Condition Assessment Research Articles

Assessment of recent lake sediment conditions in the conterminous U.S.

This study provides the largest sediment quality assessment of lakes in the conterminous United States (U.S.). A variety of lakes (n = 1005) were selected based on the randomized, probabilistic sampling design of the broader 2017 National Lakes Assessment study. Surficial sediment samples (0–5 cm) were collected at one representative site (generally the deepest point) for each lake (n = 969). The samples were analyzed for 16 metal(loid)s, 25 polycyclic aromatic hydrocarbons (PAHs), 53 polychlorinated biphenyl (PCB) congeners, 27 legacy pesticides and metabolites, total organic carbon, and grain size. Metal(loid)s and PAHs were widely distributed due to natural and potential anthropogenic sources, with regional variations observed for lakes in the nine ecoregions encompassing the U.S. Most sites did not have detectable PCB congeners or legacy pesticides. An integrative chemical index of mean probable effect concentration quotients, composed of seven metal(loid)s and ƩPAH13, was used to assess sediment quality for the estimated population of 224,916 lakes in the conterminous U.S. Nationally, 26.5% (CI of 20–33%) of lakes were in good condition (corresponding to predicted low incidences of toxicity to benthic organisms), 69.3% (CI of 63–76%) of lakes were in fair condition, and 1.8% (CI of 0.6–3%) of lakes were in poor condition (corresponding to predicted high incidences of sediment toxicity). Unweighted metal(loid) and ƩPAH13 concentrations were compared to lake, watershed, and land use data. Deeper lakes were significantly more contaminated (or naturally enriched) with As, Cd, Cu, Pb, Hg, Zn, and ƩPAH13 than shallow lakes. Lakes at lower elevation were also associated with more contamination (or natural enrichment) of As, Cr, Pb, Hg, Ni, Zn, and ƩPAH13 than higher elevation lakes. Greater contamination was associated with watersheds containing larger percentages of developed land. This study demonstrates an approach which can be used by others to assess sediment quality in their jurisdictions.

Next-Generation Building Condition Assessment: BIM and Neural Network Integration

Next-Generation Building Condition Assessment: BIM and Neural Network Integration

Cable Intermediate Joint Crimp Condition Assessment and Early Failure Warning: An All-Fiber Approach and Application

Cable Intermediate Joint Crimp Condition Assessment and Early Failure Warning: An All-Fiber Approach and Application

Pavement surface condition assessment: a-state-of-the-art research review and future perspective

Pavement surface condition assessment: a-state-of-the-art research review and future perspective

Technical Condition Assessment and Modelling of Reed Valves in Vehicle Engine Intake Systems

Technical Condition Assessment and Modelling of Reed Valves in Vehicle Engine Intake Systems

From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty

Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive maintenance. Although data-driven methods demonstrate promising performance in direct RUL prediction, their robustness and practicability need further improvement regarding physical interpretation and uncertainty quantification. This work leverages variational neural networks to model bearing degradation behind envelope spectra. A convolutional variational autoencoder for regression (CVAER) is developed to probabilistically predict RUL distributions with confidence measures. Enhanced average envelope spectra (AES) are used as network input for its physical robustness in bearing condition assessment and fault detection. The use of the envelope spectrum ensures that it contains only bearing-related information by removing other rotor-related frequencies, hence it improves the RUL prediction. Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. Experimental validations are conducted on vibration data collected using multiple accelerometers whose natural frequencies cover bearing resonance ranges to ensure fault detection reliability. Beyond conventional bearing diagnosis, envelope spectra are extended for statistical RUL prediction integrating physical knowledge of actual defect conditions. Comparative and ablation studies are conducted against benchmark models to demonstrate their effectiveness.

ANGLED DENTAL IMPLANTS: IMPLICATIONS FOR CLINICAL PRACTICE AND BIOMECHANICAL CONSIDERATIONS

Angled dental implants have emerged as an effective solution in clinical settings where the placement of straight implants is constrained by unfavorable bone positions or the presence of adjacent anatomical structures. The angulation of implants significantly influences stress distribution around the implant, impacting its stability and longevity. Research highlights that the direction of applied loads is critical, as angled implants can generate greater bending and shear forces compared to straight implants, potentially leading to stress concentrations in surrounding bone. Finite element analysis (FEA) is commonly used to model these conditions, offering insights into stress zones and predicting potential failure areas. Bone quality plays a vital role in how implants respond to applied forces, with denser bones effectively dissipating forces while more porous bones are at higher risk of resorption and integration failure. Studies indicate that excessive stresses on angled implants can lead to bone resorption, compromising long-term stability. Therefore, careful consideration of implant angulation, connection type, bone quality, and thorough surgical planning is essential. Recent studies, including those investigating the effects of implant design and bone density on stress distribution, have demonstrated that varying implant threads and angulated abutments affect the stresses experienced by surrounding bone and implants. The findings emphasize the need for appropriate implant selection and a detailed assessment of bone anatomy and patient-specific clinical conditions. By incorporating advanced technologies like FEA, clinicians can enhance their understanding of biomechanical interactions, ultimately leading to improved clinical outcomes and patient satisfaction. Continuous education and the implementation of best practices in dentistry remain vital for the successful application of angled dental implants.

Investigation of Critical Aspects of Roughness Assessment for Airfield Pavements

One of the main priorities of airport authorities is to maintain a high level of serviceability of runway pavements due to the high safety requirements for aircraft at high speeds. Accordingly, the assessment of the functional condition of airfield pavements is crucial for the proper operation of an airport. The most critical functional parameter appears to be pavement roughness. It characterizes the condition of the runway surface and is directly related to the safety of aircraft flights, as it affects the handling characteristics and braking performance of the aircraft, the increase in operating costs, and the wear of the aircraft. Worldwide, there are several indices for assessing the roughness of airfield pavements. This study aims to compare some of these indices to assess their ability to capture the characteristics of airfield pavement roughness. For this purpose, roughness data were collected along a runway with flexible pavement at a regional airport in southeast Europe and corresponding indices were estimated. The analysis of the data leads to the most efficient index for assessing the roughness of airfield surfaces to date. However, the need for a new index that expresses the response of the aircraft remains a critical issue.

Predictive Health Monitoring of Palm Trees: Techniques and Applications for Enhanced Processing

This paper presents new framework for predictive health monitoring in palm trees using advanced deep learning methods. The system then selects the YOLOv8 method for the detection of the health of palm trees and further divides them into their real-time classification. Here, the model will, after training on its dataset, classify the palm tree as healthy, diseased, and stressed trees, keeping all the influential factors of the growth of the palm tree in consideration. It makes provision for the profiling of health status by way of early intervention, reducing risks and preventing diseases with a view to optimizing crop yields within palm plantations. The proposed model leverages state-of-the-art methodologies in object detection to process images of the palm tree with identification of key indicators of health issues. This approach has great implications for agricultural productivity in view of the maintenance of plant health through early detection. Further, the paper discusses challenges presented during the training and validation of the model, their strategies for overcoming such obstacles, and goes further into relating details to the technical architecture of the model. Further details on the works related to plant disease detection, image-based classification, and further applications of deep learning in agriculture have inspired the research work. Based on the obtained results, it can be observed that the proposed system provides a scalable and efficient solution for continuous monitoring and assessment of the health conditions of palm trees, hence enabling sustainable agricultural practices.

Aromatherapy was used to explore the sedative and hypnotic effects of Moringa seed essential oil on insomnia rats

AbstractMoringa is a type of plant that is used both for medicinal and food. Moringa seed (MS) are rich in volatile oil and have initially been employed to treat diseases of the nervous system. Insomnia, a prevalent neurological disorder, has led to this study's aim: to extract the essential oil from MS and analyze its potential to improve sleep. This study utilized petroleum ether for the thermal extraction of the essential oil from MS, which was then subjected to compositional analysis using Gas Chromatograph Mass Spectrometer (GC–MS). P‐chlorophenyl alanine (PCPA) was used to induce an insomnia model in Sprague–Dawley (SD) rats. Following the successful establishment of the model, the MS essential oil was administered at concentrations of 10%, 5%, and 2.5% to investigate its sedative and hypnotic effects. The efficacy of the MS essential oil was assessed by observing the general condition of rats in each group, conducting an open field test, a pentobarbital sodium righting test, and measuring the serum 5‐HT (5‐hydroxytryptamine) levels and hypothalamic GABA (γ‐aminobutyric acid) content. GC–MS analysis of the MS essential oil revealed a rich composition, including oleic acid, palmitoleic acid, stigmasterol, and γ‐stigmasterol, among other substances. Through the assessment of the rats' general condition, behavioral tests, and blood biochemical assays, it was inferred that MS essential oil aromatherapy can reduce the rat's locomotor activity, increase their interest in activity and exploration, enhance the serum 5‐HT levels, and elevate hypothalamic GABA content. Consequently, it can be concluded that MS essential oil has a sedative and hypnotic effect.

Detection Probabilities of Flathead Catfish in Small Kansas Impoundments

Abstract A primary challenge of Flathead Catfish Pylodictis olivaris management is uncertainty associated with sampling strategies and resulting ambiguity in population-level information. Assessment of impoundment and environmental conditions that affect detection probability may aid in reducing sample variance and benefit inferences regarding changes to Flathead Catfish populations. We sampled eight small impoundments in Kansas (37 – 114 surface ha) using low frequency electrofishing in summer, 2021. We revisited sites nine times over three months using an occupancy modeling framework to estimate the influence of impoundment and environmental conditions on detection probability of Flathead Catfish. We employed an information theoretic approach and ranked models built with impoundment as a random effect and three environmental variables predicted to influence detection of Flathead Catfish in small impoundments. Detection probability across all populations was 0.526 (SE = 0.020) and was influenced by water temperature, mean depth of the impoundment, and proportion of impoundment sampled. Generally, detection probability increased with all measured variables. The inclusion of detection probability in assessments of Flathead Catfish in small impoundments can inform interpretation of catch-related metrics. Further, variable detection suggests collection of multiple samples during a defined sampling period might be more suitable for characterizing populations than a single sample.

Mental Health Identification System

In an era where technology's reach extends to the intricacies of human well- being, "Mental Health Identification" emerges as a thought-provoking narrative that explores the convergence of innovation, ethics, and mental health. Relationships strain under the weight of newfound possibilities, underscoring. Developing an AI-Enabled Mental Health Identification for Early Intervention This project introduces an innovative AI-enabled mental health identification designed to revolutionize the early identification of mental health conditions. The project’s goal is to create a valuable tool that contributes to improved mental health awareness and support. The abstract provides a concise overview of the project. It highlights the development of an artificial intelligence-based mental health identification that utilizes cutting- edge machine learning algorithms to analyze face patterns, facial expressions, and textual inputs. The project’s focus is on creating an automated and non-intrusive tool for early detection and assessment of mental health conditions, aiming to contribute to improved mental health awareness and support. Mental health disorders have become one of the leading causes of disability worldwide, affecting millions of individuals. The early identification of mental health conditions is crucial for effective treatment and prevention. This paper presents a Mental Health Identification System that utilizes machine learning algorithms to predict and identify common mental health disorders based on a questionnaire dataset. The model is designed to focus on anxiety, depression, and stress disorders, using classification techniques to analyze user inputs

Experimental Study on Identification of Aerodynamic Damping Matrix for the Tower of an Operating Wind Turbine by Artificial Excitation

ABSTRACTQuantitation of damping is of great significance for the design and condition assessment of wind turbines. The authors' previous theoretical and numerical studies showed that compared with damping ratios, a modal aerodynamic damping matrix can better describe the damping coupling in the fore‐aft (FA) and side‐side (SS) tower motions. In the present study, an improved damping identification method was first proposed to identify this damping matrix with artificial exciters and then verified by using OpenFAST simulations under different excitation frequencies, excitation force amplitudes, and turbulent wind fields. Following the numerical study, a scaled wind turbine model with a geometric scale ratio of 1/75 was carefully designed based on the NREL 5‐MW wind turbine prototype, in which the scaled blade design follows the rule in thrust coefficient similarity. An identification study was performed with this scaled model by a series of wind tunnel tests. The modal aerodynamic damping matrix was identified under steady‐state harmonic excitation in the operating state and compared with the identified results by a free decay method and the theoretical values. The results experimentally confirm the correctness of the aerodynamic damping matrix theory under uniform wind and the feasibility of the improved identification method in practice.

A New Framework for Condition and Risk Assessment for Sustainable Management of PT Bridges

A New Framework for Condition and Risk Assessment for Sustainable Management of PT Bridges

IFGAN: Pre- to Post-Contrast Medical Image Synthesis Based on Interactive Frequency GAN

Medical images provide a visual representation of the internal structure of the human body. Injecting a contrast agent can increase the contrast of diseased tissues and assist in the accurate identification and assessment of conditions. Considering the adverse reactions and side effects caused by contrast agents, previous methods synthesized post-contrast images with pre-contrast images to bypass the administration process. However, existing methods pay inadequate attention to reasonable mapping of the lesion area and ignore gaps between post-contrast and real images in the frequency domain. Thus, in this paper, we propose an interactive frequency generative adversarial network (IFGAN) to solve the above problems and synthesize post-contrast images from pre-contrast images. We first designed an enhanced interaction module that is embedded in the generator to focus on the contrast enhancement region. Within it, target and reconstruction branch features interact to control the local contrast enhancement region feature and maintain the anatomical structure. We propose focal frequency loss to ensure the consistency of post-contrast and real images in the frequency domain. The experimental results demonstrated that IFGAN outperforms other sophisticated approaches in terms of preserving the accurate contrast enhancement of lesion regions and anatomical structures. Specifically, our method produces substantial improvements of 7.9% in structural similarity (SSIM), 36.3% in the peak signal-to-noise ratio (PSNR), and 8.5% in multiscale structural similarity (MSIM) compared with recent state-of-the-art methods.

REMOTE MONITORING OF RHEUMATOID ARTHRITIS PATIENTS USING A PERSONAL MESSENGER-BASED PLATFORM

BACKGROUND: Being stimulated by rapid development of communication technologies, telehealth delivery methods have become widespread and have potential benefits for diagnosis, treatment, rehabilitation and follow-up of patients. The article discusses the integration into clinical practice and the effectiveness of digital tool for remote monitoring and control of treatment of patients with rheumatoid arthritis. AIM: to evaluate the safety, effectiveness and technological features of monitoring patients with rheumatoid arthritis using a remote monitoring platform. METHODS: The prospective non-randomized controlled study included patients over 18 years with high and moderate activity rheumatoid arthritis discharged from the hospital for outpatient follow-up. The patients were divided into two groups: the first was a remote observation group, and the second was a standard in-person observation group. Data for remote assessment of patients' condition was obtained through a questionnaire using a software package for remote monitoring of patients based on personal messenger - Telemedbot. We used the HAQ functional disability assessment questionnaire, the EQ-5D European quality of life questionnaire, and questions to assess patients’ adherence to recommendations, the duration of morning stiffness, the number of painful and swollen joints, and the patient’s general assessment of the disease using a visual analogue scale. After 6 months, the effectiveness of rheumatoid arthritis treatment according to DAS28 was assessed in both groups. RESULTS: 30 patients used the remote monitoring program for 6 months. The traditional observation group also consisted of 30 patients. After 6 months, among patients using Telemedbot, low RA activity and remission were achieved more often than in the second group (p = 0.049). In the remote monitoring group, 9 (30.0%) and 11 (36.7%) patients achieved remission and low disease activity, respectively, versus 3 (10.0%) and 8 (26.7%) patients in the face-to-face control group. Thus, in the remote observation group, 20 (66.7%) patients managed to control the disease, while in the only face-to-face observation group, only 11 (36.7%) patients. CONCLUSION: Remote monitoring using Telemedbot can be considered a potential tool for increasing the availability of medical care and increasing the effectiveness of rheumatoid arthritis treatment.

Reuse, remanufacturing and recycling in the steel sector.

The global steel sector is undergoing a transition from being a major CO2 emitter to a more sustainable circular material service provider, moving towards (near) net zero CO2 through combined strategies of reuse, remanufacturing, recycling and changes to primary steelmaking. This paper considers the transition using the UK as an example, based on the current sector state and future plans/opportunities. Some key enablers/barriers have been identified, and case studies are presented on the current state of knowledge and technology developments. For example, increasing reuse/remanufacturing requires data on the component's remaining life at the end-of-product life; in this work use of in-service monitoring for steel-intensive applications in the transport sector is discussed identifying sensor types/locations for fatigue loading assessment for different use conditions to feed into material/product passports for reuse/remanufacturing decisions. Increased recycling of obsolete scrap has implications for composition control with increases in residual elements, such as Cu, Sn, Cr and Ni inevitable. Current and future approaches to recycling and scrap sorting are discussed along with case studies for how residual elements affect microstructural development during steel processing, including effects on recrystallization, phase transformation and fine-scale precipitation, which potentially could be exploited to give increases in product strength. This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Examination of weight loss and colour changes in Golden Delicious apples under light and dark storage conditions

AbstractThis study analyzed the impact of storing Golden Delicious apples at 3 °C with a relative humidity of 85 ± 5% in two environments, namely a darkroom (D03) and a lightroom (L03), on the fruit quality. The research evaluated weight loss (Δm) and color metrics (a*, b*, L*, ΔE, C*, hue angle). The number of apples used for each storage was 12. Weight loss was calculated by measuring the changes in mass during storage. Color differences (ΔE), chroma (C*), and hue angle were determined based on the L*a*b* scale values. Apples stored at 3 °C in D03 experienced a minimal percentage of weight loss of 7.02%. The results indicated that compared to the D03 storage, the L03 storage had a more significant increase in Δm and color variation. The non-destructive method of measuring color characteristics facilitates the assessment of the storage conditions for Golden Delicious apples.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

“I think we should wait and see”: A qualitative study of call-takers’ decision-making in consultations with patients suffering unrecognized myocardial infarction

ObjectivesCall-takers face a complex situation when assessing medical problems in emergency medical services calls. Patients with myocardial infarction experiencing atypical symptoms risk misinterpretation. We examined development in call-takers' decision-making process in telephone consultations with patients having imminent myocardial infarction. MethodsRecording of 38 calls among 19 patients (two per patient) who contacted Copenhagen Emergency Medical Services (Denmark) at least twice within one week before myocardial infarction diagnosis. The penultimate and last call were compared using qualitative content analysis. ResultsCall-takers’ assessment of the condition changed from symptom picture and dismissal of heart disease in penultimate call to severe condition, not heart-related, and possible heart disease in last call. Call-takers recommended watchful waiting in the penultimate call. Both calls involved response negotiation, while caution regarding misinterpretation was only seen in the penultimate call. ConclusionCall-takers used different decision-making approaches when the caller’s symptom descriptions appeared unclear and not corresponding with the medical understanding of severe conditions. Call-takers did not negotiate the condition's assessment but engaged in discussions about the response choice. Practice implicationsA protocol to negotiate response choice with callers having unclear clinical conditions should be developed. Clarifying watchful waiting as a recommendation may assist call-takers’ decision-making.