Search Results (466)

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Electricity is a significant factor in the life cycle of many products, so the reliability of greenhouse gas (GHG) emissions data is crucial. The article presents publicly available sources of emission factors representative of Poland. The aim of the study is to assess their strengths and weaknesses in the context of the calculation requirements of carbon footprint analysis in accordance with the GHG Protocol. The article presents the results of carbon footprint calculations for different ranges of emissions in the life cycle of 1 kWh of electricity delivered to a hypothetical organization. Next, a discussion on the quality of the emissions factors has been provided, taking account of data quality indicators. It was concluded that two of the emissions factors that are compared—those based on the national consumption mix and the residual mix for Poland—have been recognized as suitable for use in carbon footprint calculations. Beyond the calculation results, the research highlights the significance of the impact of the selection of emissions factors on the reliability of environmental analysis. The article identifies methodological challenges, including the risk of double counting, limited transparency, methodological inconsistency, and low correlation of data with specific locations and technologies. The insights presented contribute to improving the robustness of carbon footprint calculations. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

Per- and polyfluoroalkyl substances (PFAS) have been found worldwide in water, soil, plants, and animals, including humans. A primary route of exposure for humans and animals to PFAS is through the diet and drinking water. Perfluorooctane sulfonate (PFOS), a long-chain PFAS with a relatively long half-life, has been associated with adverse health effects in humans and laboratory animals. There are few toxicokinetic studies on PFOS in domestic livestock raised for human food consumption, which are critical for assessing human food safety. This work aimed to develop a simple daily accumulation model (DAM) for predicting PFOS residues in edible beef cattle muscle. A one-compartment toxicokinetic model in a spreadsheet format was developed using simple calculations to account for daily PFAS into and out of the animal. The DAM was used to simulate two case studies to predict resultant PFOS residues in edible beef cattle tissues. The results demonstrated that the model can reasonably predict PFOS concentrations in beef cattle muscle in a real-world scenario. The DAM was then used to simulate dietary PFOS exposure in beef cattle throughout a typical lifespan in order to derive a generic bioaccumulation factor. The DAM is expected to work well for other PFAS in beef cattle, PFAS in other livestock species raised for meat, and other chemical contaminants with relatively long half-lives. Full article

To address the issue of low accuracy in identifying the transition states of rolling bearing performance degradation when relying solely on vibration signals, this study proposed a vibration–temperature fusion-based adaptive method for bearing performance degradation assessments. First, a multidimensional time–frequency feature set was constructed by integrating vibration acceleration and temperature signals. Second, a novel composite sensitivity index (CSI) was introduced, incorporating the trend persistence, monotonicity, and signal complexity to perform preliminary feature screening. Mutual information clustering and regularized entropy weight optimization were then combined to reselect highly sensitive parameters from the initially screened features. Subsequently, an adaptive feature fusion method based on auto-associative kernel regression (AFF-AAKR) was introduced to compress the data in the spatial dimension while enhancing the degradation trend characterization capability of the health indicator (HI) through a temporal residual analysis. Furthermore, the entropy weight method was employed to quantify the information entropy differences between the vibration and temperature signals, enabling dynamic weight allocation to construct a comprehensive HI. Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. The experimental results demonstrated that the proposed method outperformed traditional vibration-based life-stage identification approaches. Full article

As consumer preferences tend toward safer, chemical residue-free, and nutritionally rich fruits, preharvest bagging has gained attention as a sustainable method for improving fruit quality and protecting produce from environmental and biological stressors and pesticide residues. This study assessed the impact of preharvest bagging using paper bags with inner aluminum coating on the physicochemical traits, storability, and browning susceptibility after cutting or bruising of ‘Afrata Volou’ quince (Cydonia oblonga Mill.) fruit grown in central Greece. Fruits were either bagged or left unbagged approximately 60 days before harvest, and evaluations were conducted at harvest and after three months of cold storage, plus two days of shelf-life. Fruit bagging reduced the quince’s flesh temperature on the tree crown. Bagging had minor effects on fruit and nutritional quality, except for more yellow skin and higher titratable acidity (TA). Also, at harvest, bagging did not significantly affect fruit flesh browning after cutting or bruising. After three months of storage, unbagged and bagged quince fruit developed more yellow skin color, without significant alterations in most quality characteristics and nutritional value, but increased total tannin content (TTC). After three months of storage, the quince flesh color determined immediately after cutting or bruising was brighter and more yellowish compared to that at harvest, due to continuation of fruit ripening, but it darkened faster with time after cutting or skin removal. Therefore, fruit bagging appears to be a sustainable practice for improving the aesthetic and some chemical quality characteristics of quince, particularly after storage, without negative impacts on other characteristics such as texture and phenolic content. Full article

The olive oil sector constitutes a fundamental pillar in the Mediterranean region from socio-economic and cultural perspectives. Nonetheless, it produces significant amounts of waste, leading to numerous environmental issues. These waste streams contain valuable compounds that can be recovered and utilized as inputs for various applications. This study introduces a novel value chain for olive wastes, focused on extracting lignin from olive pomace by ionic liquids and polyphenols from olive mill wastewater, which are then incorporated as hybrid nanoparticles in the formulation of an innovative starch-based biofertilizer. This biofertilizer, obtained by using residual wastewater as a source of soluble nitrogen, acting at the same time as a plasticizer for the biopolymer, was demonstrated to surpass traditional NPK biofertilizers’ efficiency, allowing for root growth and foliage in drought conditions. In order to recognize the environmental impact due to its production and align it with the technical output, the circularity and environmental performance of the proposed system were innovatively evaluated through a combination of Life Cycle Assessment (LCA) and the Material Circularity Indicator (MCI). LCA results indicated that the initial upcycling process was potentially characterized by significant hot spots, primarily related to energy consumption (>0.70 kWh/kg of water) during the early processing stages. As a result, the LCA score of this preliminary version of the biofertilizer may be higher than that of conventional commercial products, due to reliance on thermal processes for water removal and the substantial contribution (56%) of lignin/polyphenol precursors to the total LCA score. Replacing energy-intensive thermal treatments with more efficient alternatives represents a critical area for improvement. The MCI value of 0.84 indicates limited potential for further enhancement. Full article

Background: Rapid loss of residual kidney function (RKF) is associated with unfavorable outcomes. We conducted an RCT to compare the effects on RKF preservation of incremental HD between once-weekly HD (1-WHD) and twice-weekly HD (2-WHD). Methods: ESKD patients with an eGFR of 5–10 mL/min/1.73 m² and urine output of ≥800 mL/day were randomly assigned to receive either once-weekly HD (1-WHD) or twice-weekly HD (2-WHD) for 12 months. Patients in the 1-WHD group were prescribed once-weekly HD combined with low-protein diet (0.6 g/kg/day) supplemented with keto-analogues (KAs) 0.12 g/kg/day. In the 2-WHD group, patients received twice-weekly HD with a regular-protein diet. Primary outcomes were changes in RKF by renal clearance and urine volume. Nutritional status, muscle parameters, and quality of life (QoL) were also assessed. Results: A total of 30 incident HD patients were randomized. Baseline RKF, urine volume, and demographic were not different between groups. After 3 months, urine volume was significantly higher in the 1-WHD group than in the 2-WHD group (1921 ± 767 mL/day vs. 1305 ± 599 mL/day, p = 0.02), and these significant findings persisted throughout the entire study period. For RKF, 1-WHD also had a lesser decline in urinary urea (CUrea) and creatinine clearance (CCr) than 2-WHD, with statistically significant differences observed from months 6–12. By month 6, the 1-WHD group exhibited significantly higher CUrea and CCr compared to the 2-WHD group, with CUrea at 3.2 ± 2.3 vs. 1.7 ± 1.0 mL/min (p = 0.03) and CCr at 5.9 ± 3.6 vs. 3.8 ± 1.4 mL/min (p = 0.04), respectively. Serum albumin levels, skeletal muscle mass, anemia status, metabolic parameters, protein-bound uremic toxins, and QoL scores were comparable between the two groups. Conclusions: Incremental HD, starting with once-weekly HD combined with protein restriction supplemented with KAs, appears to better preserve RKF among incident HD patients compared to twice-weekly HD with a regular-protein diet. This HD regimen was also associated with safety in metabolic and nutritional profiles. Full article

RC piers in mountainous coastal or saline areas face the dual threats of rockfall impacts and chloride-induced steel corrosion, but their combined effects on dynamic response and residual bearing capacity remain unquantified. This study aims to investigate these combined effects over a 90-year service time and propose a damage assessment formula. A validated numerical model (relative error ≤14.7%) of corroded RC columns under impact is developed using ABAQUS, based on which the dynamic response and residual bearing capacity of an actual RC pier subjected to rockfall impacts during the service time of 90 years incorporating corrosion initiation (via Life-365 software 2.2) and propagation are analyzed, with the consideration of various impact energies (1–5 t mass, 5–15 m/s velocity). Results show that (1) increasing impact mass/velocity expands damage and increases displacement (e.g., the velocity of increases peak displacement by 33.41 mm in comparison to 5 m/s); (2) a 90-year service time leads to >50% severe surface damage and 47.1% residual capacity loss; and (3) the proposed and validated damage formula assessment formula for the residual bearing capacity enables lifecycle maintenance guidance. This work provides a validated framework for assessing combined corrosion-rockfall effects, aiding design and maintenance of structures. Full article

This paper develops a novel design formula to estimate the residual strength of corroded stiffened cylindrical structures. It extends a previously established ultimate strength formulation for intact cylinders by introducing a corrosion-induced strength reduction factor. The foundational formula considers failure mode interactions like yielding, local buckling, overall buckling, and stiffener tripping. This research utilizes recent experimental and numerical investigations on corroded ring-stiffened cylinder models. Experimental results validate the numerical analysis method, showing good agreement in collapse pressures (2–4% difference) and shapes. The validated numerical method is then subject to an extensive parametric study, systematically varying corrosion characteristics. Results indicate a clear relationship between corrosion volume and strength reduction, with overall buckling being more sensitive. Based on these comprehensive results, a new empirical strength reduction factor (${\rho }_c$) is derived as a function of the corrosion volume ratio ($V_{non}$). This factor is integrated into the existing ultimate strength formula, allowing direct residual strength estimation for corroded structures. The proposed formula is rigorously verified against experimental and numerical data, showing excellent agreement (mean 1.00, COV 5.86%). This research provides a practical, accurate design tool for assessing the integrity and service life of corroded stiffened cylindrical structures. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

Oenological residues may cause environmental pollution when processing does not significantly reduce volume and/or harmful conditions. The lack of proper valorisation alternatives entails high disposal costs and resource inefficiency that jeopardise the sustainability and competitiveness of the industry. Interestingly, wine by-products are underappreciated sources of multipurpose bioactive compounds, such as anthocyanins, associated with health benefits. Alternatively, transforming oenological by-products into valuable co-products will promote sustainability and thus, create new business opportunities. In this context, the present study has assessed the applicability of winery by-products (grape pomace and wine lees) as ingredients to develop new functional kombucha-analogous beverages “3S” (safe, salubrious, and sustainable) by the Symbiotic Culture of Bacteria and Yeast (SCOBY). Concerning the main results, during the kombucha’s development, the fermentation reactions modified the physicochemical parameters of the beverages, namely pH, total soluble solids, acetic acid, ethanol, and sugars, which remained stable throughout the monitored shelf-life period considered (21 days). The fermented beverages obtained exhibited high anthocyanin concentration, especially when using wine lees as an ingredient (up to 5.60 mg/L at the end of the aerobic fermentation period (10 days)) compared with the alternative beverages produced using grape pomace (1.69 mg/L). These findings demonstrated that using winery by-products for the development of new “3S” fermented beverages would provide a dietary source of bioactive compounds (mainly anthocyanins), further supporting new valorisation chances and thus contributing to the competitiveness and sustainability of the winery industries. This study opens a new avenue for cross-industry innovation, merging fermentation traditions with a new eco-friendly production of functional beverages that contribute to transforming oenological residues into valuable co-products. Full article

Integrating recyclates into food packaging is key towards circularity while meeting functionality and safety requirements; however, associated environmental impacts remain underexplored. This gap was addressed through a cradle-to-gate life cycle assessment, using the Environmental Footprint method, along with substitution and cut-off approaches for handling the multifunctionality of recycling. Recyclates were derived from polyethylene (PE)-rich household food packaging waste, purified via delamination-deinking. Firstly, results show that shifting from virgin multi-material to mono-material multilayer structures with or without recyclates, while maintaining functionality, offers environmental benefits. Secondly, recyclates should sufficiently substitute virgin materials in quantity and quality, decreasing the need for primary plastics and avoiding recyclate incorporation without functionality. Otherwise, thicker laminates are obtained, increasing processability challenges and environmental impacts, e.g., 12% for particulate matter, and 14% for mineral-metal resource use when the recycle content rises from 34 to 50%. Thirdly, a fully closed loop for flexible food packaging is not yet feasible. Key improvements lie in reducing residues generated during recycling, especially in delamination-deinking, lowering energy use in recompounding, and using more efficient transport modes for waste collection. Further research is essential to optimise the innovative technologies studied for flexible food packaging and refine them for broader applications. Full article

Background/Objectives: Fatty acids, the building blocks of lipids, contribute to numerous crucial life processes and are implicated in numerous disease pathologies. Circulating fatty acids can be extracted/trans-esterified to their respective methyl ester forms and quantified from a variety of biological samples. This study aims to identify quantifiable fatty acids (through alkali trans-esterification) in human circulation, assess the correlation of the detectable fatty acid methyl esters (FAMEs) compounds between whole blood, serum and plasma matrices and propose the most ideal matrix for quantification of FAMEs. Methods: This anonymised study was carried out in a tertiary hospital after obtaining ethical approval and involved analysis of residual fasting whole blood, serum and plasma samples obtained from 20 apparently healthy subjects attending the routine health check services at the study centre. Fatty acids were converted to its methyl ester form by methanolic KOH trans-esterification and subjected to GCMS analysis. Paired t test, Pearsons’s correlation, linear regression and Bland Altman test were employed to assess the agreeability between matrices. Results: 9 out of 37 FAME compounds were detected in all three matrices. Strong correlations and statistically significant regression equations were obtained for the 9 compounds between plasma and serum matrices. Undecanoate, pentadecanoate, linolenate, and palmitate levels were lowest in plasma, while stearate, heptadecanoate levels were highest in whole blood. Myristate was highest in serum, dodecanoate was highest in plasma while docosahexanoate was found to be comparable in all three matrices. Methyl ester forms of dodeconate, myristate, pentadecanoate, palmitate, heptadecanoate, stearate, and linolenate were observed in higher concentrations in plasma when compared to serum. Conclusions: The current study shows similar & correlating FAME concentrations between serum and plasma matrix; however, whole blood FAME concentrations appear significantly different. Plasma serves as the most ideal matrix for detection and quantification of circulating fatty acids. Full article

This study presents a social life cycle assessment (S-LCA) of the F-CUBED Production System (FPS), an innovative process that converts wet biogenic residues—specifically paper biosludge, virgin olive pomace, and fruit and vegetable residues—into intermediate bioenergy carriers via hydrothermal treatment (TORWASH^®), pelletization, and anaerobic digestion. The hydrothermal carbonization of these low-grade, moisture-rich biogenic residues enhances the flexibility and reliability of renewable energy systems while also offering the potential to reduce environmental burdens compared to conventional disposal methods. Through this S-LCA, the study aims to evaluate the cradle-to-gate socioeconomic impacts of the FPS in three European contexts—Sweden, Italy, and Spain—using the 2020 UNEP Guidelines and the Social Hotspots Database (SHDB) and applying quantitative modeling via SimaPro. The functional unit is defined as 1 kWh of electricity produced. The assessment combines SHDB-based modeling with primary data from stakeholder surveys conducted in the three countries. Impact categories are harmonized between SHDB and UNEP typologies, and the results are reported in medium-risk-hour equivalents (mrheq). The results show a heterogeneous social impact profile across case studies. In Sweden, the treatment of paper biosludge delivers substantial benefits with minimal risk. In Spain (orange peel), the introduction of the FPS demonstrated a strong social benefit, particularly in health and safety and labor rights, indicating high institutional performance and good integration with local industry. Conversely, in Italy (olive pomace), the FPS revealed significant social risks, especially in the biopellet production and electricity generation sectors, reflecting regional vulnerabilities in labor conditions and governance. This suggests that targeted mitigation strategies are recommended in contexts like Southern Italy. These findings highlight that the social sustainability of emerging bioenergy technologies is context-dependent and sensitive to sectoral and regional socioeconomic conditions. This S-LCA complements prior environmental assessments and emphasizes the importance of integrating social performance considerations in the deployment and scaling of innovative bioenergy systems. Full article