Search Results (29)

The traditional approach that separates remaining useful life prediction from maintenance strategy design often fails to support efficient decision-making. Effective maintenance requires a comprehensive consideration of prediction accuracy, cost control, and equipment safety. To address this issue, this paper proposes a multi-objective maintenance optimization method based on stochastic modeling. First, a multi-sensor data fusion technique is developed, which maps multidimensional degradation signals into a composite degradation state indicator using evaluation metrics such as monotonicity, tendency, and robustness. Then, a linear Wiener process model is established to characterize the degradation trajectory of equipment, and a closed-form analytical solution of its reliability function is derived. On this basis, a multi-objective optimization model is constructed, aiming to maximize equipment safety and minimize maintenance cost. The proposed method is validated using the NASA aircraft engine degradation dataset. The experimental results demonstrate that, while ensuring system reliability, the proposed approach significantly reduces maintenance costs compared to traditional periodic maintenance strategies, confirming its effectiveness and practical value. Full article

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR. Full article

Using descriptive and inferential techniques together with simplified metrics derived from the ecological discipline, we offer a long-term investigation of the Environmental Sensitive Area Index (ESAI) as a proxy of land degradation vulnerability in Italy. This assessment was specifically carried out on a decadal scale from 1960 to 2020 at the province (NUTS-3 sensu Eurostat) level and benefited from a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic (‘what … if’) approach. The spatial distribution of the ESAI was investigated at each observation year (1960, 1970, 1980, 1990, 2000, 2010, 2020, 2030) calculating descriptive statistics (central tendency, variability, and distribution shape), deviation from normality, and the increase (or decrease) in diversification in the index scores. Based on nearly 300 thousand observations all over Italy, provinces were considered representative spatial units because they include a relatively broad number of ESAI measures. Assuming a large sample size as a pre-requisite for the stable distribution of the most relevant moments of any statistical distribution—because of the convergence law underlying the central limit theorem—we found that the ESAI scores have increased significantly over time in both central values (i.e., means or medians) and variability across the central tendency (i.e., coefficient of variation). Additionally, ecological metrics reflecting diversification trends in the vulnerability scores delineated a latent shift toward a less diversified (statistical) distribution with a concentration of the observed values toward the highest ESAI scores—possibly reflecting a net increase in the level of soil degradation, at least in some areas. Multiple exploratory techniques (namely, a Principal Component Analysis and a two-way hierarchical clustering) were run on the two-way (data) matrix including distributional metrics (by columns) and temporal observations (by rows). The empirical findings of these techniques delineate the consolidation of worse predisposing conditions to soil degradation in recent times, as reflected in a sudden increase in the ESAI scores—both average and maximum values. These trends underline latent environmental dynamics leading to an early desertification risk, thus representing a valid predictive tool both in the present conditions and in future scenarios. A comprehensive scrutiny of past, present, and future trends in the ESAI scores using mixed (parametric and non-parametric) statistical tools proved to be an original contribution to the study of soil degradation in advanced economies. Full article

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) not only prevents battery system failure but also promotes the sustainable development of the energy storage industry and solves the pressing problems of industrial and energy crises. Because of the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, the prediction precision and generalization performance of a single model can be poor. This article proposes a novel RUL prediction based on data pre-processing methods and the CNN-LSTM-ASAN framework. The model is based on a fusion technique for optimizing the tandem fusion of the Convolutional Neural Network (CNN) and the Long Short-Term Memory Network (LSTM). Firstly, the improved adaptive noise fully integrates empirical mode decomposition (ICEEMDAN) and the Pearson correlation coefficient (PCC), which are used to estimate the global deterioration tendency component and the local capacity restoration component, to reconstruct the dataset and eliminate the noise. Then, the Adaptive Sparse Attention Network (ASAN) is added in the model construction stage to improve the training efficiency of the model. The reconstructed degraded data are features extracted by the CNN-LSTM-ASAN model. Finally, the proposed method is validated against models such as DCLA, using the NASA public datasets, the CALCE public datasets, and the self-use datasets. And the results show that the root mean square error (RMSE) of the model is below 1.5%. Full article

Sufficient light and high UV intensity pose significant challenges to the long-term performance of polymeric geonet materials for slope-protection structures. This study investigates strength degradation under the effect of UV radiation; five different types of geonets were selected, which can be categorized as polyamide (PA), polypropylene (PP), and polyethylene (PE) materials. A comprehensive experimental investigation was performed, including tension strength, peer strength, artificially accelerated aging, and SEM tests, to further establish a service life prediction method used for slope-protection design. The results showed that the tension strength, percentage of breaking elongation, and peer strength all depict a descending trend with aging-elapsed time, especially in the early 600 h. The decreasing tendency of these mechanical properties’ magnitude differed in the diversity of direction and material type. Significant changes have been generated on the geonet surface after aging; materials with smooth surfaces exhibit a strong ability against strength degradation. Fitting results affirmed the predictive technique as a useful engineering tool for tension strength assessments, offering guidelines for using and designing of geonets for slope-protection structures. Full article

Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time. Full article

This work aimed to evaluate the impacts caused by extreme frost events in an ecological restoration area. We grouped the species in three ways: (1) type of trichome coverage; (2) shape of the seedling crown; and (3) functional groups according to the degree of damage caused by frost. The variables of the restored area and species characteristics were selected to be subjected to linear generalization analysis models (GLMs). A total of 104 individuals from seven species were sampled. The most affected species were Guazuma ulmifolia Lam. (98% of leaves affected), followed by Cecropia pachystachia Trécul and Hymenea courbaril L. (both 97%), Inga vera Willd. (84%), and Senegalia polyphylla (DC.) Britton & Rose with 75%. Tapirira guianensis Aubl. was considered an intermediate species, with 62% of the crown affected. Only Solanum granulosoleprosum Dunal was classified as slightly affected, with only 1.5% of leaves affected. With the GLM analysis, it was verified that the interaction between the variables of leaf thickness (Χ² = 37.1, df = 1, p < 0.001), trichome coverage (Χ² = 650.5, df = 2, p < 0.001), and leaf structure culture (Χ² = 54.0, df = 2, p < 0.001) resulted in a model with high predictive power (AIC = 927,244, BIC = 940,735, Χ² = 6947, R² = 0.74, p < 0.001). Frost-affected crown cover was best explained by the interaction between the three functional attributes (74%). We found that there is a tendency for thicker leaves completely covered in trichomes to be less affected by the impact of frost and that the coverage of the affected crown was greatly influenced by the coverage of trichomes. Seedlings with leaves completely covered in trichomes, thicker leaves, and a funneled or more open crown structure are those that are most likely to resist frost events. The success of ecological restoration in areas susceptible to extreme events such as frost can be predicted based on the functional attributes of the chosen species. This can contribute to a better selection of species to be used to restore degraded areas. Full article

Background: According to statistics, worldwide, the number of young persons diagnosed with idiopathic scoliosis has tripled in the last 10 years. This tendency seems to be related to the development of technological devices that induce vicious postures. Specialized literature shows that the predicted evolution will lead to a tripling of the population affected by scoliosis by 2050. Associated complications can be most varied, with functional or respiratory and cardiac impairment being the most severe. The purpose of this study is to objectify the effect of associating Schroth therapy with general elements of global postural reeducation (GPR) therapy in the treatment of scoliosis using electromyography, scoliosis assessment scales, and sleep quality evaluation. The present study is addressed to scoliotic patients. Methods: In order to assess the muscle imbalance installed in scoliosis, we have used SEMG, while Epworth, Baecke, and SAQ scales assessed sleepiness, physical activity levels, and self-perception of the scoliotic patient. Results: After performing a therapeutic protocol that combines Schroth and global postural reeducation (GPR) exercises, an improvement of the functional status was observed for the scoliotic patients. The statistical analysis presents a favorable symmetry index during flexion (p = 0.042), a significant difference in the Epworth score (p = 0.002), as well as a significant difference in the SAQ2 score (p = 0.049). Conclusion: Early detection of scoliosis prevents functional degradation. On the other hand, developing an adequate therapeutic protocol leads to an improved functional status and increased life quality. Full article

Supplementation with saccharomyces cerevisiae culture products (SCs) has shown effectiveness in alleviating or improving the health and productivity of ruminants at a high risk of digestive and metabolic problems as a consequence of their physiological state and feeding system (i.e., Holstein cows during peak lactation). However, the effects of SC supplementation on ruminal digestion and microbial population are not yet well-understood. Hence, this study aimed to contribute to the knowledge of the effects of in vitro SC supplementation on ruminal nutrient digestibility and microbial community. This study included three treatment groups: a control group (CON, 0% SC proportion of substrate DM), a low-dose SC group (LSC, 0.10% SC proportion of substrate DM), and a high-dose SC group (HSC, 0.30% SC proportion of substrate DM). The SC product contained 7.0 × 10⁹ CFU/g. After 48 h of fermentation at 39 °C, the incubation fluid and residue were collected to measure the ruminal nutrient digestibility and microbial community. The results showed that supplemental SC tended (p = 0.096) to increase DM digestibility due to an increase (9.6%, p = 0.03) in CP digestibility and via a tendency (0.05 < p < 0.08) to increase the fiber fraction. Additionally, the 16S rRNA high-throughput sequencing results revealed that the richness and diversity of the microbiota were unchanged by SC supplementation, while the abundances of Spirochaetes, Tenericutes, and Spirochaetaceae were lower in the SC groups than those in the CON group (p < 0.05). At the genus level, the abundances of Selenomonas and Succinivibrio were increased by SC supplementation (p < 0.05), while SC supplementation decreased the abundances of Ruminococcaceae_UCG-014 and Treponema_2 (p < 0.05). Furthermore, the predicted function of the microbiota showed that carbohydrate metabolism and lipid metabolism were enriched in the SC groups compared with the CON group (p < 0.05). Except for the increases in ADF digestibility (p = 0.032) and pH (p = 0.076) at 0.30%, the supplemental level did not result in additional effects. In summary, our results demonstrate that SC supplementation could improve ruminal nutrient degradation digestibility and alter microbiota composition. Full article

The study aimed to forecast and monitor drought over degraded land based on monthly precipitation using the Seasonal Autoregressive Integrated Moving Average (SARIMA) approach. Several statistical parameters to select the most appropriate model were applied. The results indicate that the SARIMA (1,1,1) (0,1,1)12 is the most suitable for 1981 to 2019 CHIRPS time-series data. The combination of precipitation data and this approved model will subsequently be applied to compute, assess, and predict the severity of drought in the study area. The forecasting performance of the generated SARIMA model was evaluated according to the mean absolute percentage error (15%), which indicated that the proposed model showed high performance in forecasting drought. The forecasting trends showed adequate results, fitting well with the historical tendencies of drought events. Full article

The surface texture of asphalt pavement is of enormous importance to skid resistance. To investigate the degradation tendency of surface texture related to the skid resistance, four years of sensor measured texture depth (SMTD) panel data measured from 19 pavement structures including 4 types of surface asphalt layer are used to develop a surface texture prediction model. The panel data-based prediction model takes into account the dependence on time scale, diversified road sections, traffic factors, environmental factors, and the Bailey method-based aggregate gradation parameters. The regression and prediction capability of the surface texture model is evaluated from both short and long-term perspective. The results indicated that the random-effects model is the most suitable form to characterize the degradation of surface texture. The cumulative standard axle loads (CSAL), monthly average humidity (MAH), and the Bailey method-based aggregate gradation parameters (the coarse aggregate ratio (CA), the fine aggregate coarse ratio (FAC), and the fine aggregate fine ratio (FAF)) have significant influences on the sensor measured texture depth. The time scale of the input sensor measured texture depth (SMTD) data has influence on the accuracy of the surface texture prediction model; therefore, long-term data series can ensure the robustness of long-term prediction. The results of this study can benefit the material design, construction, maintenance of asphalt pavement, and its evaluation pavement for longer service life. Full article

Reduction of area (RA) measurement in a hot ductility test is widely used to define the susceptibility of surface crack of cast steel, but the test is complex because it entails processes such as specimen fabrication, heat treatment, tensile testing, and analysis. As an alternative, this study proposes a model that can predict RA. The model exploits the relationship between precipitation and RA behavior, which has a major effect on hot ductility degradation in high-Mn steels. Hot ductility tests were performed using four grades of high-Mn steels that had different V-Mo compositions, and the RA behavior was compared with the precipitation behavior obtained from a time-temperature-precipitation (TTP) graph. The ductility deterioration of high-Mn steels shows a tendency to start at the nose temperature T_N at which precipitation is most severe. Using this relationship, we developed a model to predict the hot ductility degradation temperature of high-Mn steels. T_N was calculated using J-matpro software (version 12) for 1500 compositions of high-Mn steels containing the precipitating elements V, Mo, Nb, and Ti, and by applying this to a deep neural network (DNN), then using the result to develop a model that can predict T_N for various compositions of high-Mn steel. The model was tested by comparing its predicted RA degradation temperature with RAs extracted from reference data for five high-Mn steels. In all five steels, the temperature at which the RA decreases coincided with the value predicted by the DNN model. Use of this model can eliminate the cost and time required for hot ductility testing to measure RA. Full article

Monitoring vegetation growth and exploring the driving force behind it is very important for the study of global climate change and ecological environmental protection. Based on Normalized Difference Vegetation Index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS), meteorological and nighttime lights data from 2001 to 2020, this study uses the Theil–Sen slope test, Mann–Kendall significance test, Rescaled Range Analysis and partial correlation analysis to investigate the evolution of NDVI in the Minjiang River Basin, China, from three aspects: the spatial-temporal variation characteristics and future trend prediction of NDVI, the variation of climate and human activities in the basin, and the influences of different driving forces on NDVI. The results show that the average NDVI in the growing season was 0.60 in the Minjiang River Basin in the past twenty years, with a growth rate of 0.002/a. The area with high NDVI growth accounts for 66.02%, mainly distributed in the southeast, the central and the northern low-altitude areas of the basin. Combined with the Hurst index, the NDVI in the Minjiang River Basin exhibits an anti-sustainable tendency, with 63.22% of the area changing from improvement to degradation in the future. Meanwhile, the spatial differentiation of NDVI in the Minjiang River Basin is mainly affected by topography and climate factors, followed by human activities. This study not only provides scientific guidelines for the vegetation restoration, soil and water conservation and sustainable development of the Minjiang River Basin, but also provides a scientific basis for making informed decisions on ecological protection under the impacts of climate change and human activities. Full article

The objective of this study was to investigate the dynamics of the urinary collagen type II C-terminal cleavage neoepitope (uC2C) before and after total knee replacement (TKR) in rapid knee OA progressors. C2C in the urine was measured by IBEX-uC2C assay in 86 patients (mean age: 59.9 years) with symptomatic knee OA (kOA) undergoing TKR, assessed before surgery and 3 and 12 months after. The patients’ condition was determined by self-assessment questionnaires, by lower limb performance tests, and by radiography. In the preoperative period, the uC2C level was significantly higher in females than in males, and was associated with the radiographic severity of kOA. A weak correlation between the C2C and knee pain was observed in the whole group and in males, but not in females. The individual dynamics of uC2C after TKR were heterogenic. In general, uC2C increased three months after TKR, but fell to the preoperative level after 12 months. A higher preoperative uC2C implied the tendency to diminish as a result of TKR, and vice versa. TKR did not stop the degradation of Coll2 in the tissues in the majority of cases. The pre-TKR uC2C predicts the postoperative uC2C level. The uC2C dynamic seems to be sex-specific, so it could be considered a prospective pre- and post-TKR biomarker for progressive kOA. Full article

The utilization of composite materials is increasing at a growing rate in almost all types of products, due to their strength-to-stiffness ratio. From this perspective, natural waste composites, i.e., wood waste composites, have also been investigated for their effective and sustainable employment. This paper deals with the application of hard and soft wood waste (i.e., acacia and cedar wood) with epoxy resin polymer to develop high strength and thermally stable wood composites. Mechanical (tensile, flexural, impact, and hardness) and thermal properties of samples are studied using Differential Scanning Calorimeter (DSC) and Thermo Gravimetric Analysis (TGA), respectively. The properties are evaluated by varying the type of wood waste and its percentage by weight. Based on the Taguchi Orthogonal Array Mixture Design, eighteen experiments are investigated. Analysis of variance (ANOVA) results show that wood waste type and wood waste content have a significant effect on all mechanical properties. From the TGA analysis, it is predicted that both types of wood waste composites exhibit similar thermal-induced degradation profiles in terms of the initial and final degradation temperatures. From the DSC results, higher glass transition temperature T_g is detected in 10% of the hardwood waste composite, and a reducing tendency of glass transition temperature T_g is observed with exceeding wood waste content. Moreover, hardwood waste at 10% demonstrated improved decomposition temperature T_d, due to strong adhesion between waste and matrix. Full article