Search Results (110)

The delayed coking unit (DCU) is a critical technology in Mexican refineries for upgrading heavy crude oil into lighter, high-value products. Despite its economic relevance, the process is energy-intensive, generates substantial emissions, and produces significant coke, challenging its sustainability. This study proposes a multi-objective optimization framework to enhance DCU performance by integrating Aspen Plus^® v.12.1 simulations with sustainability metrics. Five key indicators were considered: Global Warming Potential (GWP), Specific Energy Intensity (SEI), Mass Intensity (MI), Reaction Mass Efficiency (RME), and Product Yield. A validated Aspen Plus^® model was combined with sensitivity analysis to identify critical decision variables, which were optimized through the $ϵ$-constraint method. Strategic adjustments in reflux flows, split ratios, and column operating conditions improved separation efficiency and reduced energy demand. Results show GWP reductions of 15–25% and SEI improvements of 5–18% for light and heavy gas oils, with smaller gains in MI and trade-offs in RME. Product yield was preserved under optimized conditions, ensuring economic feasibility. A key limitation is that this study did not model coking reactions; instead, optimization focused on the separation network, using reactor effluent as a fixed input. Despite this constraint, the methodology demonstrates a replicable path to improve refining sustainability. Full article

In this study, data on the characteristics of overhead power lines of high voltage was used in a classification task to predict power supply outages by means of a supervised machine learning technique. In order to choose the most optimal features for power outage prediction, an Exploratory Data Analysis on power line parameters was carried out, including statistical and correlational methods. For the given task, five classifiers were considered as machine learning algorithms: Support Vector Machine, Logistic Regression, Random Forest, and two gradient-boosting algorithms over decisive trees LightGBM Classifier and CatBoost Classifier. To automate the process of data conversion and eliminate the possibility of data leakage, Pipeline and Column Transformers (builder of heterogeneous features) were applied; data for the models was prepared using One-Hot Encoding and standardization techniques. The data were divided into training and validation samples through cross-validation with stratified separation. The hyperparameters of the classifiers were adjusted using optimization methods: randomized and exhaustive search over specified parameter values. The results of the study demonstrated the potential for predicting power failures on 110 kV overhead power lines based on data on their parameters, as can be seen from the derived quality metrics of tuned classifiers. The best quality of outage prediction was achieved by the Logistic Regression model with quality metrics ROC AUC equal to 0.78 and AUC-PR equal to 0.68. In the final phase of the research, an analysis of the influence of power line parameters on the failure probability was made using the embedded method for determining the feature importance of various models, including estimating the vector of regression coefficients. It allowed for the evaluation of the numerical impact of power line parameters on power supply outages. Full article

Colorectal cancer (CRC) is one of the most common cancers worldwide and a significant global health issue. The human gut microbiota, a complex ecosystem hosting numerous microorganisms such as bacteria, viruses, fungi, and protozoa, plays a crucial role. Increasing evidence indicates that gut microbiota is involved in CRC pathogenesis. In this study, the gut microbiota profiles, short-chain fatty acids, zonulin, and lipopolysaccharide-binding protein levels of newly diagnosed CRC patients were analyzed along with healthy controls to elucidate the relationship between CRC and the gut microbiota. The study included 16 newly diagnosed CRC patients and 16 healthy individuals. For microbiota analysis, DNA isolation from stool samples was performed using the Quick-DNA™ Fecal/Soil Microbe Miniprep Kit followed by sequencing using the MinION device. Data processing was conducted using Guppy software (version 6.5.7) and the Python (3.12) programming language. ELISA kits from Elabscience were utilized for analyzing LBP and zonulin serum levels. Fecal short-chain fatty acids were analyzed using GC-MS/MS equipped with a flame ionization detector and DB-FFAP column. Microbial alpha diversity, assessed using Shannon and Simpson indices, was found to be lower in CRC patients compared to healthy controls (p = 0.045, 0.017). Significant differences in microbial beta diversity were observed between the two groups (p = 0.004). At the phylum level, Bacteroidota was found to be decreased in CRC patients (p = 0.027). Potential biomarker candidates identified included Enterococcus faecium, Ruminococcus bicirculans, Enterococcus gilvus, Enterococcus casseliflavus, Segatella oris, and Akkermansia muciniphila. Serum zonulin levels were higher in CRC patients (CRC = 70.1 ± 26.14, Control = 53.93 ± 17.33, p = 0.048). There is a significant relationship between gut microbiota and CRC. A multifactorial evaluation of this relationship could shed light on potential biomarker identification and the development of new treatment options for CRC. Full article

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward irradiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

The CorA/MRS2-type transporters represent a crucial family of magnesium ion transporters widely distributed in plants. Through comprehensive screening and alignment using the Phytozome database, we identified seven magnesium-related MdMRS2 Confirm the deletion of the “Chinese Province” column in the address. genes in apple (MdMRS2-1 to MdMRS2-7), which were distributed across seven distinct chromosomes. Phylogenetic analysis classified these genes into five distinct clades. Tissue-specific expression profiles revealed the differential expression patterns of MdMRS2 members in different tissues such as the apple roots, stems, leaves, seedlings, seeds, flowers, and fruits. Among them, the expression level of MdMRS2-5 was the highest in fruits, while that of MdMRS2-6 was the lowest in seeds. Analysis of cis-regulatory elements in MdMRS2 promoter regions identified numerous light-responsive elements, MYB binding sites, and hormone-responsive elements, suggesting their transcriptional regulation may be influenced by related metabolic pathways or signaling molecules. qRT-PCR results showed that the relative expression levels of all genes were significantly upregulated compared with CK under M3 treatment, while there were no significant differences in other treatments. Among them, the upregulation of MdMRS2-7 was the most significant, increasing by 142% compared with CK. Notably, all MdMRS2 genes were significantly upregulated under 4 mmol·L⁻¹ MgSO₄ treatment. Subcellular localization experiments conducted in tobacco leaves confirmed the membrane and cytoplasmic distribution of these transporters, consistent with bioinformatic predictions. These genes may become candidate genes for subsequent functional studies. This work will provide a basis for future research on the response mechanism and function of the MRS2 gene family in response to magnesium stress. Full article

The main function of triangulation-based laser profile sensors—also referred to as laser profilometers or profilers—is the three-dimensional scanning of moving objects using laser triangulation. In addition to capturing 3D data, these profilometers simultaneously generate grayscale images of the scanned objects. However, the quality of these images is often degraded due to interference of the laser light, manifesting as speckle noise. In profilometer images, this noise typically appears as vertical stripes. Unlike the column fixed pattern noise commonly observed in TDI CMOS cameras, the positions of these stripes are not stationary. Consequently, conventional algorithms for removing fixed pattern noise yield unsatisfactory results when applied to profilometer images. In this article, we propose an effective method for suppressing speckle noise in profilometer images of flat surfaces, based on local column median vectors. The method was evaluated across a variety of surface types and compared against existing approaches using several metrics, including the standard deviation of the column mean vector (SDCMV), frequency spectrum analysis, and standard image quality assessment measures. Our results demonstrate a substantial improvement in reducing column speckle noise: the SDCMV value achieved with our method is 2.5 to 5 times lower than that obtained using global column median values, and the root mean square (RMS) of the frequency spectrum in the noise-relevant region is reduced by nearly an order of magnitude. General image quality metrics also indicate moderate enhancement: peak signal-to-noise ratio (PSNR) increased by 2.12 dB, and the structural similarity index (SSIM) improved from 0.929 to 0.953. The primary limitation of the proposed method is its applicability only to flat surfaces. Nonetheless, we successfully implemented it in an optical inspection system for the furniture industry, where the post-processed image quality was sufficient to detect surface defects as small as 0.1 mm. Full article

This study presents a 50-year hydrodynamic simulation of Laguna Carén, a shallow lake near the capital of Chile, utilizing the 3-Dimensional coupled Hydrodynamic-Aquatic Ecosystem Model, AEM3D, to investigate the factors influencing the mixing regimes of the water column. The model incorporates key processes, such as heat and momentum exchanges with the atmosphere, light penetration, and heat diffusion in sediments, all fundamental for understanding the hydrodynamics of the lake. The obtained results allowed classifying the mixing regime of the lake in rarely mixed (RM), intermittently mixed (IM), and often-mixed (RM) regimes. Furthermore, the numerical results reveal a significant differentiation between IM and OM years, primarily driven by changes in meteorological forces, particularly wind speed. The analysis indicates that increased wind speeds enhance turbulent kinetic energy, leading to a reduction in the percentage of time the water column is stratified, whereas lower wind conditions promote stable stratification. This study shows that the mixing regime in shallow lakes can exhibit dramatic changes in response to minor alterations in meteorological conditions, particularly wind speed. Such changes in mixing regimes may result in unpredictable consequences for the ecology of the lake. Full article

Size-exclusion chromatography (SEC) is presently a widely used and very informative technique for the characterization of macromolecules in solution. Beyond the first implementations of SEC—which required cumbersome column calibrations and were mainly intended for the determination of molecular weights—the modern SEC approach involving multiple detectors (md-SEC) is based on solution properties such as intrinsic viscosity and light scattering. Thus, md-SEC enables the direct and more efficient determination of molecular weights, as well as the determination of relationships between property and molecular weight, which can be quite useful in structural studies. Here, we first present a review of the fundamental aspects of the dilute-solution properties of macromolecules—particularly the differential refractive index, intrinsic viscosity, and scattering-related properties—on which the various detectors involved in md-SEC are based. Then, we developed SECtools, a suite of public-domain, open-source computer programs, which allow for the full analysis of md-SEC chromatograms. These analyses range from just the recorded raw signals (mV) of the detectors to a full determination of molecular weight averages and distributions. The use of these programs is illustrated through experimental studies using various samples. Full article

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method. Full article

Rigid reinforced concrete (RC) frames are generally adopted as stiff elements to make the building structures resistant to seismic forces. However, a method has yet to be fully sought to provide earthquake resistance through optimizing beam and column performance in a rigid frame. Due to its high corrosion resistance, the integration of CFRP offers an opportunity to reduce frequent repairs and increase durability. This paper presents the structural response of CFRP beams integrated into rigid frames when subjected to seismic events. Without any design provision for CFRP systems in extreme events, multiscale simulations and parametric analyses were performed to optimize the residual state and global performance. Macroparameters, represented by the ductility ratio and microfactors, have been analyzed using a customized version of the modified compression field theory (MCFT). The main parameters considered were reinforcement under tension and compression, strength of concrete, height-to-width ratio, section cover, and confinement level, all of which are important to understand their influence on seismic performance. The parametric analysis results highlight the increased ductility and higher load-carrying capacity of the CFRP-reinforced tested component compared to the RC component. These results shed light on the possibility of designing CFRP-reinforced concrete components that could improve ductile frames with increased energy dissipation and be suitable for applications in non-corrosive seismic-resistant buildings. This also shows reduced brittleness and enhancement in the failure mode. Numerical simulations and experimental results showed a strong correlation with a deviation of about 8.3%, underlining the reliability of the proposed approach for designing seismic-resistant CFRP-reinforced structures. Full article

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km. Full article

Contamination from light nonaqueous phase fluids (LNAPLs) and their derivatives during mining, production, and transportation has become a concern. Scholars have extensively studied LNAPL contamination, but the role of water content variation on its migration process in the unsaturated zone has not been sufficiently researched. The specific issue addressed in this study is the impact of water content on the migration of light nonaqueous phase liquids (LNAPLs) in sandy soils, a critical yet under-researched aspect of subsurface contamination. To tackle this, we employed indoor simulated vertical, one-dimensional, multiphase flow soil column experiments, utilizing the orthogonal experimental method to systematically evaluate the effects of varying water contents on the occurrence state and migration rate of LNAPLs. The experimental results indicate the following: (1) The migration rate of LNAPL exhibits an L-shaped trend during subsurface imbibition and a nonlinear relationship with migration time. The migration rate and migration time of surface infiltration have a linear growth relationship. (2) The residual rate of LNAPL is negatively correlated with water content and positively correlated with oil content in the homogeneous non-saturated state. With the increase in the amount of leaked oil, 40% of the leaked LNAPL is sorbed within the soil. (3) When the water content of the test medium is below 14%, and the oil content is below 11%, LNAPL appears in the unsaturated zone in a solid phase. As the water content increases, the adsorption rate of the oil phase gradually decreases and eventually reaches the oil saturation point. (4) When the water content of the medium exceeds 8%, over time, LNAPL will be subject to oil–water interfacial tension, and the rate of LNAPL movement first decreases and then increases, displaying nonlinear growth. The innovation of this work lies in the comprehensive analysis of LNAPL migration under controlled laboratory conditions, providing results that enhance the understanding of LNAPL behavior in sandy soils. These quantitative insights are crucial for developing targeted remediation strategies for LNAPL-induced pollution in the unsaturated zone. Full article

Plastics, once regarded as a revolutionary material shaping modern society, now pose an unprecedented threat to our environment. Household solid waste sorting stations produce several fractions, one of which contains a high concentration of Low-Density Polyethylene (LDPE) film waste (packaging, sunscreen film, etc.). This fraction is difficult to recycle because it contains quite a lot of impurities. Usually, it is sent to cement factories that burn it together with other fuels. However, with some processing techniques such as catalytic pyrolysis, this fraction could be valorized. In this paper, experiments were carried out in batches at a laboratory-scale installation, with a processing capacity of 1–3 kg of waste. A pyrolysis reactor was connected to a distillation column, enabling separation of the fractions. The gaseous and liquid fractions were characterized by GC-FID-TCD (gases) and GC-MS (liquids) analysis. Natural catalysts such as bentonite or clinoptilolite were studied and used in the melting of plastic mass to simplify the process as much as possible. To test the activity of the catalysts, the pyrolysis of LDPE granules was initially studied. It was found that natural zeolites are much more active than bentonite and that a minimum concentration of 5–10% is needed to have a positive effect on the composition of the fractions (increasing the weight of the light fractions (C1–C6, C6–C10, and C11–C13) in relation to the heavy fractions (C13–C20 and C20+). Catalytic pyrolysis gives a completely different distribution of light hydrocarbons. The best catalyst selected from LDPE lab experiments was then tested upon the pyrolysis of plastic film waste obtained by a waste treatment plant. The research objective reported in this paper was to obtain a fraction of combustible gases in the largest possible proportion, which can be much more easily exploited by burning in an engine that drives an electric generator. Full article

Thin-walled concrete-filled steel tubes are efficient and economical with promising applications in civil and light industrial buildings. However, their local buckling resistance and deformation capacity are low, which adversely affects the seismic safety of structures. There are relatively few studies on thin-walled concrete-filled steel tubular columns with ultra-large diameter-to-thickness ratios, and there is also a lack of relevant experimental research on them. In this study, horizontal hysteresis tests were conducted on concrete columns with a large diameter-to-thickness ratio. The seismic performances of regular and straight-ribbed specimens were analyzed and compared, including the analyses of load-displacement hysteresis curves, strain distribution, skeleton curves, ductility, and energy dissipation capacity. Using these results, a restoring force model for concrete columns with a large diameter-to-thickness ratio was established. The findings indicate that under horizontal loading, the ductility of concrete columns with a regular thin-walled steel tube is 3.9, with an equivalent viscous damping coefficient of 1.65. Meanwhile, the ultimate bearing capacity is 201 kN. After adding stiffening ribs, the ultimate bearing capacity reaches 266 kN and the ductility coefficient reaches 4.4, resulting in the stiffeners increasing the ultimate bearing capacity and ductility by >30% and 12.8%, respectively. However, they have a less pronounced effect on deformation and energy dissipation. Building on these research outcomes, we propose a dimensionless three-line skeleton curve model and a restoring force model. The calculation results from these models align well with the test results, offering valuable insights for the seismic safety analysis of real-world engineering structures. Full article