Search Results (1,166)

Sample pretreatment is one of the most important steps in guaranteeing the success of a chromatographic analysis. The selected methodology must ensure simultaneously that a sample is “clean” enough for analysis and that the target analytes are not removed in the process. This can be especially difficult when working with complex matrices such as natural waters and wastewater. For pharmaceutical active compounds (PhACs) analysis by solid-phase extraction (SPE) followed by ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS), and due to the high level of organic matter in wastewater, the water samples are filtered consecutively through three filters, a paper filter, a glass microfiber filter of 1 µm, and a Nylon filter of 0.45 µm. This filtration allows the sample’s passage through the SPE cartridge to be faster, and there is no cartridge clogging, allowing for greater efficiency in the adsorption process. The big question is whether the PhACs are eliminated during filtration, since they may be adsorbed to organic matter. This work aimed to determine if the best approach for quantifying PhACs in wastewater and surface waters would be to filter them prior or to perform SPE directly. Both approaches analyzed a total of 26 PhACs. Turbidity (TUR) and permanganate index (PI) were determined, and their values were high for samples with a high organic matter content. A statistical analysis was performed to determine the best approach to treat these water samples and whether any correlation existed between PhAC concentrations, PI, and TUR. The PhAC quantification shows a positive correlation with TUR and a negative correlation with PI for most of the target PhACs. However, there are not significantly different results for filtered and not-filtered wastewater samples. Full article

Building extraction—needed for urban planning and monitoring—is affected by the misalignment between labels and off-nadir remote sensing imagery. A computer vision approach to teacher–student learning between large–noisy and small–clean data has been introduced as a solution, but with limited accuracy and efficiency. This paper proposes fine-tuning-based transfer learning (FTL) to adapt a pre-trained model from a noisy source to a clean target dataset, improving segmentation accuracy in off-nadir images. A standardized experimental framework is developed with three new building datasets containing large–noisy and small–clean image–label pairs of multiple spatial resolutions. These datasets cover a range of building types, from low-rise to skyscrapers. Additionally, this paper presents one of the most extensive benchmarking efforts in teacher–student learning for building extraction from off-nadir images. Results demonstrate that FTL outperforms the existing methods with higher F1 scores—0.943 (low-rise), 0.868 (mid-rise), 0.912 (high-rise), and 0.697 (skyscrapers)—and higher computational efficiency. A notable gain in mean difference is observed in taller buildings from complex urban environments. The proposed method, datasets, and benchmarking framework provide a robust foundation for accurate building extraction and broader remote sensing applications. Full article

The inadequate collection and treatment of urban wastewater continue to pollute built environments, threaten public health, and contribute to epidemic outbreaks in many densely populated, underdeveloped regions. This study investigates whether algae-based wastewater treatment offers an optimal and efficient solution for drought-prone and underdeveloped cities. Given recent global challenges, such as the COVID-19 pandemic, nature-based wastewater treatment methods—particularly algae-based systems—have regained attention due to their feasibility, cost-effectiveness, and sustainability. Algae-based wastewater treatment presents an innovative approach to sustainable urban development, offering environmental, resource-efficient, energy-saving, and biodiversity benefits while supporting circular economy principles. This study evaluates recent advancements in wastewater treatment technologies and applies a case study methodology to Zahedan City, analyzing sewage canal networks, wastewater composition, and treatment feasibility. Three algae-based techniques were assessed, with waste stabilization ponds (WSPs) identified as the most suitable solution based on technical, economic, and environmental indicators. Key factors such as climate conditions, land-use policies, and cost-effectiveness were incorporated into the comparative analysis, enhancing the scientific rigor of this study compared to prior research. The findings provide actionable insights for urban planners, engineers, and policymakers to address simultaneous challenges in wastewater management, public health, and water scarcity. Full article

Six novel green extraction techniques were evaluated and optimized to extract pyrethrin from dried Dalmatian pyrethrum (Tanacetum cinerariifolium (Trevir./Sch.Bip.). This approach offers a promising natural alternative to conventional chemotherapeutics. Four methods are presented for the first time in this study: microwave-assisted extraction (MAE), high-voltage electric discharge (HVED) extraction, subcritical water extraction (SWE), and deep eutectic solvent (DES) extraction, together with supercritical CO₂ extraction (SC-CO₂) and ultrasound-assisted extraction (UAE), for pyrethrin extraction from Dalmatian pyrethrum. The study revealed that supercritical CO₂ extraction was the most effective method for extracting all six pyrethrins, yielding the highest total amount of 124.37 ng/mg. This approach offers a “natural” insecticide produced with a clean, environmentally friendly technology that can contribute to the development of sustainable and effective insecticide strategies that are in line with environmental safety and organic production standards. In addition, this research highlights the potential application of pyrethrins as antiparasitic agents, emphasizing their role in environmentally friendly and ecological practices. Full article

Developing clean and renewable energy instead of the ones related to hydrocarbon resources has been known as one of the different ways to guarantee reduced greenhouse gas emissions. Geothermal systems and native hydrogen exploration could represent an opportunity to diversify the global energy matrix and lower carbon-related emissions. All of these natural energy sources require a well to be drilled for its access and/or extractions, similar to the petroleum industry. The main focuses of this technical–scientific contribution and research are (i) to evaluate the global energy matrix; (ii) to show the context over the years and future perspectives on geothermal systems and natural hydrogen exploration; and (iii) to present and analyze the importance of developing technologies on drilling process optimization aiming at accessing these natural energy resources. In 2022, the global energy matrix was composed mainly of nonrenewable sources such as oil, natural gas, and coal, where the combustion of fossil fuels produced approximately 37.15 billion tons of CO₂ in the same year. In 2023, USD 1740 billion was invested globally in renewable energy to reduce CO₂ emissions and combat greenhouse gas emissions. In this context, currently, about 353 geothermal power units are in operation worldwide with a capacity of 16,335 MW. In addition, globally, there are 35 geothermal power units under pre-construction (project phase), 93 already being constructed, and recently, 45 announced. Concerning hydrogen, the industry announced 680 large-scale project proposals, valued at USD 240 billion in direct investment by 2030. In Brazil, the energy company Petroleo Brasileiro SA (Petrobras, Rio de Janeiro, Brazil) will invest in the coming years nearly USD 4 million in research involving natural hydrogen generation, and since the exploration and access to natural energy resources (oil and gas, natural hydrogen, and geothermal systems, among others) are achieved through the drilling of wells, this document presents a technical–scientific contextualization of social interest. Full article

The concentrations and spreading of eight synthetic and two natural progestins (PGs) were investigated in surface waters from ten sites at the Douro River Estuary. Samples were filtrated and subjected to solid-phase extraction (SPE) to isolate and concentrate the target PGs. The extracts were cleaned by silica cartridges and analyzed by LC-MS/MS. The finding of biologically relevant amounts of gonanes (22.3 ± 2.7 ng/L), progesterone derivatives (12.2 ± 0.5 ng/L), drospirenone (4.1 ± 0.8 ng/L), and natural PGs (9.4 ± 0.9 ng/L) support the possibility of these compounds acting as endocrine disruptors. Despite the absence of significant differences amongst sampling sites and seasons, the principal component analysis (PCA) and the linear discriminant analysis (LDA) approaches reveal that spring and summer have different patterns of PG distribution compared to autumn and winter. The assessment of risk coefficients (RQs) and the potential concentrations of synthetic progestins in fish blood sustains that all tested compounds pose a significant risk to local biota (RQs > 1). Additionally, three progestins—norethindrone, norethindrone acetate, and medroxyprogesterone acetate—should reach human-equivalent therapeutic levels in fish plasma. Overall, the current data show PGs’ presence and potential impacts in one of the most important estuaries of the Iberian Peninsula. Full article

Fouling, particularly from organic fouling and biofouling, poses a significant challenge in the RO/NF treatment of marginal waters, especially wastewater. Part 1 of this review detailed LMWOC fouling mechanisms. Part 2 focuses on countermeasures and applications. Effective fouling prevention relies on pretreatment, early detection, cleaning, optimized operation, and in situ membrane modification. Accurate fouling prediction is crucial. Preliminary tests using flat-sheet membranes or small-diameter modules are recommended. Currently, no specific fouling index exists for LMWOC fouling. Hydrophobic membranes, such as polyamide, are proposed as alternatives to the standard silt density index (SDI) filter. Once LMWOC fouling potential is assessed, suitable pretreatment methods can be implemented. These include adsorbents, specialized water filters, oxidative decomposition, and antifoulants. In situations where pretreatment is impractical, alternative strategies like high pH operation might be considered. Membrane cleaning becomes necessary upon fouling; however, standard cleaning often fails to fully restore the original flow. Specialized CIP chemicals, including organic solvent-based and oxidative agents, are required. Conversely, LMWOC fouling typically leads to a stabilized flow rate reduction rather than a continuous decline. Aggressive cleaning may be avoided if the resulting operating pressure increase is acceptable. When a significant flow rate drop occurs and LMWOC fouling is suspected, analysis of the fouled membrane is necessary for identification. Standard FT-IR often fails to detect LMWOCs. Solvent extraction followed by GC-MS is required. Pyrolysis GC-MS, which eliminates the extraction step, shows promise. The review concludes by examining how LMWOCs can be strategically utilized to enhance membrane rejection and restore deteriorated membranes. Full article

The conventional manganese carbonate preparation process faces challenges such as low resource utilization efficiency and difficulties in treating by-product Mg-containing ammonium sulfate solution. In this study, a two-stage leaching process was developed to efficiently extract Mn and Mg from the ore. NH₄HCO₃ was used as a precipitant to convert Mn²⁺ in the leachate to MnCO₃, achieving a Mn precipitation efficiency of 99.89%, and the resulting product contained 44.45% Mn, meeting the first-class product indicators of HG/T 4203-2011 (Chinese standard on manganese carbonate for industrial use). To further enhance resource utilization, a combined stripping–adsorption process was designed to treat the Mg-containing ammonium sulfate solution generated during the carbonization process. Subsequently, the economically valuable gypsum and magnesium oxide products were prepared. Additionally, 88.20% of the NH₃ in the solution was stripped and recycled to prepare NH₄HCO₃ and then used during carbonization. Finally, a purified solution free of ammonia nitrogen was obtained using 001×7 resin to dynamically adsorb the filtrates obtained during the stripping process, and the maximum adsorption capacity of resin for ammonia nitrogen was 51.14 mg/g. This process provides a novel approach to achieving clean production in the manganese carbonate production industry. Full article

Geothermal energy is a sustainable resource, specifically referenced as a key energy resource in the Trump adminstration’s Declaring a National Energy Emergency Executive Order in 2025, that harnesses heat from the Earth’s crust to provide continuous clean energy. Identifying suitable geothermal sites involves evaluating various geological and geographical factors to ensure optimal resource extraction and minimal environmental impact. This study evaluates potential geothermal sites in South and Southwestern US states with a high concentration of abandoned fossil fuel infrastructure, tribal lands, HUBZones, or all three in order to evaluate how to balance resource development, tribal land rights, and environmental justice in future geothermal energy systems. First, we used publicly available Geographic Information System (GIS) datasets to identify areas that are tribal lands, HUBZones, and/or have orphaned fossil fuel infrastructure. Then, we leveraged geothermal potential GIS datasets to classify subsurface temperatures and calculated how much energy enhanced geothermal system (EGS) technology could produce in these areas using methods from the geothermal literature. The analysis identified promising geothermal sites that overlap with tribal lands, HUBZones, and existing fossil fuel infrastructure in the following states: Arizona, New Mexico, Texas, Louisiana, Mississippi, Nevada, Arkansas, and Oklahoma. These states have at least a technical potential of over 2300 GW and have over 18,000 abandoned oil wells that could be converted into geothermal plants. This potential could contribute significantly to the nation’s renewable energy portfolio while simultaneously providing additional revenue opportunities and environmental remediation to tribal lands and low-income communities by leveraging policies and programs like the Indian Energy Purchase Preference (IEPP) and the Historically Underutilized Business Zone program (HUBZone), respectively. Full article

In recent years, the integration of nano titanium dioxide (TiO₂) into building materials has become a popular research topic due to its superior mechanical, photocatalytic and self-cleaning properties. In this study, the dynamic behavior of a light steel container house model coated with nano-TiO₂ is investigated using Operational Modal Analysis (OMA). The effects of TiO₂ on the natural frequencies, damping ratios and mode shapes of the light steel container house model are investigated. The Stochastic Subspace Identification-Unweighted Principal Component (SSI-UPC) method is used to extract the modal parameters from the ambient vibration data. The results show that the TiO₂ coating significantly increases the stiffness and improves the damping properties by increasing the natural frequencies of the light steel container house model. The findings indicate that nano-TiO₂ coatings can increase the structural integrity and durability of light steel container houses. This study provides a foundation for future research on nano-reinforced coatings in light steel structural systems. Full article

A diagnostic chemical analysis has been performed on a Roman Cippus funebris in precious white marble located close to an ancient Roman road. The Cippus was in good condition but almost completely covered by a black patina, requiring a conservative cleaning intervention. The restorer in charge of the restoration asked us to make a preliminary diagnosis, on the basis of which we could suggest the most appropriate intervention. The Cippus was dedicated to the young Quintus Cornelius Proclianus, who died at the age of 15, by his mother Valeria Calpurnia Scopele. It perfectly fits into the Roman funerary liturgy and also shows an Etruscan-type iconography that seems to confirm the Etruscan Gens of the family and its dating to the 1st century AD. Ion chromatography (IC) analyses were performed to determine anions and cations on solutions obtained from the extraction of salts from the four samples of the Cippus. pH, conductivity, and red-ox potential measures, as well as UV-visible spectra were carried out on the same solutions. A small fragment, spontaneously fallen from the Cippus’ surface, was also observed by optical microscopy (OM) and scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS). From the analyses, the dark patina that covered the surface before cleaning turned out to be made of black crusts, that is, smog particles adsorbed on sulfates, but above all, by a layer of microflora. The results allowed us to suggest some conservative interventions. Full article

The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection. A range of detection algorithms has been developed to differentiate between benign samples and adversarial examples. However, the detection accuracy of these algorithms is significantly influenced by the characteristics of the adversarial attacks, such as attack type and intensity. Furthermore, the impact of image preprocessing on detection robustness—a common step before adversarial example generation—has been largely overlooked in prior research. To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. For each test image, a counterpart image with the same predicted label is randomly selected from the training dataset. The high-level features of both images are extracted using an encoder and compared through a similarity measurement model. If the feature similarity is low, the test image is classified as an adversarial example. The proposed method was evaluated for detection accuracy against four comparison methods, showing significant improvements over FS, DF, and MD, with a performance comparable to ESRM. Therefore, the subsequent robustness experiments focused exclusively on ESRM. Our results demonstrate that the proposed method exhibits superior robustness against preprocessing operations, such as downsampling and common corruptions, applied by attackers before generating adversarial examples. It is also applicable to various target models. By exploiting semantic conflicts in high-level features between clean and adversarial examples with the same predicted label, the method achieves high detection accuracy across diverse attack types while maintaining resilience to preprocessing, providing a valuable new perspective in the design of adversarial example detection algorithms. Full article

Forecasting natural gas futures prices can help to promote sustainable global energy development, as the efficient use of natural gas as a clean energy source has become key to the growing global demand for sustainable development. This study proposes a new hybrid model for the prediction of natural gas futures prices. Firstly, the original price series is decomposed, and the subsequences, along with influencing factors, are used as input variables. Secondly, the input variables are grouped based on their correlations with the output variable, and different models are employed to forecast each group. A gated recurrent unit (GRU) captures the long-term dependence, an autoencoder (AE) downscales and extracts the features, and a multilayer perceptron (MLP) maps the complex relationships. Subsequently, random forest (RF) integrates the results of the different models to obtain the final prediction. The experimental results show that the model has a mean absolute error (MAE) of 0.32427, a mean absolute percentage error (MAPE) of 10.17428%, a mean squared error (MSE) of 0.46626, a root mean squared error (RMSE) of 0.68283, an R-squared (R²) of 93.10734%, and an accuracy rate (AR) of 89.82572%. The results demonstrate that the proposed decomposition–selection–prediction–integration framework reduces prediction errors, enhances the stability through multiple experiments, improves the prediction efficiency and accuracy, and provides new insights for forecasting. Full article

Effectively handling mixed noise types and varying intensities is crucial for accurate information extraction and analysis, particularly in resource-limited edge computing scenarios. Conventional image denoising approaches struggle with unseen noise distributions, limiting their effectiveness in real-world applications such as object detection, classification, and change detection. To address these challenges, we introduce a novel image denoising framework that integrates asymmetric learning with symmetric fusion. It leverages a pretrained model trained only on clean images to provide semantic priors, while a supervised module learns direct noise-to-clean mappings using paired noisy–clean data. The asymmetry in our approach stems from its dual training objectives: a pretrained encoder extracts semantic priors from noise-free data, while a supervised module learns noise-to-clean mappings. The symmetry is achieved through a structured fusion of pretrained priors and supervised features, enhancing generalization across diverse noise distributions, including those in edge computing environments. Extensive evaluations across multiple noise types and intensities, including real-world remote sensing data, demonstrate the superior robustness of our approach. Our method achieves state-of-the-art performance in both in-distribution and out-of-distribution noise scenarios, significantly enhancing image quality for downstream tasks such as environmental monitoring and disaster response. Future work may explore extending this framework to specialized applications like hyperspectral imaging and nighttime analysis while further refining the interplay between symmetry and asymmetry in deep-learning-based image restoration. Full article

Following the concept of “waste into resources”, a mild and controllable light grafting technique was used to immobilize pancreatic lipase (PPL) in situ on modified organic, chromium-free tanned leather scraps to catalyze the hydrolysis of waste oil. The experimental results showed that immobilized PPL significantly improved the catalytic activity, operational stability, reusability, and storage stability compared to free PPL. Furthermore, the study evaluated the environmental compatibility of the system through biological risk assessment of soil extracts after degradation, indicating that the system has good environmental compatibility. The experiment is simple to operate, uses mild conditions, and the immobilized material is obtained from leather-making solid waste. The use of this immobilization system to treat waste oil in the leather-making process is of great significance for achieving clean and sustainable production in the leather industry. Full article