Search Results (151)

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

The co-occurring relationships between ilmenite and gangue minerals in ilmenite deposits, as well as fine mineral embedding particle sizes, are complex. During the beneficiation process, grinding ilmenite finely is necessary to achieve sufficient individual mineral dissociation and the efficient recovery of ilmenite. During this process, a large number of fine-grained minerals can easily be generated, which adversely affects flotation separation. Micro–nanobubbles have been proven to effectively enhance the flotation separation efficiency of fine-grained minerals, as their cavitation characteristics are closely related to the flotation performance of the minerals. In order to fully understand the cavitation characteristics of micro–nanobubbles and their impact on the flotation recovery of fine-grained ilmenite, a series of experiments were conducted using methods such as the bubble cavitation property test, micro-flotation experiments, zeta potential analysis, the contact angle test, adsorption capacity detection, and PBM monitoring. The results indicate that during the process of slurry cavitation, appropriate concentrations of 2-octanol, cycle treatment times, and external inflation volume are conducive to the formation of micro–nanobubbles. Compared with deionized water without cavitation, cavitated micro–nanobubble water is more beneficial for the flotation separation of fine particulate ilmenite, titanaugite, and olivine. The presence of micro–nanobubbles can effectively promote the adsorption of combined collectors on mineral surfaces, significantly enhancing the hydrophobicity of the minerals, with an even stronger promoting effect observed under the treatment of 2-octanol. Micro–nanobubbles can adsorb a portion of the collectors originally attached to the mineral surfaces, thereby decreasing the absolute value of the surface potential of the minerals, which is beneficial for mineral aggregation. The introduction of micro–nanobubbles promotes the aggregation of fine ilmenite iron ore particles into flocculent bodies. 2-Octanol can reduce the size of the micro–nanobubbles generated during the cavitation process of the mineral slurry and, to a certain extent, weaken the phenomenon of bubble coalescence, so they demonstrate a greater advantage in facilitating the aggregation phenomenon. Full article

This study presents a comprehensive dosimetric characterization and commissioning of a grid-type collimator manufactured via 3D printing using PLA-W composite filament, following an international protocol for small-field dosimetry. PLA doped with high concentrations of tungsten (>90% w/w) enables the fabrication of miniaturized collimators (<1 cm) with complex geometries, suitable for non-conventional radiotherapy applications. However, accurate assessment of spatial dose modulation is challenged by penumbra overlap between closely spaced beamlets, limiting the application of conventional instrumentation and protocols. To address this, absolute and relative dose distributions were evaluated for various radiation field configurations (number of beamlets) in both lateral and depth directions. Measurements were performed according to the IAEA TRS-483 protocol, using micro-ionization chambers and diode detectors. Additionally, long-term stability assessments were carried out to evaluate both the structural integrity and modulation performance of the printed grid over time. Point dose measurements using the same detectors were repeated after one year, and 2D surface dose distributions measured with EBT3 films were compared to SRS MapCHECK measurements two years later. The generated radiation field size of the central beamlet (FWHM) differed by less than 0.2% (15.8 mm) from the physical projection size (15.6 mm) and the lateral transmission due simultaneous beamlets resulted in FWHM variations of less than 3.8%, confirming manufacturing precision and collimator capability. Output factor measurements increased with the number of beamlets, from 0.75 for a single beamlet to 0.82 for the full beamlets configuration. No significant changes were observed in the depth of maximum dose across the different beamlets configurations (1.20 ± 0.20 cm). On the other hand, the long-term evaluations show no relevant changes in the FWHM or VPR, confirming the performance and reliability of the system. These results support the clinical feasibility and lasting performance stability of in-house manufactured grid collimators using PLA-W filaments and accessible 3D printing technology. Full article

Accurate estimation of anthropogenic CO₂ emissions is crucial for effective climate change mitigation policies. This study aims to improve CO₂ emission estimates in China using remote sensing measurements of column-averaged dry air mole fractions of CO₂ (XCO₂) and a neural network approach. We evaluated XCO₂ anomalies derived from three background XCO₂ concentration approaches: CHN (national median), LAT (10-degree latitudinal median), and NE (N-nearest non-emission grids average). We then applied the Generalized Regression Neural Network model, combined with a partition modeling strategy using the K-means clustering algorithm, to estimate CO₂ emissions based on XCO₂ anomalies, net primary productivity, and population data. The results indicate that the NE method either outperformed or was at least comparable to the LAT method, while the CHN method performed the worst. The partition modeling strategy and inclusion of population data effectively improved CO₂ emission estimates. Specifically, increasing the number of partitions from 1 to 30 using the NE method resulted in mean absolute error (MAE) values decreasing from 0.254 to 0.122 gC/m²/day, while incorporating population data led to a decrease in MAE values between 0.036 and 0.269 gC/m²/day for different partitions. The present methods and findings offer critical insights for supporting government policy-making and target-setting. Full article

This study investigated the effects of two nanofertilizers (NFs): copper nanoparticles (NPs) synthesised using cotton (CuC) and chitosan (CuCh) as well as two biofertilizers (BFs), nopal extract (NE) and commercial Biojal^® worm humus (WH), on the growth of black bean seedlings. The treatments consisted of applying 50 mg L⁻¹ of CuC, 50 mg L⁻¹ of CuCh, 50 mg L⁻¹ of NE, 100 mg L⁻¹ of WH, their respective combinations, and an absolute control that consisted of distilled water. The CuC, CuCh, WH, and WH + CuC leaf applications resulted in an increase in plant height by 34.4%, 19.5%, 25.7%, and 20.3%, respectively. Furthermore, the CuC and WH applications led to an increase in the number of leaves by 53.2% and 36.9%, respectively. However, the addition of NE + CuC resulted in a 37.4% decrease in dry weight. A total of 44 metabolites were identified, including 7 sugars, 17 amino acids, 12 organic acids, 4 nucleosides, 1 alcohol, and 3 miscellaneous metabolites. The NE + CuC and WH treatments resulted in a notably higher concentration of various metabolites, including amino acids, organic acids, and sugars. Conversely, the CuCh treatment led to an increased concentration of nucleosides, amino acids, trigonelline, and nicotinamide adenine dinucleotide (NAD⁺). Full article

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), and the Squeeze-and-Excitation (SE) attention mechanism. First, WOA dynamically optimizes VMD parameters (mode number k and penalty factor α to effectively separate noise and valid signals, avoiding modal aliasing). Then, SSA globally searches for optimal LSTM hyperparameters (hidden layer nodes, learning rate, etc.) to enhance feature mining for non-continuous data. The SE attention mechanism recalibrates channel-wise feature weights to capture critical time-series patterns. Experimental validation using real transformer oil data demonstrates that the model outperforms existing methods in prediction accuracy and computational efficiency. For instance, the CH₄ test set achieves a Mean Absolute Error (MAE) of 0.17996 μL/L, a Mean Absolute Percentage Error (MAPE) of 1.4423%, and an average runtime of 82.7 s, making it significantly faster than CEEMDAN-based models. These results provide robust technical support for transformer fault prediction and condition-based maintenance, highlighting the model’s effectiveness in handling non-stationary time-series data. Full article

Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) for feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model to predict TN concentrations in the Dongjiang River in China. A dataset of 28,922 time-series data points was collected from seven sampling sites along the Dongjiang River, spanning from November 2020 to February 2023. The DCC method identified conductivity, Permanganate Index (COD_Mn), and total phosphorus as the most significant predictors for TN levels. The At-CBiLSTM model, optimized with a time step of three, outperformed other models, including standalone Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Neural Network LSTM (CNN-LSTM), and Attention-LSTM variants, achieving excellent performance with the following metrics: mean absolute error (MAE) = 0.032, mean squared error (MSE) = 0.005, mean absolute percentage error (MAPE) = 0.218, and root mean squared error (RMSE) = 0.045. Importantly, increasing the number of input features beyond three variables led to a decline in model accuracy, underscoring the importance of DCC-driven feature selection. The results highlight that combining DCC with deep learning models, particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies and improves prediction accuracy. This approach provides a solid foundation for real-time water quality monitoring and can inform targeted pollution control strategies in river ecosystems. Full article

Deep neural networks have been widely applied for gas concentration estimation in low-cost gas sensor arrays; however, their dependency on sample distribution remains a significant challenge. Current research indicates that deep learning models are susceptible to sample imbalance, where their predictive accuracy is strongly influenced by the number of available samples. In sensor arrays used for monitoring indoor and outdoor harmful gas emissions, most response values remain within a normal range, while only a limited number exhibit high response values. Addressing this imbalance typically requires assigning weights to different classes or pruning datasets; however, the cross-sensitivity of sensors and the limited availability of datasets complicate this approach. In this study, we investigated the impact of sample imbalance on model performance and proposed a simulated sensor generative adversarial network (SSGAN) to generate synthetic sensor response values alongside their corresponding gas concentrations. A multiple-sensor generator was designed to produce sensor array response values paired with gas concentrations, while discriminators ensured that generated samples closely resembled real instances without being identical. Furthermore, a customized generative loss function was developed to optimize the training of the SSGAN. To validate our approach, experiments were conducted on the UCI Machine Air Quality dataset using a traditional convolutional neural network (CNN), a backpropagation neural network (BPNN), and a custom-designed attention block. The results demonstrated that SSGAN effectively reduced the average absolute error of the three target models by 4.45%, 12.06%, and 3.08%, respectively. Full article

Compared with traditional single-view and radiometric-only observations, multiangle polarimetric observations of water-leaving radiation play a crucial role in enhancing the retrieval of ocean constituents and aerosol microphysical properties. In this study, the impacts of uncertainties in the degree of polarization (DOP) of water-leaving radiance (L_w) on the retrieval of oceanic constituents and inherent optical properties (IOPs) were investigated via global radiative transfer (RT) simulations and the fully connected U-Net (FCUN) model. The uncertainties in the retrieval of oceanic constituents and IOPs were further investigated with various sensor azimuth angles. The results indicated that the global mean absolute percentage errors (MAPEs) for differing oceanic constituents and IOPs significantly decreased as the number of observation angles increased. Taking the retrieval of Chla as an example, the global MAPEs between the FCUN predictions and RT simulation inputs for Chla concentrations under differing observation angles were 7.41%, 3.76%, 2.70%, 2.44%, 2.62%, and 1.82%. Moreover, the MAPEs at sensor azimuth angles of 0° and 30° were significantly lower than those at other azimuth angles for the single-view observations. As the number of observation angles increased, the variation in MAPEs with the sensor azimuth angle gradually weakened. Furthermore, the impact of errors in the L_w DOP on the retrieval uncertainties decreased as the number of observation angles increased, and the global MAPEs of Chla after adding the various random instrument noises were 46.56% (46.91%), 6.59% (7.21%), 5.21% (5.79%), 4.72% (4.98%), 3.99% (4.52%), and 3.64% (4.03%). Overall, the multiangle polarimetric observations can suppress or balance the impact of uncertainties in the L_w DOP on the retrieval of oceanic constituents and IOPs. Full article

This study addresses the agricultural impact of the grasshopper Sphenarium purpurascens and evaluates the efficacy of entomopathogenic fungi (EPF), Beauveria bassiana, and Metarhizium robertsii, formulated in vegetable oil emulsions as sustainable pest control agents. The losses caused by S. purpurascens at different developmental stages (N4, N5, and adult) were assessed in five economically significant crops (Medicago sativa, Zea mays, Helianthus sp., Cynodon dactylon, and Cucurbita pepo), revealing a marked preference for Helianthus sp. and C. pepo, with consumption rates reaching 0.92 g/48 h during N4 and N5 stages, while adults showed preference for M. sativa (1.18 g/48 h) and Z. mays (1.15 g/48 h). The viability of EPF in oil emulsions (20% and 40% concentrations) was evaluated, demonstrating that formulations with Azadirachta indica and Moringa oleifera maintained over 99% fungal viability compared to the control absolute with distilled water (DW). The effectiveness of EPF against S. purpurascens adults was tested, with EPF on M. robertsii combined with Persea americana achieving 100% mortality within 72 h. Finally, the pathogenicity and dispersion of EPF in oil emulsions were evaluated, demonstrating that, at 240 h, the B. bassiana + A. indica strain (with three inoculated insects) achieved 100% mortality. It was observed that the number of inoculated adults directly influenced the mortality of S. purpurascens. These findings highlight the potential of EPF as a sustainable pest management strategy, emphasizing the need for further field trials to optimize its application and mitigate agricultural losses caused by S. purpurascens. Full article

In order to cope with the extremely difficult challenges of water pollution control, China has widely implemented the river chief system. The water quality monitoring of surface water environment, as a solid defense line to safeguard human health and ecosystem balance, is of great importance in the river chief system. As a well-known island county in China, Yuhuan City holds even more precious water resources. Leveraging machine learning technology to develop water quality prediction models is of great significance for enhancing the monitoring and evaluation of surface water environment quality. This case study aims to evaluate the effectiveness of six machine learning models in predicting water quality index (CWQI) and uses SHAP (Shapley Additive exPlans) as an interpretability analysis method to deeply analyze the contribution of each variable to the model’s prediction results. The research results show that all models exhibited good performance in predicting CWQI, and as the number of significantly correlated variables in the input variables increased, the prediction accuracy of the models also showed a gradual improvement trend. Under the optimal input variable combination, the Extreme Gradient Boosting model demonstrated the best prediction performance, with a root mean square error (RMSE) of 0.7081, a mean absolute error (MAE) of 0.4702, and an adjusted coefficient of determination (Adj.R²) of 0.6400. Through SHAP analysis, we found that the concentrations of TP (total phosphorus), NH3-N (ammonia nitrogen), and CODCr (chemical oxygen demand) have a significant impact on the prediction of CWQI in Yuhuan City. The implementation of the river chief system not only enhances the pertinence and effectiveness of water quality management, but also provides richer and more accurate data support for machine learning models, further improving the accuracy and reliability of water quality prediction models. Full article

To address the challenge of the real-time monitoring of alumina concentrations during the production process, this paper employs a Deep Belief Network (DBN) within the framework of deep learning to predict alumina concentration. Additionally, the improved Grey Wolf Optimizer (IGWO) is utilized to optimize key parameters of the DBN model, including the number of hidden layer nodes, reverse iteration count, and learning rate. An IGWO-DBN hybrid model is then constructed and compared against DBN models optimized by other techniques, such as the Sparrow Search Algorithm (SSA) and Particle Swarm Optimization (PSO), to evaluate the predictive performance. The comparative analysis reveals that, in terms of predictive accuracy, the IGWO-DBN model outperforms both the SSA-DBN and PSO-DBN models. Specifically, it achieves lower root mean square errors (RMSE) and mean absolute errors (MAE), alongside a higher coefficient of determination (R²). Furthermore, the IGWO-DBN model exhibits a faster convergence rate and a lower final convergence value, indicating superior generalization ability and robustness. Furthermore, the IGWO-DBN model not only demonstrates significant advantages in prediction accuracy for alumina concentration but also substantially reduces model training time through its efficient parameter optimization mechanism. The successful implementation of this model provides robust support for the intelligent and refined management of the aluminum electrolysis industry, aiding enterprises in reducing costs, improving production efficiency, and advancing the green and sustainable development of the industry. Full article

Background: Osteogenesis imperfecta (OI) is a rare monogenic connective tissue disorder characterized by fragility of bones and recurrent fractures. In addition to the hereditary component, there are a number of factors that influence the course of the disease, the contribution of which is poorly understood, in particular the levels of micronutrients. Methods: A cross-sectional study was conducted involving 45 with OI and 45 healthy individuals. The concentrations of micronutrients (calcium, copper, inorganic phosphorus, zinc, and magnesium) and bone mineral density (BMD) were evaluated in all the participants. Results: The concentrations of micronutrients in all the groups were within the reference values. In the OI overall, magnesium and copper were elevated, and phosphorus and zinc were lower. Type I exhibited higher concentrations of magnesium and copper and the lowest phosphorus; type III was associated with lower zinc, type IV with lower calcium and higher copper, and type V with the lowest phosphorus. OI overall was associated with lower BMD values. A correlational analysis in the OI group showed that the number of fractures correlated with BMD in absolute values but not with the Z-score. Conclusions: The obtained data emphasize the importance of the levels of micronutrients in the pathogenesis of connective tissue diseases, in particular OI. As in the results of previous studies, the levels of micronutrients were within the population norm, which probably requires the development of individual criteria for the content of substances in this category of patients. Full article

This study presents an automated control system for wastewater treatment, developed using machine learning (ML) models integrated into a Supervisory Control and Data Acquisition (SCADA) framework. The experimental setup focused on a laboratory-scale Aerobic Granular Sludge (AGS) reactor, which utilized synthetic wastewater to model real-world conditions. The machine learning models, specifically N-BEATS and Temporal Fusion Transformers (TFTs), were trained to predict Biological Oxygen Demand (BOD₅) values using historical data and real-time influent contaminant concentrations obtained from online sensors. This predictive approach proved essential due to the absence of direct online BOD₅ measurements and an inconsistent relationship between BOD₅ and Chemical Oxygen Demand (COD), with a correlation of approximately 0.4. Evaluation results showed that the N-BEATS model demonstrated the highest accuracy, achieving a Mean Absolute Error (MAE) of 0.988 and an R² of 0.901. The integration of the N-BEATS model into the SCADA system enabled precise, real-time adjustments to reactor parameters, including sludge dose and aeration intensity, leading to significant improvements in granulation stability. The system effectively reduced the standard deviation of organic load fluctuations by 2.6 times, from 0.024 to 0.006, thereby stabilizing the granulation process within the AGS reactor. Residual analysis suggested a minor bias, likely due to the limited number of features in the model, indicating potential improvements through additional data inputs. This research demonstrates the value of machine learning-driven predictive control for wastewater treatment, offering a resilient solution for dynamic environments. By facilitating proactive management, this approach supports the scalability of wastewater treatment technologies while enhancing treatment efficiency and operational sustainability. Full article

Wavelet analysis algorithms in combination with thresholding procedures are widely used in nonparametric regression problems when estimating a signal function from noisy data. The advantages of these methods lie in their computational efficiency and the ability to adapt to the local features of the estimated function. It is usually assumed that the signal function belongs to some special class. For example, it can be piecewise continuous or piecewise differentiable and have a compact support. These assumptions, as a rule, allow the signal function to be economically represented on some specially selected basis in such a way that the useful signal is concentrated in a relatively small number of large absolute value expansion coefficients. Then, thresholding is performed to remove the noise coefficients. Typically, the noise distribution is assumed to be additive and Gaussian. This model is well studied in the literature, and various types of thresholding and parameter selection strategies adapted for specific applications have been proposed. The risk analysis of thresholding methods is an important practical task, since it makes it possible to assess the quality of both the methods themselves and the equipment used for processing. Most of the studies in this area investigate the asymptotic order of the theoretical risk. In practical situations, the theoretical risk cannot be calculated because it depends explicitly on the unobserved, noise-free signal. However, a statistical risk estimate constructed on the basis of the observed data can also be used to assess the quality of noise reduction methods. In this paper, a model of a signal contaminated with additive Gaussian noise is considered, and the general formulation of the thresholding problem with threshold functions belonging to a special class is discussed. Lower bounds are obtained for the threshold values that minimize the unbiased risk estimate. Conditions are also given under which this risk estimate is asymptotically normal and strongly consistent. The results of these studies can provide the basis for further research in the field of constructing confidence intervals and obtaining estimates of the convergence rate, which, in turn, will make it possible to obtain specific values of errors in signal processing for a wide range of thresholding methods. Full article