Search Results (1,755)

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational principles and the limitations of sensors. To address this problem, this study theoretically explores a numerically simulated integrated inertial navigation system consisting of a single-axis cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU). The system leverages the low bias and drift of the CAIG and the high sampling rate of the conventional IMU to obtain more accurate navigation information. Furthermore, an adaptive gradient ascent (AGA) method is proposed to estimate the variance of the measurement noise online for the Kalman filter. It was found that errors of latitude, longitude, and positioning are reduced by 43.9%, 32.6%, and 32.3% compared with the conventional IMU over 24 h. On this basis, errors from inertial sensor drift could be further reduced by the online Kalman filter. Full article

The fraction of absorbed photosynthetically active radiation (FAPAR) stands as a pivotal parameter within the Earth system, quantifying the energy exchange between vegetation and solar radiation. Accordingly, there is an urgent need for comprehensive validation studies to accurately quantify uncertainties and improve the reliability of FAPAR-based applications. This study validated five global FAPAR products, MOD15A2H, MYD15A2H, VNP15A2H, GEOV2, and GEOV3, over four boreal forest sites in North America. Qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) of each product were analyzed. Time series high-resolution reference FAPAR maps were developed using the Harmonized Landsat and Sentinel-2 dataset. The reference FAPAR maps revealed a strong agreement with the in situ FAPAR from AmeriFlux (correlation coefficient (R) = 0.91; root mean square error (RMSE) = 0.06). The results revealed that global FAPAR products show similar uncertainties (RMSE: 0.16 ± 0.04) and moderate agreement with the reference FAPAR (R = 0.75 ± 0.10). On average, 34.47 ± 6.91% of the FAPAR data met the goal requirements of the Global Climate Observing System (GCOS), while 54.41 ± 6.89% met the threshold requirements of the GCOS. Deciduous forests perform better than evergreen forests, and the products tend to underestimate the reference data, especially for the beginning and end of growing seasons in evergreen forests. There are no obvious quality differences at different QQFs, and the relative QQI can be used to filter high-quality values. To enhance the regional applicability of global FAPAR products, further algorithm improvements and expanded validation efforts are essential. Full article

Feed supplementation strategies are essential for optimizing cattle productivity, and the incorporation of non-conventional feed resources may reduce both production costs and environmental impact. This study evaluated the effects of pelletized protein concentrates (including Acacia farnesiana, A. schaffneri, and Agave duranguensis bagasse) on rumen fermentation parameters, microbial communities, and gas emissions. Fistulated bullocks received the concentrate daily, and ruminal contents were collected and filtered before and after supplementation to assess in vitro gas and methane production, pH, and microbial composition using high-throughput sequencing of 16S rRNA and mcrA amplicons. In addition, in situ degradability was evaluated during and after the supplementation period. Supplementation led to a significant (p < 0.05) reduction in degradability parameters and methane production, along with a marked decrease in the abundance of Methanobrevibacter and an increase in succinate-producing taxa. These effects were attributed to the enhanced levels of non-fiber carbohydrates, hemicellulose, crude protein, and the presence of bioactive secondary metabolites and methanol. Rumen microbiota composition was consistent with previously described core communities, and mcrA-based sequencing proved to be a valuable tool for targeted methanogen detection. Overall, the inclusion of non-conventional ingredients in protein concentrates may improve ruminal fermentation efficiency and contribute to methane mitigation in ruminants, although further in vivo trials on a larger scale are recommended. Full article

Wireless sensor networks (WSNs) are used for data acquisition and transmission in unmanned surface vessels (USVs). However, the openness of wireless networks makes USVs highly susceptible to false data injection (FDI) attacks during data transmission, which affects the sensors’ ability to receive real data and leads to decision-making errors in the control center. In this paper, a novel dynamic data encryption method is proposed whereby data are encrypted prior to transmission and the key is dynamically updated using historical system data, with a view to increasing the difficulty for attackers to crack the ciphertext. At the same time, a dynamic relationship is established among ciphertext, key, and auxiliary encrypted ciphertext, and an attack detection scheme based on dynamic encryption is designed to realize instant detection and localization of FDI attacks. Further, an $H_{\infty }$ fusion filter is designed to filter external interference noise, and the real information is estimated or restored by the weighted fusion algorithm. Ultimately, the validity of the proposed scheme is confirmed through simulation experiments. Full article

Background: Melanoma metastasis, driven by tumor microenvironment (TME)-mediated crosstalk facilitated by extracellular vesicles (EVs), remains a major therapeutic challenge. A critical barrier to clinical translation is the overlap in protein cargo between tumor-derived and healthy cell EVs. Objective: To address this, we developed Scaffold-free Functional Deconvolution (SFD), a novel computational approach that leverages a comprehensive healthy cell EV protein database to deconvolute non-oncogenic background signals. Methods: Beginning with 1915 proteins (identified by MS/MS analysis on an Orbitrap Fusion Lumos Mass Spectrometer using the IonStar workflow) from melanoma EVs isolated using REIUS, SFD applies four sequential filters: exclusion of normal melanocyte EV proteins, prioritization of metastasis-linked entries (HCMDB), refinement via melanocyte-specific databases, and validation against TCGA survival data. Results: This workflow identified 21 high-confidence targets implicated in metabolic-associated acidification, immune modulation, and oncogenesis, and were analyzed for reduced disease-free and overall survival. SFD’s versatility was further demonstrated by surfaceome profiling, confirming enrichment of H7-B3 (CD276), ICAM1, and MIC-1 (GDF-15) in metastatic melanoma EV via Western blot and flow cytometry. Meta-analysis using Vesiclepedia and STRING categorized these targets into metabolic, immune, and oncogenic drivers, revealing a dense interaction network. Conclusions: Our results highlight SFD as a powerful tool for identifying clinically relevant biomarkers and therapeutic targets within melanoma EVs, with potential applications in drug development and personalized medicine. Full article

Due to the growing issue of plastic pollution over recent decades, it is essential to establish well-defined and appropriate methodologies for their extraction from diverse environmental samples. These particles can be found in complex agricultural matrices such as compost, sediments, agricultural soils, sludge, and wastewater, as well as in less complex samples like tap and bottled water. The general steps of MPs extraction typically include drying the sample, sieving to remove larger particles, removal of organic matter, density separation to isolate polymers, filtration using meshes of various sizes, oven drying of the filters, and polymer identification. Complex matrices with high organic matter content require specific removal steps. Most studies employ an initial drying process with temperature control to prevent polymer damage. For removal of organic matter, 30% H₂O₂ is the most commonly used reagent, and for density separation, saturated NaCl and ZnCl₂ solutions are typically applied for low- and high-density polymers, respectively. Finally, filtration is carried out using meshes selected according to the identification technique. This review analyzes the advantages and limitations of the different methodologies to extract microplastics from different sources, aiming to provide in-depth insight for researchers dedicated to the study of environmental samples. Full article

In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input. Then, a fault detection filter adapted to this system is designed, thereby obtaining the unknown nonlinear stochastic singular uncertain Augmented filter residual system (UNSSUAFRS) model. To obtain the sufficient conditions for the random admissibility of this augmented system, a weak infinitesimal generator was used to design the required Lyapunov-Krasovskii functional. With the help of the Lyapunov principle and $H_{\infty }$ performance analysis method, the sufficient conditions for the random admissibility of UNSSUAFRS under the $H_{\infty }$ performance index $\gamma$ were derived. Finally, with the aid of the designed residual evaluation function and threshold, simulation analysis was conducted on the examples of DC servo motors and numerical calculation examples to verify the effectiveness and practicability of this fault detection filter. Full article

Military personnel deployed to Iraq and Afghanistan were exposed to emissions from open-air burn pits, where plastics, metals, and medical waste were incinerated. These exposures have been linked to deployment-related respiratory diseases (DRRD) and may also impact neurological health via the lung–brain axis. To investigate molecular mechanisms, adult male rats were exposed to filtered air, naphthalene (a representative volatile organic compound), or a combination of naphthalene and carbon black (surrogate for particulate matter; CBN) via whole-body inhalation (six hours/day, three consecutive days). Lung, brain, and plasma samples were collected 24 h after the final exposure. Pro-inflammatory biomarkers were assessed using multiplex electrochemiluminescence and western blot. Differentially expressed genes (DEGs) were identified by RNA sequencing, and elastic net modeling was used to define exposure-predictive gene signatures. CBN exposure altered inflammatory biomarkers across tissues, with activation of nuclear factor kappa B (NF-κB) signaling. In the lung, gene set enrichment revealed activated pathways related to proliferation and inflammation, while epithelial–mesenchymal transition (EMT) and oxidative phosphorylation were suppressed. In the brain, EMT, inflammation, and senescence pathways were activated, while ribosomal function and oxidative metabolism were downregulated. Elastic net modeling identified a lung gene signature predictive of CBN exposure, including Kcnq3, Tgfbr1, and Tm4sf19. These findings demonstrate that inhalation of a surrogate burn pit mixture induces inflammatory and metabolic gene expression changes in both lung and brain tissues, supporting the utility of this animal model for understanding systemic effects of airborne military toxicants and for identifying potential biomarkers relevant to DRRD and Veteran health. Full article

In response to the increasing demand for high-precision navigation of satellites operating in the cislunar space, this study introduces an onboard orbit determination algorithm considering both convergence and computational efficiency, referred to as the Sliding-Window Batch Processing (SWBP) algorithm. This algorithm combines the strengths of data batch processing and the sequential processing algorithm, utilizing measurement data from multiple historical and current epochs to update the orbit state of the current epoch. This algorithm facilitates rapid convergence in orbit determination, even in instances where the initial orbit error is large. The SWBP algorithm has been used to evaluate the navigation performance in the Distant Retrograde Orbit (DRO) and the Earth–Moon transfer orbit. The scenario involves a low-Earth-orbit (LEO) satellite establishing satellite-to-satellite tracking (SST) links with both a DRO satellite and an Earth–Moon transfer satellite. The LEO satellite can determine its orbit accurately by receiving GNSS signals. The experiments show that the DRO satellite achieves an orbit determination accuracy of 100 m within 100 h under an initial position error of 500 km, and the transfer orbit satellite reaches an orbit determination accuracy of 600 m within 3.5 h under an initial position error of 100 km. When the Earth–Moon transfer satellite exhibits a large initial orbital error (on the order of hundreds of kilometers) or the LEO satellite’s positional accuracy is degraded, the SWBP algorithm demonstrates superior convergence speed and precision in orbit determination compared to the Extended Kalman Filter (EKF). This confirms the proposed algorithm’s capability to handle complex orbital determination scenarios effectively. Full article

Background: As a well-known environmental hazard, ambient fine particulate matter (PM_2.5, aerodynamic diameter ≤ 2.5 µm) has been positively correlated with an increased risk of digestive system diseases, including appendicitis, inflammatory bowel disease, and gastrointestinal cancer. Additionally, PM_2.5 exposure has been shown to alter microbiota composition and diversity in human and animal models. However, its impact on goblet cells and gut mucus barrier integrity remains unclear. Methods: To address this, 8-week-old male and female interleukin-10 knockout (IL10^−/−) mice, serving as a spontaneous colitis model, were exposed to concentrated ambient PM_2.5 or filtered air (FA) in a whole-body exposure system for 17 weeks. Colon tissues from the PM_2.5-exposed mice and LS174T goblet cells were analyzed using H&E staining, transmission electron microscopy (TEM), and transcriptomic profiling. Results: The average PM_2.5 concentration in the exposure chamber was 100.20 ± 13.79 µg/m³. PM_2.5 exposure in the IL10^−/− mice led to pronounced colon shortening, increased inflammatory infiltration, ragged villi brush borders, dense goblet cells with sparse enterocytes, and lipid droplet accumulation in mitochondria. Similar ultrastructure changes were exhibited in the LS174T goblet cells after PM_2.5 exposure. Transcriptomic analysis revealed a predominantly upregulated gene expression spectrum, indicating an overall enhancement rather than suppression of metabolic activity after PM_2.5 exposure. Integrated enrichment analyses, including GO, KEGG, and GSEA, showed enrichment in pathways related to oxidative stress, xenobiotic (exogenous compound) metabolism, and energy metabolism. METAFlux, a metabolic activity analysis, further substantiated that PM_2.5 exposure induces a shift in cellular energy metabolism preference and disrupts redox homeostasis. Conclusions: The findings of exacerbated gut barrier impairment and goblet cell dysfunction following PM_2.5 exposure provide new evidence of environmental factors contributing to colitis, highlighting new perspectives on its role in the pathogenesis of colitis. Full article

In response to the dual challenges of the mechanical behavior of steel sheet pile cofferdam and sediment control in urban water intake projects, a multi-method integrated study was conducted based on the Nenjiang Project. The results show that the peak stress of FSP-IV steel sheet piles (64.3 MPa) is located at a depth of 5.5–8.0 m in the center of the foundation pit, and that the maximum horizontal displacement (6.96 mm) occurs at the middle of the side span of the F pile. The internal support stress increases with depth, reaching 87.2 MPa at the bottom, with significant stress concentration at the connection of the surrounding girder. The lack of support or excessively large spacing leads to insufficient stiffness at the side span (5.3 mm displacement at the F point) and right-angle area (B/H point). The simultaneously developed sediment control integrated system, through double-line water intake, layered placement of the geotextile filter, and the collaborative construction of the water intake hole–filter layer system, achieves a 75% reduction in sediment content and a decrease in standard deviation. This approach ensures stable water quality and continuous water supply, ultimately forming a systematic solution for water intake in high-sediment rivers. Full article

The control of following motion under mesoscale gap flow fields has important applications. The flexible characteristics of the plant, wideband time-varying disturbances caused by the flow field, and requirements of high precision and low overshoot make achieving submicron level accuracy a significant challenge for traditional control methods. This study adopts the control concept of Disturbance Observer Control (DOBC) and uses H_∞ mixed-sensitivity shaping technology to design a Q-filter. Simultaneously, multiple control techniques, such as high-order reference trajectory planning, Proportional-Integral-Derivative (PID) control, low-pass filtering, notch filtering, lead lag correction, and disturbance rejection filtering, are applied to obtain a control system with a high open-loop gain, sufficient phase margin, and stable closed-loop system. Compared to traditional control methods, the new method can increase the open-loop gain by 15 times and the open-loop bandwidth by 8%. We even observed a 150-time increase of the open-loop gain at the peak frequency. Ultimately, the method achieves submicron level accuracy, making important advances in solving the control problem of semiconductor equipment. Full article

This study aimed to analyse the spatiotemporal evolution of phytoplankton community dynamics and its underlying mechanisms in the Liaoning section of the Liao River Basin in 2010, 2015, and 2020. Phytoplankton species diversity increased significantly, with an increase from three phyla and 31 species in 2010 to six phyla and 74 species in 2020. Concurrent increases in α-diversity indicated continuous improvements in habitat heterogeneity. The community structure shifted from a diatom-dominated assemblage to a green algae–diatom co-dominated configuration, contributing to an enhanced water purification capacity. The upstream agricultural zone (Tieling section) had elevated biomass and low diversity, indicating persistent non-point-source pollution stress. The midstream urban–industrial zone (Shenyang–Anshan section) emerged as a phytoplankton diversity hotspot, likely due to expanding niche availability in response to point-source pollution control. The downstream wetland zone (Panjin section) exhibited significant biomass decline and delayed diversity recovery, shaped by the dual pressures of resource competition and habitat filtering. The driving mechanism of community succession shifted from nutrient-dominated factors (NH₃-N, TN) to redox-sensitive factors (DO, pH). These findings support a ‘zoned–graded–staged’ ecological restoration strategy for the Liao River Basin and inform the use of phytoplankton as bioindicators in watershed monitoring networks. Full article

The Tuztaşı low-sulfidation epithermal Au–Ag deposit (Biga Peninsula, Türkiye) records a multi-stage hydrothermal history that can be interpreted through the trace and rare-earth-element (REE) chemistry of quartz. High-precision LA-ICP-MS analyses of five representative quartz samples (23 ablation spots; 10 analytically robust) reveal two fluid stages. Early fluids were cold, dilute meteoric waters (δ¹⁸O₍H₂O₎ ≈ −6.8 to +0.7‰), whereas later fluids circulated deeper, interacted with felsic basement rocks, and evolved in composition. Mineralized quartz displays marked enrichment in As (raw mean = 2854 ± 6821 ppm; filtered mean = 70 ± 93 ppm; one spot 16,775 ppm), K (498 ± 179 ppm), and Sb (57.8 ± 113 ppm), coupled with low Ti/Al (<0.005) and elevated Ge/Si (0.14–0.65 µmol mol⁻¹). Chondrite-normalized REE patterns show pronounced but variable LREE enrichment ((La/Yb)_n ≤ 45.3; ΣLREE/ΣHREE up to 10.8) and strongly positive Eu anomalies (δEu ≤ 9.3) with slightly negative Ce anomalies (δCe ≈ 0.29); negligible Ce–Eu covariance (r² ≈ 0.05) indicates discrete redox pulses. These signatures indicate chemically evolved, reducing fluids conducive to Au–Ag deposition. By contrast, barren quartz is characterized by lower pathfinder-element contents, less fractionated REE profiles, higher Ti/Al, and weaker Eu anomalies. A composite exploration toolkit emerges: As > 700 ppm, As/Sb > 25, Ti/Al < 0.005, Ge/Si > 0.15 µmol mol⁻¹, and δEu ≫ 1 reliably identify ore-bearing zones when integrated with δ¹⁸O data and fluid-inclusion microthermometry from earlier studies on the same vein system. This study provides one of the first systematic applications of integrated trace-element and REE analysis of quartz to a Turkish low-sulfidation epithermal system, offering an applicable model for vectoring mineralization in analogous settings worldwide. Full article