Search Results (335)

Hydraulic asphalt concrete (HAC) is susceptible to performance deterioration under long-term water immersion. This study conducted compressive, tensile, and bending tests on HAC under various immersion times (0–96 h), established a multidimensional performance evaluation method, and developed a service-life prediction model for long-term water immersion. The average relative error between test values and predicted values was less than 5%, validating the model’s effectiveness and applicability. Results indicate that the rate of mechanical property degradation exhibits stage-dependent characteristics with immersion time, and the water damage resistance of alkaline aggregate is significantly superior to that of acidic aggregate. The predictive model shows that after 192 h of immersion, the retention rate of key mechanical properties for the alkaline aggregate reaches 92.71%, while that for acidic aggregate was only 73.85%. This study establishes a predictive model that provides a theoretical basis for assessing the lifespan of HAC under long-term immersion conditions. Full article

To prolong the service life of asphalt pavement and reduce its maintenance cost, a fiber Bragg grating (FBG) sensor encapsulated in carboxylated carbon nanotube (CNT-COOH)-modified gel material suitable for strain monitoring of asphalt pavement was developed. Through tensile and bending tests, the effects of carboxylated carbon nanotubes on the mechanical properties of gel materials under different dosages were evaluated and the optimal dosage of carbon nanotubes was determined. Infrared spectrometer and scanning electron microscopy were used to compare and analyze the infrared spectra and microstructure of carbon nanotubes before and after carboxyl functionalization and modified gel materials. The results show that the incorporation of CNTs-COOH increased the tensile strength, elongation at break, and tensile modulus of the gel material by 36.2%, 47%, and 17.2%, respectively, and increased the flexural strength, flexural modulus, and flexural strain by 89.7%, 7.5%, and 63.8%, respectively. Through infrared spectrum analysis, it was determined that carboxyl (COOH) and hydroxyl (OH) were successfully introduced on the surface of carbon nanotubes. By analyzing the microstructure, it can be seen that the carboxyl functionalization of CNTs improved the agglomeration of carbon nanotubes. The tensile section of the modified gel material is rougher than that of the pure epoxy resin, showing obvious plastic deformation, and the toughness is improved. According to the data from the calibration experiment, the strain and temperature sensitivity coefficients of the packaged sensor are 1.9864 pm/μm and 0.0383 nm/°C, respectively, which are 1.63 times and 3.61 times higher than those of the bare fiber grating. The results of an applicability study show that the internal structure strain of asphalt rutting specimen changed linearly with the external static load, and the fitting sensitivity is 0.0286 με/N. Combined with ANSYS finite element analysis, it is verified that the simulation analysis results are close to the measured data, which verifies the effectiveness and monitoring accuracy of the sensor. The dynamic load test results reflect the internal strain change trend of asphalt mixture under external rutting load, confirming that the encapsulated FBG sensor is suitable for the long-term monitoring of asphalt pavement strain. Full article

Corrosion fatigue damage significantly affects the long-term service of marine platforms such as propellers. Fatigue testing of pre-corrosion specimens is essential for understanding damage mechanisms and accurately predicting fatigue life. However, traditional seawater-based tests are time-consuming and yield inconsistent results, making them unsuitable for rapid evaluation of newly developed equipment. This study proposes an accelerated corrosion testing method for ZCuAl₈Mn₁₃Fe₃Ni₂ nickel–aluminum bronze, simulating the marine full immersion zone by increasing temperature, adding H₂O₂, reducing the solution pH, and preparing the special solution. Coupled with the fatigue test of pre-corrosion specimens, the corrosion damage characteristics and their influence on fatigue performance were analyzed. A numerical simulation method was developed to predict the fatigue life of pre-corrosion specimens, showing an average error of 13.82%. The S–N curves under different pre-corrosion cycles were also established. The research results show that using the test solution of 0.6 mol/L NaCl + 0.1 mol/L H₃PO₄-NaH₂PO₄ buffer solution + 1.0 mol/L H₂O₂ + 0.1 mL/500 mL concentrated hydrochloric acid for corrosion acceleration testing shows good corrosion acceleration. Moreover, the test methods ensure accuracy and reliability of the fatigue behavior evaluation of pre-corrosion specimens of the structure under actual service environments, offering a robust foundation for the material selection, corrosion resistance evaluation, and fatigue life prediction of marine structural components. Full article

In actual service, asphalt pavement is subjected to freeze–thaw cycles and ultraviolet radiation (UV) over the long term, which can easily lead to mixture aging, enhanced brittleness, and structural damage, thereby reducing pavement durability. This study focuses on the influence of freeze–thaw cycles and ultraviolet aging on the performance of recycled asphalt mixtures. Systematic indoor road performance tests were carried out, and a fatigue prediction model was established to explore the comprehensive effects of recycled asphalt pavement (RAP) content, environmental action (ultraviolet radiation + freeze–thaw cycle), and other factors on the performance of recycled asphalt mixtures. The results show that the high-temperature stability of recycled asphalt mixtures decreases with the increase in environmental action days, while higher RAP content contributes to better high-temperature stability. The higher the proportion of old materials, the more significant the environmental impact on the mixture; both the flexural tensile strain and flexural tensile strength decrease with the increase in environmental action time. When the RAP content increased from 30% to 50%, the bending strain continued to decline. With the extension of environmental action days, the decrease in the immersion Marshall residual stability and the freeze–thaw splitting strength became more pronounced. Although the increase in RAP content can improve the forming stability, the residual stability decreases, and the freeze–thaw splitting strength is lower than that before the freeze–thaw. Based on the fatigue test results, a fatigue life prediction model with RAP content and freeze–thaw cycles as independent variables was constructed using the multiple nonlinear regression method. Verification shows that the established prediction model is basically consistent with the change trend of the test data. The research results provide a theoretical basis and optimization strategy for the performance improvement and engineering application of recycled asphalt materials. Full article

Environmental comfort and air pollution are among the most important indicators for assessing the population’s quality of life in urban agglomerations. This study aims to explore long-term memory in air pollution time series by analyzing the dynamics of the Hurst exponent and evaluating the predictability index. This type of statistical pre-forecast analysis is essential for developing accurate forecasting models for such time series. The effectiveness of air quality monitoring systems largely depends on the precision of these forecasts. The Ekibastuz coal-mining center, which houses one of the largest coal-fired power stations in Kazakhstan and the world, with a capacity of about 4000 MW, was chosen as an example for the study. Data for the period from 1 March 2023 to 31 December 2024 were collected and analyzed at the Ekibastuz coal-fired power station. During the specified period, 14 indicators (67,527 observations) were collected at 10 min intervals, including mass concentrations of CO, NO, NO₂, SO₂, PM_2.5, and PM₁₀, as well as current mass consumption of CO, NO, NO₂, SO₂, dust, and NO_x. The detrended fluctuation analysis of a time series of air pollution indicators was used to calculate the Hurst exponent and identify long-term memory. Changes in the Hurst exponent in regards to dynamics were also investigated, and a predictability index was calculated to monitor emissions of pollutants in the air. Long-term memory is recorded in the structure of all the time series of air pollution indicators. Dynamic analysis of the Hurst exponent confirmed persistent time series characteristics, with an average Hurst exponent of about 0.7. Identifying the time series plots for which the Hurst exponent is falling (analysis of the indicator of dynamics), along with the predictability index, is a sign of an increase in the influence of random factors on the time series. This is a sign of changes in the dynamics of the pollutant release concentrations and may indicate possible excess emissions that need to be controlled. Calculating the dynamic changes in the Hurst exponent for the emission time series made it possible to identify two distinct clusters corresponding to periods of persistence and randomness in the operation of the coal-fired power station. The study shows that evaluating the predictability index helps fine-tune the parameters of time series forecasting models, which is crucial for developing reliable air pollution monitoring systems. The results obtained in this study allow us to conclude that the method of trended fluctuation analysis can be the basis for creating an indicator of the level of air pollution, which allows us to quickly respond to possible deviations from the established standards. Environmental services can use the results to build reliable monitoring systems for air pollution from coal combustion emissions, especially near populated areas. Full article

Timber and other biobased materials store carbon that has been captured from the atmosphere during photosynthesis and plant growth. The estimation of these biogenic carbon stocks in the harvested wood products (HWP) pool has received increasing attention since its inclusion in greenhouse gas reporting by the IPCC. It is of particular interest for long service life products such as timber in buildings; however, some aspects require further thought—in particular the handling of service lives as opposed to half-lives. The most commonly used model for calculating changes in the HWP pool uses first order decay based on half-lives. However other approaches are based on average service lives and estimates of residence times in the product pool, enabling different mathematical functions to be used. This paper considers the evolution of the two concepts and draws together data from a wide range of sources to consider service life estimation, which can be either related to design life or practical observations such as local environmental conditions, decay risk or consumer behaviour. As an increasing number of methods emerge for calculating HWP pool dynamics, it is timely to consider how these numerical inputs from disparate sources vary in their assumptions, calculation types, accuracy and results. Two groups are considered: half-lives for first order decay models, and service life and residence time population distributions within models based on other functions. A selection of examples are drawn from the literature to highlight emerging trends and discuss numerical constraints, data availability and areas for further study. The review indicated that issues exist with inconsistent use of nomenclature for half-life, average service life and peak flow from the pool. To ensure better sharing of data between studies, greater clarity in reporting function types used is required. Full article

Background: Caregivers of children often encounter barriers when accessing pediatric healthcare services. These challenges highlight the need for digital innovations to improve accessibility and efficiency in pediatric outpatient care. Objectives: This study aimed to design, implement, and pilot evaluate the Smart Well Child Center application in conjunction with enhancements to the Pediatric Outpatient Department. Methods: This study employs a mixed-methods research approach. The application was developed following the system development life cycle (SDLC) process, and its performance was subsequently evaluated. Additionally, its effectiveness in real-world settings was assessed through a satisfaction survey completed by 85 child caregivers. The results were summarized using the mean and standard deviation, and satisfaction levels were compared using paired t-test and repeated measures ANOVA. Results: The findings reveal that caregivers face significant challenges, including financial burdens related to travel, prolonged wait times, and difficulties accessing healthcare services. In response, the application was designed to incorporate key functionalities. Within the pre-consultation self-assessment module, caregivers can complete evaluations and receive recommendations directly through the application. Furthermore, the service procedure flowchart was restructured to seamlessly integrate these digital innovations, thereby enhancing the overall healthcare experience. The evaluation results indicate that the application achieved high performance ratings across all assessed dimensions (4.06 ± 0.77). Additionally, caregivers reported a substantial increase in satisfaction levels both immediately after implementation (4.58 ± 0.57) and one month afterward (4.59 ± 0.33). Conclusions: Given these findings, it is recommended that the hospital fully adopt the Smart Well Child Center application to improve healthcare accessibility and reduce patient wait times. Future research should assess the long-term impact of the intervention on both caregiver outcomes and healthcare professional workflow, satisfaction, and system usability, to inform broader implementation strategies. Full article

In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems. Full article

Introduction: Visual impairment impacts millions of people around the world, with the vast majority of problems being treatable. Disadvantaged communities are unable to utilize the same resources to treat these problems due to a lack of knowledge or resources, in addition to the presence of barriers preventing access. The objective of this paper is to assess eye health awareness and evaluate the barriers for individuals from disadvantaged communities in order to inform future interventions and increase access to care. Methods: This is a pilot study utilizing an online anonymous questionnaire designed to assess the demographics, eye health awareness, and access to eye care of community-based patients. A comprehensive literature review was also conducted using PubMed, Scopus, and Google Scholar to evaluate barriers to eye care and methods to improve community health outcomes. The primary goal was to improve understanding of eye health awareness and access in order to inform future strategies that can help in improving eye health awareness and service availability. Results: The results indicated that 61.2% of respondents believed that eye exams are very important, and only 7.7% of participants believed that regular eye exams are not important. The majority of participants (75%) agree that regular eye exams help prevent serious eye conditions and 84.5% believe that eye health can affect quality of life. 35.6% of participants reported they had their eyes checked by a healthcare professional within the last year, while 21.2% reported never having an eye exam. Although the majority of participants found access to eye care services in their community somewhat or very easy, 8.6% and 9.5% of participants found access difficult and very difficult, respectively. Even though 45.6% of participants reported not facing any barriers regarding access to eye care, the cost of services, long waiting times, and lack of nearby eye care providers were often cited as barriers from the remainder of the participants. Moving forward, local interventions such as mobile eye clinics, public health workshops, and telehealth are viable options to obtain an understanding of the community’s health status in addition to creating opportunities to educate and provide health screenings. Conclusion: The results indicate that although there is awareness of the importance of eye health for the majority of participants, there is still a sizable minority who have insufficient understanding. Barriers to healthcare such as cost, waiting times, and proximity to providers are common problems that are preventing many from seeking eye care. Future interventions should be created to increase access and literacy amongst the community through telehealth, mobile eye clinics, and public health workshops. Additional efforts should be taken by healthcare stakeholders to enhance care delivery, implement policies, and improve awareness. Full article

Green roofs are increasingly being adopted as sustainable, nature-based solutions for managing urban stormwater, mitigating the urban heat island effect, and saving energy in buildings. However, the long-term performance of their individual components—particularly filter geotextiles—remains understudied, despite their critical role in maintaining system functionality. The filter layer, responsible for preventing clogging of the drainage layer with fine substrate particles, directly affects the hydrological performance and service life of green roofs. While most existing studies focus on the initial material properties, there is a clear gap in understanding how geotextile filters behave after prolonged exposure to real-world environmental conditions. This study addresses this gap by assessing the mechanical and structural integrity of geotextile filters after five years of use in both extensive and intensive green roof systems. By analyzing changes in surface morphology, microstructure, and porosity through tensile strength tests, digital imaging, and scanning electron microscopy, this research offers new insights into the long-term performance of geotextiles. Results showed significant retention of tensile strength, particularly in the machine direction (MD), and a 56% reduction in porosity, which may affect filtration efficiency. Although material degradation occurs, some geotextiles retain their structural integrity over time, highlighting their potential for long-term use in green infrastructure applications. This research emphasizes the importance of material selection, long-term monitoring, and standardized evaluation techniques to ensure the ecological and functional resilience of green roofs. Furthermore, the findings contribute to advancing knowledge on the durability and life-cycle performance of filter materials, promoting sustainability and longevity in urban green infrastructure. Full article

Prefabricated unidirectional carbon fiber reinforced polymer (CFRP) composite strips are extensively used as a means of infrastructure rehabilitation through adhesive bonding to the external surface of structural concrete elements. Most data to date are from laboratory tests ranging from a few months to 1–2 years providing an insufficient dataset for prediction of long-term durability. This investigation focuses on the assessment of the response of three different prefabricated CFRP systems exposed to water, seawater, and alkaline solutions for 5 years of immersion in deionized water conducted at three temperatures of 23, 37.8 and 60 °C, all well below the glass transition temperature levels. Overall response is characterized through tensile and short beam shear (SBS) testing at periodic intervals. It is noted that while the three systems are similar, with the dominant mechanisms of deterioration being related to matrix plasticization followed by fiber–matrix debonding with levels of matrix and interface deterioration being accelerated at elevated temperatures, their baseline characteristics and distributions are different emphasizing the need for greater standardization. While tensile modulus does not degrade appreciably over the 5-year period of exposure with final levels of deterioration being between 7.3 and 11.9%, both tensile strength and SBS strength degrade substantially with increasing levels based on temperature and time of immersion. Levels of tensile strength retention can be as low as 61.8–66.6% when immersed in deionized water at 60 °C, those for SBS strength can be 38.4–48.7% at the same immersion condition for the three FRP systems. Differences due to solution type are wider in the short-term and start approaching asymptotic levels within FRP systems at longer periods of exposure. The very high levels of deterioration in SBS strength indicate the breakdown of the materials at the fiber–matrix bond and interfacial levels. It is shown that the level of deterioration exceeds that presumed through design thresholds set by specific codes/standards and that new safety factors are warranted in addition to expanding the set of characteristics studied to include SBS or similar interface-level tests. Alkali solutions are also shown to have the highest deteriorative effects with deionized water having the least. Simple equations are developed to enable extrapolation of test data to predict long term durability and to develop design thresholds based on expectations of service life with an environmental factor of between 0.56 and 0.69 for a 50-year expected service life. Full article

Laser surface treatment (LST) has been employed on ductile cast iron (DCI) parts to obtain a good performance and a long service life. There is a need to understand the laser surface-treated microstructure and hardening ability of DCIs with different matrix structures to facilitate the scientific selection of DCI for specific applications. In this study, a Laserline-LDF3000 fiber-coupled semiconductor laser with a rectangular spot was used to harden the surface of ductile cast irons (DCIs) with different pearlite contents. The hardened surface layer having been solid state transformed (SST) and with or without being melted–solidified (MS) was obtained under various process parameters. The microstructure, hardened layer depth, hardness and hardening ability were analyzed and compared as functions of pearlite contents and laser processing parameters. The results show that the MS layers on the DCIs with varied pearlite contents have similar microstructures consisting of fine transformed ledeburite, martensite and residual austenite. The microstructure of the SST layer includes martensite, residual austenite and ferrite, whose contents vary with the pearlite content of DCI. In the pearlite DCI, martensite and residual austenite are found, while in ferrite DCI, there is only a small amount of martensite around the graphite nodule, with a large amount of unaltered ferrite remaining. There exists no significant difference in the hardness of MS layers among DCIs with different pearlite contents. Within the SST layer, the variation in the hardness value in the pearlite DCI is relatively small, but it gradually decreases along the depth in the ferrite DCI. In the transition region between the SST layer and the base metal (BM), there is a steep decrease in hardness in the pearlite DCI, but it decreases gently in the ferrite DCI. The depth of the hardened layer increases slightly with the increase in the pearlite content in the DCI; however, the effective hardened depth and the hardening ability increase significantly. When the pearlite content of DCI increases from 10% to 95%, its hardening ability increases by 1.1 times. Full article

Vegetable intake among military Service Members (SMs) is well below public health guidelines, with only 12.9% meeting the Dietary Guidelines for Americans (DGAs). Low vegetable consumption negatively impacts diet quality as measured by the Healthy Eating Index (HEI), and poses risks to health and performance. Given the high physical and mental demands of military life, improving diet quality, including through increased vegetable intake, is crucial for optimizing health and readiness. Providing meal kits may help improve vegetable intake by reducing access-related barriers for SMs living or working on a military base. Furthermore, the addition of spices and herbs is a readily modifiable accompanying approach to address taste-related barriers and increase intake that has shown promise in other populations with poor diet quality. Background/Objectives: This study aimed to evaluate whether heat-and-serve meal kits with spices and herbs could increase vegetable intake and liking among active-duty SM by simultaneously targeting barriers to healthy eating and modifiable sensory factors. Methods: Conducted at Naval Support Activity Bethesda, the study randomly distributed heat-and-serve meal kits (n = 400) featuring either spiced (n = 200) or plain versions (n = 200) of four vegetables (broccoli, carrots, cauliflower, and kale). Each kit contained a quick response (QR) code for participants to upload post-consumption photos and rate vegetable liking on a nine-point Likert scale. Food photography (SmartIntake^®) was used to estimate vegetable consumption. Paired t-tests were used to determine differences between the intake of plain and spiced vegetables. Results: Intake of the heat-and-serve vegetables was very high for both the spiced and plain preparations (1.73 out of 2 cups, 87%). There was minimal difference (p = 0.87) between the consumption of spiced (1.75 cups) and plain (1.725 cups) vegetables, suggesting that both were well accepted. Overall, convenient and accessible meal options, alongside sensory-driven strategies, appear to improve some barriers to vegetable consumption in SM populations. Conclusions: Future studies should explore long-term outcomes and adaptability across different military environments, while considering additional factors, including convenience and time constraints, that influence dietary choices in the military. Full article

With rapid urbanisation and population growth, transport equity has become a critical issue, especially when considering the mobility gap among people with disability. Understanding the dynamics between the quality of public transport services and the mobility of people with disability is critical to fostering transport equity and inclusivity. This research investigated service quality barriers encountered by people with disability in the City of Tshwane while navigating the city’s public transport system. A quantitative research method was employed, using a structured questionnaire to collect primary data from people with mobility, vision, and hearing disability. The responses were analysed using descriptive statistics, exploratory factor analysis (EFA), and multiple comparison tests to uncover trends and differences among the groups. The findings reveal that people with all types of disability experience considerable service quality challenges. Long travel and waiting times are major concerns amongst people with mobility disability, which lead to heightened inconvenience. The research also found a pervasive lack of transport information, which aggravates the difficulties faced by people with disability. Lastly, the absence of announcements of stops further compounds the challenges experienced by people with a vision disability. The study emphasises the need for high quality public transport services that prioritise accessible and inclusive public transport that caters to all. Addressing service quality barriers in public transport promotes participation in socio-economic life among people with disability. This study contributes to the broader goal of transport equity and highlights the importance of inclusive transport policies and the priority areas that require consideration in a typical developing country. Full article

Lithium-ion batteries are an indispensable component of numerous contemporary applications, such as electric vehicles and renewable energy systems. However, accurately predicting their remaining service life is a significant challenge due to the complexity of degradation patterns and time series data. To tackle these challenges, this study introduces a novel Multi-Scale Time Attention (MSTA) mechanism designed to enhance the modeling of both short-term fluctuations and long-term degradation trends in battery performance. This mechanism is integrated with the Bidirectional Gated Recurrent Unit (BiGRU) to develop the BiGRU-MSTA framework. This framework effectively captures multi-scale temporal features and enhances prediction accuracy, even with limited training data. The BiGRU-MSTA model is evaluated via two sets of experiments. First, using the NASA lithium-ion battery dataset, the experimental results demonstrate that the proposed model outperforms the LSTM, BiGRU, CNN-LSTM, and BiGRU-Attention models across all evaluation metrics. Second, experiments conducted on the CALCE dataset not only examine the impact of varying time scales within the MSTA mechanism but also compare the model against state-of-the-art architectures such as Transformer and LSTM–Transformer. The findings indicate that the BiGRU-MSTA model exhibits significantly superior performance in terms of prediction accuracy and stability. These experimental results underscore the potential of the BiGRU-MSTA model for application in battery management systems and sustainable energy storage solutions. Full article