Search Results (56,129)

This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high costs or inefficiency in resource allocation. CBM leverages system reliability models in conjunction with component degradation data to dynamically establish maintenance thresholds, optimizing resource utilization while minimizing operational risks and repair costs. Research has expanded from single-component degradation systems to multi-component systems, leveraging degradation models and optimization algorithms to propose strategies addressing multi-level control limits, economic dependencies, and task constraints. Recent studies emphasize multi-component interactions, incorporating structural influences, imperfect repairs, and economic correlations into maintenance planning. Despite progress, challenges persist in modeling coupled degradation mechanisms and coordinating maintenance decisions for interdependent components. Future research directions should encompass adaptive learning strategies for dynamic degradation processes, such as those employed in intelligent agents for real-time environmental adaptation, and the incorporation of intelligent predictive technologies to enhance system performance and resource utilization. Full article

Escherichia coli biofilms are implicated in the development of persistent infections and increased antibiotic resistance, posing a significant challenge in clinical settings. These biofilms enhance bacterial survival by forming protective extracellular matrices, rendering conventional treatments less effective. Serrapeptase (SPT), a proteolytic enzyme, has emerged as a potential anti-biofilm agent due to its ability to degrade biofilm components and disrupt bacterial adhesion. In this study, we report the inhibitory effect of SPT against E. coli biofilm and its effect on key virulence factors. In vitro assays, including crystal violet staining, optical and fluorescence microscopy, and viability measurements, revealed the dose-dependent inhibition of biofilm formation (IC₅₀ = 14.2 ng/mL), reduced biofilm (−92%, 500 ng/mL) and planktonic viability (−45%, 500 ng/mL), and a marked loss of amyloid curli fibers. SPT treatment also lowered the levels of key virulence factors: cellular and secreted lipopolysaccharides (−76%, 8 ng/mL; −94%, 32 ng/mL), flagellin (−63%, 8 ng/mL), and peptidoglycan (−29%, 125 ng/mL). Mechanistically, SPT induced a phosphate-dysregulating response: secreted alkaline phosphatase activity rose (+70%, 125 ng/mL) while cellular DING/PstS proteins declined (−84%, 64 ng/mL), correlating strongly with biofilm inhibition. In silico docking further suggests direct interactions between SPT and the curli subunits CsgA and CsgB, potentially blocking fiber polymerization. Together, these findings position SPT as a powerful non-antibiotic biofilm disruptor against E. coli, offering a promising strategy to undermine bacterial persistence and resistance by targeting both structural matrix components and metabolic regulatory pathways. Full article

The increasing frequency of wildfires in larch forests across the discontinuous permafrost zone of Eastern Eurasia heightens the vulnerability of soil organic matter (SOM) under a warming climate. However, post-fire SOM thermal stability in this frequently burned forest region remain poorly understood. We assessed the long-term effects of wildfire on SOM structure and thermal stability in burned and unburned larch forests using complex analytical approaches: pyrolysis–gas chromatography/mass spectrometry (TMAH-py-GC/MS) and thermogravimetry/differential thermal analysis (TG/DTA). The focus was on the upper mineral soil horizon, where fire impacts may persist for decades. Sixteen years post-fire, total carbon content did not differ significantly between burned and control soils. Nonetheless, the molecular composition and thermal properties of SOM showed marked post-fire alterations. Burned soils exhibited higher proportions of lignin-derived compounds and reduced levels of short-chain fatty acid methyl esters. A lower degradation temperature (T50) and a higher thermal mass loss of labile fractions indicate a decrease in the thermal stability of SOM after fire. Our study shows that recurrent forest fires in larch forests of the Russian Far East decrease the thermal stability of soil organic matter, thereby increasing its vulnerability to subsequent fire degradation. Full article

Land degradation in drylands represents a critical environmental challenge, with persistent bare soil serving as a key indicator of ecosystem vulnerability, including in the Caatinga biome. This study maps and analyzes the spatial and temporal dynamics of persistent bare soils over three decades using multi-temporal remote sensing data. We applied Spectral Mixture Analysis (SMA), temporal metrics, and machine learning classifiers within Google Earth Engine to process long-term Landsat datasets and to derive the Normalized Difference Fraction Index Adjusted (NDFIa). The results indicate a widespread increase in bare soil, with over 63% of mapped hexagons showing expansion, particularly in the São Francisco Basin. Peaks in soil exposure coincided with severe drought events, highlighting the link between climate variability and land degradation. Moreover, abandoned agricultural lands and pasturelands emerged as the dominant contributors to persistent bare soils. These findings reinforce the need for targeted policies to mitigate land degradation and to promote sustainable land management in semi-arid ecosystems. This research provides a robust framework for long-term environmental monitoring in drylands by integrating satellite data with advanced analytical techniques. These advancements support more effective land management and conservation strategies in semi-arid ecosystems. Full article

Styles are invasive medical devices that are visible on images and are used in several medical specialties, including cardiology, neurology, orthopaedics, anaesthesia, oto-rhino-laryngology (ENT), and dentistry. With their thin and flexible design, they allow for the optimal positioning and precise guidance of medical devices such as nerve stimulation, defibrillation, electrode positioning, and catheter insertion. Generally, they are made of stainless steel, offering a combination of rigidity and flexibility. The aim of this study is to evaluate the sensitivity of austenitic stainless steel 304L used for the manufacture of J-stylets in uniform, pitting, crevice, and intergranular corrosion. We follow the manufacturing process step by step in order to analyse the risks of corrosion sensitisation and the cumulative effects of various forms of degradation, which could lead to a significant release of metal cations. Another objective of this study is to determine the optimal heat treatment temperature to minimise sensitivity to the intergranular corrosion of 304L,steel. Uniform corrosion: Two samples were taken at each stage of the manufacturing process (eight steps in total), in the form of rods. After one hour of immersion, potentiodynamic polarisation curves were plotted up to ± 400 mV vs. SCE. A coulometric analysis was also performed by integrating the anode zone between E (i = 0) and +400 mV vs. SCE. The values obtained by integration are expressed as mC/cm². The test medium used was a simulated artificial plasma solution (9 g/L NaCl solution). Intergranular corrosion: (a) Chemical test: Thirty rod-shaped samples were tested, representing the eight manufacturing steps, as well as heat treatments at 500 °C, 620 °C, and 750 °C, in accordance with ASTM A262 (F method). (b) Electrochemical Potentiokinetic Reactivation (EPR) according to ASTM G108–94 (2015). Two samples were tested for each condition: without heat treatment and after treatments at 500 °C, 620 °C, and 750 °C. Release of cations: The release of metal ions was evaluated in the following two media: artificial sweat, according to EN 1811:2011+A1:2015, and bone plasma, according to the Fitton-Jackson and Burks-Peck method. Six samples that had been heat-treated at 500 °C for one hour were analysed. Results, discussions: (a) Analysis of the polarisation curves revealed significant disturbances in the heat treatment steps, as well as the μC/cm² quantities, which were between 150,000 and 400,000 compared to only 40–180 for the other manufacturing steps; (b) Electrochemical Potentiokinetic reactivation (EPR) tests indicated that the temperature of 500 °C was a good choice to limit 304L steel sensitisation in intergranular corrosion; and (c) the quantities of cations released in EN 1811 sweat were of the order of a few μg/cm² week, as for Fe: 2.31, Cr: 0.05, and Ni: 0.12. Full article

Extreme operating conditions in solar receivers of concentrated solar thermal power plants, such as high temperatures, intense irradiance, and thermal cycling, pose significant challenges for conventional sensors. Optical fibers offer a promising alternative for flux measurement in such environments, but their long-term performance and degradation mechanisms require detailed investigation and characterization. This work presents a proof of concept for high solar flux measurement by using optical fibers as photon-capturing elements and showcases the behavior and damage that these optical fibers undergo when exposed to relevant conditions, including temperatures over 600 °C and flux levels exceeding 400 kW/m². Three fiber configurations, including polyimide and gold-coated fibers, were tested at a high-flux solar simulator and analyzed via scanning electron microscopy to assess structural integrity and material degradation. Results reveal significant coating deterioration, fiber retraction, and thermal-induced stress effects, which impact measurement reliability. These findings provide essential insights for improving the durability and accuracy of optical fiber-based sensing technologies in concentrating solar energy. Full article

Under intensified global climate change and complex land use transitions, the Leaf Area Index (LAI) serves as a key ecological indicator to monitor vegetation responses to natural and anthropogenic factors. This study provided a comprehensive spatiotemporal diagnosis of the LAI and uniquely integrated remote sensing data with the Geodetector model to quantitatively assess both individual and interactive effects of natural and human drivers. Specifically, we analyzed LAI dynamics in the Jinsha River Basin from 2000 to 2023 using Sen’s Slope and Mann–Kendall tests, combined with Geodetector modeling to identify drivers and their interactions. Furthermore, ARIMA-based forecasting offered forward-looking insights to support land use planning and ecosystem resilience. Results revealed a fluctuating upward trend in LAI, with larger areas improving than degrading, and distinct seasonal and spatial patterns, with a notably higher LAI in southern regions. Elevation and temperature were the primary drivers, explaining 57% and 54% of spatial variation, respectively, with their combined effects further enhancing explanatory power. The future LAI trend appeared stable without significant changes. These findings demonstrated LAI’s utility for assessing land use change impacts and ecological sustainability, providing a scientific basis for land use optimization, ecological restoration, and sustainable regional development under the human–earth system framework. Full article

This study investigates the effect of copper (Cu) and silver (Ag) additions on the electrochemical behavior of the Fe40Al intermetallic alloy in artificial saliva, aiming to evaluate its potential for biomedical applications such as dental implants. Alloys with varying concentrations of Ag (0.5, 1.0, and 3.0 wt%) and Cu (1.0, 3.0, and 5.0 wt%) were synthesized and exposed to a biomimetic electrolyte simulating oral conditions. Electrochemical techniques, including open circuit potential (OCP), linear polarization resistance (LPR), potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS), were employed to assess corrosion performance. Results show that unmodified Fe40Al exhibits good corrosion resistance, attributed to the formation of a stable passive oxide layer. The addition of Cu, particularly at 3.0 wt%, significantly improved corrosion resistance, yielding lower corrosion current densities and higher polarization resistance and charge transfer resistance values, surpassing even 316L stainless steel in some metrics. Conversely, Ag additions led to a degradation of corrosion resistance, especially at 3.0 wt%, due to microstructural changes and the formation of metallic Ag precipitates, AgSCN, and galvanic cells, which promoted localized corrosion. EIS results revealed that Cu- and Ag-modified alloys developed less homogeneous and less protective passive layers over time, as indicated by increased double-layer capacitance (C_dl) and reduced constant phase element exponent (n_dl) values. Overall, the Fe40Al alloy shows intrinsic corrosion resistance in simulated physiological environments, and Cu additions can enhance this performance under controlled conditions. However, Ag additions negatively affect the protective behavior of the passive layer. These findings offer critical insight into the design of Fe-Al-based biomaterials for dental or biomedical applications where corrosion resistance and electrochemical stability are paramount. Full article

The Karnak Temples are considered one of Egypt’s most significant archaeological sites, dating back to the Middle Kingdom (c. 2000–1700 BC) and were continuously expanded until the Ptolemaic period (305–30 BC). As the second most visited UNESCO World Heritage archaeological site in Egypt after the Giza Pyramids, Karnak faces severe deterioration processes due to prolonged exposure to environmental impacts, mechanical damage, and historical interventions. This study employs a multidisciplinary approach integrating non-destructive testing (NDT) methods to assess the physical and mechanical condition and degradation mechanisms of scattered sandstone blocks at the site. Advanced documentation techniques, including Reflectance Transformation Imaging (RTI), photogrammetry, and Infrared Thermography (IRT), were used to analyze surface morphology, thermal stress effects, and weathering patterns. Ultrasonic Pulse Velocity (UPV) testing provided internal structural assessments, while spectral and gloss analysis quantified chromatic alterations and surface roughness. Additionally, the Karsten Tube test determined the water absorption behavior of the sandstone, highlighting variations in porosity and susceptibility to salt crystallization. In this sense, the results indicate that climatic factors such as extreme temperature fluctuations, wind erosion, and groundwater infiltration contributed to sandstone deterioration. Thermal cycling leads to microcracking and granular disintegration, while high capillary water absorption accelerates chemical weathering processes. UPV analyses showed substantial internal decay, with low-velocity zones correlating with fractures and differential cementation loss. Finally, an interventive conservation plan was proposed. Full article

The development of efficient photocatalytic materials for waterborne pathogen inactivation and self-cleaning surfaces in biomedical applications remains a critical challenge due to the rising prevalence of antimicrobial-resistant bacteria. This study systematically investigates the structural, optical, and photocatalytic disinfection properties of graphitic carbon nitride (g-C₃N₄) synthesized at variable temperatures (450–600 °C) and doped with transition metals (Mn, Co, Cu). Through FTIR and UV/Vis spectroscopy, we demonstrate that synthesis temperatures between 450 and 550 °C yield a well-ordered polymeric network with enhanced π-conjugation and charge separation, while 600 °C induces structural degradation. Metal doping with Mn and Co significantly enhances photocatalytic disinfection, achieving complete E. coli inactivation (6-log reduction) within 6 h via optimized reactive oxygen species (ROS) generation. The best material (g-C₃N₄ synthesized at 500 °C and doped with Mn) was integrated into sodium alginate hydrogel surfaces, demonstrating reusable self-cleaning functionality with sustained bactericidal activity (5.9-log CFU/mL reduction after five cycles). This work provides a roadmap for tailoring metal-doped g-C₃N₄ composites for practical antimicrobial applications, emphasizing the interplay between synthesis parameters, ROS dynamics, and real-world performance. Full article

Climate change and environmental degradation require inclusive and multidimensional strategies, in which women from Generation Z are emerging as key actors. This study explores how female university students from this generation perceive and prioritize social, political, economic, and technological dimensions of sustainable development, with a focus on respondents from Europe. A structured survey instrument, based on a SPET model (Social, Political, Economic, Technological), was administered to 834 female students at a highly internationalized university in Poland. The questionnaire was available in Polish and English to account for linguistic and cultural variation within the Western civilizational context. Quantitative analysis revealed that the political dimension—particularly international cooperation and legal regulations—was viewed as the most critical for environmental protection, followed by technological innovation in energy and resource management. Social and economic factors received relatively less emphasis, with skepticism toward consumer-level behavior change and shared economy models. This study offers a meaningful contribution to understanding gender- and generation-specific perspectives on environmental responsibility. It also provides a foundation for the development of socially grounded, culturally sensitive strategies in sustainability education and policymaking, with relevance for both academic researchers and public stakeholders. Full article

High-resistance faults on the DC lines of multi-terminal VSC-HVDC grids lead to insufficient protection reliability, and the introduction of current-limiting strategies alters the system’s intrinsic fault characteristics, degrading protection performance. To address these issues, we propose a DC-line protection scheme that is immune to converter control strategies and highly tolerant to fault resistance. First, based on the grid topology, post-fault current paths are analyzed, and the fault characteristics produced solely by the fault-induced voltage source are identified. A sequential overlapping derivative transformation is then employed to magnify the discrepancy between internal and external faults, forming the core of the fault-identification criterion; the zero-mode component is used for pole selection. Finally, a four-terminal VSC-HVDC model is built in PSCAD/EMTDC version 4.6.2 for validation. Simulation results show that, after applying the current-limiting strategy, the characteristic quantity changes only marginally, and the proposed protection can reliably withstand fault resistances of up to 700 Ω. Full article

ZrB₂-HfB₂ composites allow us to obtain materials characterized by the high chemical resistance characteristic of HfB₂ while reducing density and improving sinterability due to the presence of ZrB₂. Since boride composites are difficult-to-sinter materials. One way to achieve high density during sintering is to add phases that activate mass transport processes and, after sintering, remain as composite components that do not degrade and even improve some properties of the borides. The following paper is a comprehensive review of the effects of various and the most commonly used sintering aids, i.e., SiC, B₄C, WC, MoSi₂, and CrSi₂, on the thermo-chemical properties of the ZrB₂-HfB₂ composites. High-density composites with a complex phase composition dominated by (Zr,Hf)B₂ solid solutions were obtained using a hot pressing method. The tests showed differences in the properties of the composites due to the type of sintering additives used. From the point of view of the thermo-chemical properties, the best additive was silicon carbide. The composites containing SiC, when compared to the initial, pure borides, were characterized by high thermal conductivity λ (80–150 W/m·K at 20–1000 °C), a significantly reduced thermal expansion coefficient (CTE ~6.20 × 10⁻⁶ 1/K at 20–1000 °C), and considerably improved oxidation resistance (up to 1400 °C). Full article

Urban and peri-urban lakes are increasingly threatened by water quality degradation due to rising anthropogenic pressures and environmental variability. This study proposes an integrated framework that combines multi-source data and machine learning to estimate and monitor three key water quality parameters: turbidity, total dissolved solids (TDS), and biological oxygen demand (BOD). Field measurements from three lakes in West Bengal, India, Rabindra Sarovar, Mirikh Lake, and Hanuman Ghat Lake, were combined with Landsat-8 satellite imagery, meteorological data, and land use information. Three modeling scenarios were developed: (i) using only remote sensing indices, (ii) combining remote sensing indices with meteorological variables, and (iii) integrating remote sensing indices, meteorological data, and land use features. Principal component analysis (PCA) was used to reduce dimensionality and redundancy. Machine learning models, namely, XGBoost, Decision Tree, and Ridge Regression, were trained and evaluated using R² and RMSE (Root Mean Square Error) metrics. The third scenario outperformed the others, with Ridge Regression achieving the highest accuracy for BOD prediction (R² = 0.99). Spatiotemporal patterns revealed persistently high BOD levels along urban lake fringes and post-monsoon spikes in turbidity and TDS, especially in agriculturally influenced zones. These patterns were closely linked to land use practices, rainfall-driven runoff, and point-source pollution. This study underscores the effectiveness of remote sensing and machine learning as scalable tools for real-time water quality monitoring, promoting sustainability through informed lake management strategies in India. Full article

Various low-light image enhancement techniques inevitably introduce noise to varying degrees while improving visibility, leading to a decline in image quality that adversely affects downstream vision tasks. Existing post-processing denoising methods often produce overly smooth results lacking in detail, presenting the challenge of balancing noise suppression and detail preservation. To address this, this paper proposes a conditional diffusion denoising framework based on multi-feature fusion. The framework utilizes a diffusion model to learn the conditional distribution between underexposed and normally exposed images. Complementary features are extracted in parallel through four dedicated branches. These multi-source features are then concatenated and fused to enrich semantic information. Subsequently, redundant information is compressed via 1 × 1 convolutional layers, mitigating the issue of information degradation commonly encountered with U-Net skip connections during multi-scale feature fusion. Experimental results demonstrate the method’s applicability across diverse scenarios and illumination conditions. It outperforms both traditional methods and mainstream deep learning models in qualitative and quantitative evaluations, particularly in terms of perceptual quality. This research provides significant technical support for subsequent image restoration and denoising within low-light enhancement pipelines. Full article