conditions (73%). The likelihood of partial substitution of telemedicine for in-person visits was significantly higher in the post-COVID-19 pandemic period compared to during the pandemic (71.7% vs. 49.0%, p < 0.001). Conclusion: In the post-COVID-19 pandemic era, telemedicine continues to offer significant benefits in enhancing patient access to medical care. However, medical staff remain concerned about its safety and show lower satisfaction compared to in-person visits. In response to these concerns, an effective telemedicine platform and legal standards, including practical guidelines, should be developed.

Enhanced vertical railway track quality index with dynamic responses from moving trains

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m-1 to 0.33 m-1 (substructure) and 1.25 m-1 to 2.50 m-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

Weakly‐supervised structural component segmentation via scribble annotations

AbstractSegmentation of structural components in infrastructure inspection images is crucial for automated and accurate condition assessment. While deep neural networks hold great potential for this task, existing methods typically require fully annotated ground truth masks, which are time‐consuming and labor‐intensive to create. This paper introduces Scribble‐supervised Structural Component Segmentation Network (ScribCompNet), the first weakly‐supervised method requiring only scribble annotations for multiclass structural component segmentation. ScribCompNet features a dual‐branch architecture with higher‐resolution refinement to enhance fine detail detection. It extends supervision from labeled to unlabeled pixels through a combined objective function, incorporating scribble annotation, dynamic pseudo label, semantic context enhancement, and scale‐adaptive harmony losses. Experimental results show that ScribCompNet outperforms other scribble‐supervised methods and most fully‐supervised counterparts, achieving 90.19% mean intersection over union (mIoU) with an 80% reduction in labeling time. Further evaluations confirm the effectiveness of the novel designs and robust performance, even with lower‐quality scribble annotations.

Study Assessment of Soil and Water Quality Conditions on Barren Agricultural Lands in Tropical Regions

Barren land is non-productive land that is difficult to use as a medium for integrated agricultural cultivation. The aim of this research is to determine the profile and feasibility status of land in barren agricultural areas quantitatively. The research method used was descriptive with purposive data collection. Data analysis included SQI and WQI parametric analyses. The research results indicate that the ideal soil parameter is only pH 7.5-7.7, while for water parameters it is pH 8.0-8.1, meeting water quality standards. SQI values range from 0.44 to 0.49 and WQI from 0.45 to 0.57. SQI and WQI values at the research site fall into the poor category, indicating difficulty in converting the land into agricultural use. SQI and WQI show a strong correlation as depicted in the model Y = 4.113 + 0.026 (R2 = 0.789). Correlation tests showed strong correlations in soil between redox potential and soil pH (0.449), and redox potential and organic matter (0.377). Weak correlations were found between cation exchange capacity and soil pH (0.009), and nitrate and total N (0.517). In water, strong correlations were found between water pH and nitrite (0.302) and ammonia (0.529). Additionally, water pH showed weak correlations with carbon dioxide (0.752) and organic matter (0.659). The values of soil and water parameters have an immediate impact on plant growth patterns. Therefore, integrated agricultural cultivation patterns need to be developed. In conclusion, empirically, the condition of barren land indicates poor land use feasibility. The poor profile and biophysical feasibility conditions of barren land are attributed to environmental pollution and runoff from other land areas.

Technical aspect of the operation of accelerometers as composition of a system for monitoring engineering structures of a cable-stayed bridge over the Petrovsky canal in the construction of the Western Speed Diameter highway in Saint Petersburg

Aim. This study analyzes the current monitoring system for the engineering structures of a cable-stayed bridge across the Petrovsky Canal, part of the Western High-Speed Diameter highway in St. Petersburg, focusing on emergency situations where accelerometers on pylons record values that exceed limits. Methods and Materials. The study utilizes statistical data from existing monitoring system databases. The tasks, set within the framework of the study, are accomplished by applying theoretical scientific methods including analytical techniques, mathematical statistics, and induction. Results. The analysis presents an overview of the current system used for monitoring engineering structures and highlights issues related to emergency situations in which accelerometer readings exceed threshold values. This study proposes a method for assessing the reliability of the monitoring system by classifying the data obtained from the accelerometer into two groups. The research identifies the need for further investigations to understand the root causes of these issues. Conclusion. Тhe results can assist in the design and installation of monitoring systems of the engineering structures for cable-stayed bridges, as well as for modernization and optimization of existing monitoring systems to improve the quality of structural technical condition assessments.

System identification for structural condition assessment: Application to critical neoclassical monuments in Nepal

This paper presents structural condition assessment of some critical neoclassical monuments in Nepal using nonparametric and parametric system identification techniques. Neoclassical buildings in Nepal house critical administrative units. Structural condition assessment of such buildings is therefore important for the safety of their occupants and interrupted function during and after major earthquakes. The historical and cultural values of such monuments necessitate regular structural assessment for maintenance, repair, and preservation. Dynamic characteristics such as natural vibration frequencies of structures provide valuable insights on their structural condition. While numerical models are commonly used to estimate eigen frequencies of structures, they are associated with large uncertainties in massive monuments with complex structural and geometrical configurations. Noninvasive dynamic identification techniques provide an alternative means in such structures. Ambient vibration records from six neoclassical monuments in the Kathmandu Valley, Nepal are used in this study to estimate vibration frequencies of the structures in different states. The structures were damaged during the 2015 Gorkha earthquake, and subsequently retrofitted. Changes in vibration frequencies before and after retrofitting provide useful insights on structural improvement through retrofitting. Input-output and output-only system identification techniques are tested to simplify the structural condition assessment approach. We conclude that the state space system identification can stably quantify dynamic properties so stiffness variation can be confidently extracted, which is the key application for noninvasive structural system identification. Also, using minimum number of sensors, we captured damage aggravation deploying the modal assurance criterion. The outcomes indicate that structural condition assessment of complex structures is possible using a limited number of sensors for circumstances such as damage aggravation to temporal variation (evolution/reduction) of dynamic characteristics.

National survey of hospital rheumatology service users to inform a statement set describing the minimum service expectations for publicly funded rheumatology secondary care in Aotearoa New Zealand.

The essential components of a rheumatology service for public hospital rheumatology services in Aotearoa New Zealand are not yet defined. We aimed to seek the views of users of public hospital rheumatology services on potential components of a rheumatology service. Online survey of adults in Aotearoa New Zealand who self-reported as having used district health board rheumatology services in the past 5 years. Participants indicated their level of agreement (7-point Likert scale) on whether service statements should be a component of public hospital rheumatology services and provided free-text comments. Analysis used descriptive statistics and inductive content analysis. Over 80% of participants (n=237) agreed or strongly agreed with 23 of the 26 statements about rheumatology care. The three statements that did not reach 80% agree or strongly agreed addressed infusion services for biologic disease modifying anti-rheumatic drugs, offering outpatient assessment for non-inflammatory musculoskeletal conditions and discharge back to primary care when an inflammatory disease is stable. The free-text comments were 1) expression of support of the statement, 2) reconfirming how or why particular services were valued or valuable, 3) caveats about statements, and 4) suggesting other services not mentioned in the statement. People with inflammatory rheumatic diseases who have used rheumatology services agreed with the majority of the statements of service components, with some caveats. A statement set describing the minimum service expectations for publicly funded rheumatology secondary care in Aotearoa New Zealand has been developed and endorsed by Arthritis New Zealand and the New Zealand Rheumatology Association.

Signs and symptoms in hospitalized children and adolescents' triggering nurses' awareness – A qualitative study across Denmark and Norway

BackgroundAssessment of signs and symptoms in hospitalized children presents unique challenges due to the children's age-related differences, such as vital signs and the broad range of medical conditions that affects children. Early detection of clinical changes in children is crucial to prevent deterioration, and while standardized tools exist, there is a growing recognition of the need to consider subjective factors based on experienced nurses' knowledge and intuition. ObjectiveTo explore which signs and symptoms, apart from vital signs, that trigger nurses' concern regarding deterioration of hospitalized children and adolescents. DesignThis study used a descriptive qualitative design. SettingsThe study was conducted at three pediatric departments in Denmark and a nursing department of a university in Norway, offering post graduate education programs for health care professions working with children and adolescents throughout Norway. ParticipantsA total sample of 29 registered nurses with varying levels of experience participated. MethodFour focus group interviews were used to collect data and analyzed with inductive content analysis approach. ResultsNurses' knowledge about children's clinical conditions is influenced by the nurses experience, their use of senses like touching the child with their hands, and the use of various approaches. Information from parents about the child's normal behavior are considered valuable. These sources of information, often difficult to verbalize, might be referred to as intuition or “gut feeling” and often guides the nurses' actions when vital signs appear normal, and nurses rely on their senses to assess the child's condition. Specific indicators triggering concern include changes in respiration, circulation, level of consciousness, and facial expressions. Challenges arise from nighttime assessments, interactions with parents, the presence of electronic devices, and children's ability to compensate. Clinical experience is a significant factor in nurses' ability to recognize changes in in the child's condition. ConclusionThis study highlights the multifaceted nature of nurses' assessments of clinical conditions in hospitalized children. Nurses draw on their experiences, intuition, and interactions with parents to complement vital signs-based assessments. Their intuition, or “gut feeling” serves as a valuable tool when vital signs do not fully capture the child's clinical status. Specific signs and symptoms that trigger nurses' concern, along with the challenges they face, contribute to a comprehensive understanding of the complexity of assessing children's clinical conditions. These findings, emphasize the role of nurses in early recognition of clinical deterioration in hospitalized children and the need for assessments that go beyond vital signs. Tweetable abstractBoth objective assessments and intuitive clinical judgment play an important role in identifying potential deterioration in pediatric patients.

Exosome-based drug delivery systems for enhanced neurological therapeutics.

Exosomes are small extracellular vesicles naturally secreted by cells into body fluids, enriched with bioactive molecules such as RNAs, proteins, and lipids. These nanosized vesicles play a crucial role in physiological and pathological processes by facilitating intercellular communication and modulating cellular responses, particularly within the central nervous system (CNS). Their ability to cross the blood-brain barrier and reflect the characteristics of their parent cells makes exosomal cargo a promising candidate for biomarkers in the early diagnosis and clinical assessment of neurological conditions. This review offers a comprehensive overview of current knowledge on the characterization of mammalian-derived exosomes, their application as drug delivery systems for neurological disorders, and ongoing clinical trials involving exosome-loaded cargo. Despite their promising attributes, a significant challenge remains the lack of standardized isolation methods, as current techniques are often complex, costly, and require sophisticated equipment, affecting the scalability and affordability of exosome-based therapies. The review highlights the engineering potential of exosomes, emphasizing their ability to be customized for targeted therapeutic delivery through surface modification or conjugation. Future advancements in addressing these challenges and leveraging the unique properties of exosomes could lead to innovative and effective therapeutic strategies in neurology.

Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach

Pavement health monitoring and assessment using sensor technology provides valuable insights into the condition of roads and helps in effective maintenance and management. This paper presents an advanced fiber Bragg grating (FBG) sensor-based pavement monitoring system that leverages multi-sensor data fusion techniques and an unsupervised learning approach for enhanced structural health assessment. The study commences with the fusion of data from various FBG sensors based on specific considerations and employing a novel function to improve the accuracy of pavement condition assessment. A dedicated data management system is introduced to address the challenges associated with continuous monitoring. This system automates data organization, pre-processing, and storage, facilitating historical trend analysis and post-processing. Then, the research introduces a novel unsupervised learning approach utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This algorithm identifies abnormal patterns in the FBG sensor data, which may indicate structural anomalies or unexpected events, aiding the operator by automatically flagging abnormal data. By incorporating principle component analysis (PCA)-based feature fusion, the system reduces the dimensionality of the data, which enhances the efficiency of the clustering process. Various anomalies were detected in the data collected for four months from the test track. The cause and relevance of these anomalies were assessed to facilitate informed decision-making. This approach enables the operator to achieve early detection of potential structural issues and distinguish abnormal damage data from other anomalous data, thereby contributing to the overall success of the pavement monitoring system. In conclusion, the integration of advanced FBG sensor technology with multi-sensor data fusion and unsupervised learning techniques results in a sophisticated and robust pavement monitoring system. The presented approach enhances accuracy by detecting anomalies at any strain level, computational time using multi-sensor data fusion, and reliability of structural health assessment through anomaly evaluation. It also provides a framework for proactive decision-making, ultimately improving the longevity and structural integrity of critical infrastructure.

Exploring the effective implementation of population-based SHM in existing buildings. Part I: Structural condition assessment

Despite major critical progress over the past few decades, condition-based decision-making concerning the immediate occupancy or efficient operation of an existing building continues to face unresolved issues. The key issue is to determine the structural condition and its residual capacity using data-driven-based methods without knowledge of its life cycle since design (ageing, extreme events, etc.). Environmental and operational variations, boundary conditions and a lack of damage-state building data lead to further uncertainties in the actual assessment of the structural capacity and affect the effectiveness of implementing structural health monitoring (SHM) solution. Recently, population-based SHM has been developed to address some of these issues to enable transfer from data collected from a group of nominally identical buildings, and to model missing data for the target building. This study presents the Build’Health™ framework for operational data-driven SHM, considering populations of buildings evaluated under operational conditions. Fifty-five period-height empirical models obtained from methods based on ambient vibrations recorded in nominally identical reinforced concrete buildings were first identified in the literature to define the building population models, along with design features (such as boundary conditions, geometry or material design). The shift in structural condition of the target building is inferred from the nominally identical building population, after adding empirical variability in the resonance period due to temperature, assumed herein to be the main environmental cause of structural variation. This work presents the framework implemented and its application to five target buildings under different monitoring and structural conditions. This manuscript is the first of a two-part article on population-based SHM relative to structural condition assessment (part I) and damage-feature classification (part II) presenting the Build’Health™ concept.

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads.

Multifactor evaluation method of smart meter

As an important terminal of smart grid, smart meter has the functions of equipment health monitoring and power metering. However, it is difficult to quantify the impact of factors such as ex-factory parameters, historical failure rates, electromagnetic interference when conducting an operational condition assessment. Therefore, in view of the large number of influencing factors and the difficulty in quantifying the degree of influence, an improved multifactor evaluation model for the operating status of smart meters is constructed. Firstly, a multifactor index system for evaluating the operating state of smart meters is constructed by analyzing the working mechanism and operating characteristics of smart meters. Then, an improved combinatorial weighting method is proposed by combining the subjective weight and the objective weight. The combinatorial weighting method is used to estimate the weight of factors in the health status evaluation of smart meters, and the improved multifactor evaluation method is presented to evaluate the health status of smart meters. Finally, an example is given to verify and analyze the proposed method. Compared with the other three methods, this method can provide effective evaluation results, and help guide targeted maintenance or replacement to improve the efficiency of smart meter detection.

Descriptive Study of the Influence of Foot Type on Physical Characteristics, Laxity, Strength and Baropodometry in Children Aged 5 to 10 Years

Background: Foot morphology in children is a crucial factor influencing multiple aspects of their physical development. Between the ages of 5 and 10 years, the critical period of child development is when the movement and stability patterns are consolidated that can affect their long-term physical performance and quality of life. The aim of this study is to analyze how the type of foot influences different physical characteristics, laxity, strength, motor tests, and baropodometric variables in children aged 5 to 10 years. Methods: A cross-sectional study involving 196 children was conducted. Different physical characteristics, laxity, strength, motor tests, and baropodometric variables of the sample were analyzed for age and Foot Posture Index (FPI). Results: Differences in all variables were examined by age and FPI. Statistical analysis showed a moderate to high correlation (r &gt; 0.6, p &lt; 0.01) between FPI and the relaxed calcaneal stance position (RCSP) test. Some significant differences were also found in variables related to foot pronation and supination. These results provide valuable information for understanding differences in motor and functional development during childhood and pre-adolescence. Conclusions: The findings highlight the variability in physical and functional development between age and foot type groups, highlighting the importance of considering these differences in the assessment and management of foot-related conditions and biomechanics in childhood. Foot type significantly influences children’s growth and development.

EarMonitor: Non-clinical Assessment of Ear Health Conditions Using a Low-cost Endoscope Camera on Smartphones

Hearing loss affects 20% of the global population, a rate that is increasing dramatically as the world's population ages. Early prevention and identification of ear diseases can significantly reduce the risk of becoming disabled with hearing impairment. We propose EarMonitor, an interactive, vision-based ear health monitoring system that enables users to examine their ear conditions with a low-cost hand-held endoscope. EarMonitor can detect six ear health conditions suitable for self-assessment. It can particularly recognize complications from ear diseases, helping users better understand the results. In the wild, our computer vision algorithm achieves a detection sensitivity of 0.949 for earwax buildup and blockage in 100 external auditory canal photos; our deep learning model achieves an average detection sensitivity of 0.861 for the other five conditions considering complications in 350 tympanic membrane photos. We validated EarMonitor 's effectiveness through a user study involving 17 participants and two experts, leading to valuable insights regarding the design and interpretation of non-clinical assessment devices.