Search Results (15,912)

Hydrogels have emerged as a promising material for the removal of heavy metal ions from contaminated water owing to their high water absorption capacity and biocompatibility. Despite notable advancements in improving the adsorptive capacity of hydrogels, the demand for a more efficient structure persists. Here, we explore the ion adsorption performance of crosslinked hydrogels based on chitosan and sodium alginate with various ratios of carbon nanotubes (CNT) and graphene platelets (GNP). This study highlights the adsorption of chromium ions and the thermal stability of hydrogels for pure, single-particle, and hybrid nanocomposites. The results depict a uniform microstructure attained when CNT, GNP, or both are implemented into the hydrogel due to the strong interaction of functional moieties. The incorporation of CNT and GNP manipulates the crystalline structure of the hydrogels, lowering their crystallinity by around 28% and 13%, respectively. The synergistic effect of CNT and GNP in hybrid hydrogels raises the decomposition temperature by 16%, indicating a favorable interplay interaction between nanoparticles and polymers. Calculations of the adsorption capacity accentuate such a mutual effect between CNT and GNP in various loads of ion capture from aqueous solutions. Kinetic models fitted to the hydrogel nanocomposites reveal that the pseudo-second-order model aligns better with the experimental data in comparison to the pseudo-first-order and intraparticle diffusion models, addressing the adsorption mechanisms while capturing chromium ions. Full article

Accurate forecasting of wind power is crucial for addressing energy demands, promoting sustainable energy practices, and mitigating environmental challenges. In order to improve the prediction accuracy of wind power, a VMD-CNN-BiLSTM hybrid model with physical constraints is proposed in this paper. Initially, the isolation forest algorithm identifies samples that deviate from actual power outputs, and the LightGBM algorithm is used to reconstruct the abnormal samples. Then, leveraging the variational mode decomposition (VMD) approach, the reconstructed data are decomposed into 13 sub-signals. Each sub-signal is trained using a CNN-BiLSTM model, yielding individual prediction results. Finally, the XGBoost algorithm is introduced to add the physical penalty term to the loss function. The predicted value of each sub-signal is taken as the input to get the predicted result of wind power. The hybrid model is applied to the 12 h forecast of a wind farm in Zhangjiakou City, Hebei province. Compared with other hybrid forecasting models, this model has the highest score on five performance indicators and can provide reference for wind farm generation planning, safe grid connection, real-time power dispatching, and practical application of sustainable energy. Full article

To address the issues of insufficient samples, limited scene diversity, missing perspectives, and low resolution in existing UAV-based pedestrian detection datasets, this paper proposes a novel UAV-based pedestrian detection benchmark dataset named the Novel Surveillance View (NSV). This dataset encompasses diverse scenes and pedestrian information captured from multiple perspectives, and introduces an innovative data mining approach that leverages tracking and optical flow information. This approach significantly improves data acquisition efficiency while ensuring annotation quality. Furthermore, an improved pedestrian detection method is proposed to overcome the performance degradation caused by significant perspective changes in top-down UAV views. Firstly, the View-Agnostic Decomposition (VAD) module decouples features into perspective-dependent and perspective-independent branches to enhance the model’s generalization ability to perspective variations. Secondly, the Deformable Conv-BN-SiLU (DCBS) module dynamically adjusts the receptive field shape to better adapt to the geometric deformations of pedestrians. Finally, the Context-Aware Pyramid Spatial Attention (CPSA) module integrates multi-scale features with attention mechanisms to address the challenge of drastic target scale variations. The experimental results demonstrate that the proposed method improves the mean Average Precision (mAP) by 9% on the NSV dataset, thereby validating that the approach effectively enhances pedestrian detection accuracy from UAV perspectives by optimizing perspective features. Full article

To address the curtailment phenomenon caused by the high penetration of renewable energy in the system, an optimization scheduling strategy is proposed, considering the full process of electrolytic aluminum production and the integration of thermal power and energy storage. Firstly, to explore the differentiated response capabilities of various devices such as high-energy-consuming electrolytic aluminum units, thermal power units, and energy storage devices to effectively address uncertain variables in the power system, a Variational Mode Decomposition method is introduced to construct differentiated response methods for its low-frequency, medium-frequency, and high-frequency components. Secondly, based on the real production regulation characteristics of the high-energy-consuming electrolytic aluminum load, and considering various influencing factors such as current, temperature, and output, a scheduling model involving electrolytic aluminum load is established. Then, the power generation characteristics in other processes of electrolytic aluminum production are fully exploited to achieve energy storage conversion, replacing the energy storage batteries that respond to high-frequency components. Finally, by combining the deep peak-shaving model of thermal power units, an optimization scheduling model is established for the joint operation of the full electrolytic aluminum production load and thermal-power-storage systems, with the goal of minimizing system operating costs. The case study results show that the proposed model can significantly enhance the system’s renewable energy absorption capacity, reduce energy storage installations, and enhance the economic efficiency of the system’s peak-shaving operation. Full article

In this paper, we introduce a shipping logistics network optimization method for construction waste recycling. In our case, construction waste is transported by a relay mode integrating land transportation, shipping transportation, and land transportation. Under the influence of urban economic life, the quantity (demand) of construction waste is uncertain and stochastic. Considering the randomness of construction waste generation, a two-stage stochastic integer programming model for the design of a shipping logistics network for construction waste recycling is proposed, and an accurate algorithm based on Benders decomposition is presented. Based on an actual case in Shanghai, numerical experiments are carried out to evaluate the efficacy of the proposed model and algorithm. Based on an actual case study in Shanghai, numerical experiments demonstrate that the proposed model can help to reduce transportation costs of construction waste. Sensitivity analysis highlights that factors like the penalty for untransported waste and capacity constraints play a crucial role in network optimization decisions. The findings provide valuable theoretical support for developing more efficient and sustainable logistics networks for construction waste recycling. Full article

Poa pratensis is one of the world’s most widely planted cold-season turfgrasses, with good quality but poor drought resistance. When plants suffer from stress, the metabolism of soluble sugar takes place, which is a dynamic process involving both degradation and synthesis. A detailed and in-depth study of the sugar metabolism process in plants’ response to stress will help us to understand the internal mechanism of plant adaptation to stress. In this study, the ‘10-202’ ecotype with drought resistance and the ‘Blue moon’ ecotype with drought sensitivity were used to explore the sugar metabolism process in response to drought stress. The results showed that drought stress induced sucrose accumulation in the leaves and roots, promoted increases in SPS, S-AI, and PpN/A-Inv activities, as well as gene expression in the leaves, and changed the content and distribution of fructose, glucose, sucrose, maltose, and trehalose in vivo. Compared with ‘Blue moon’, ‘10-202’ had higher trehalose content in leaves under normal conditions, and its roots could accumulate more fructose and glucose to maintain the balance of osmotic potential and redox under drought stress. Meanwhile, PpSWEET1b, -12, and -15 in the leaves and roots of the two ecotypes were significantly induced by drought stress. The improvements in sucrose accumulation and decomposition efficiency in leaves under drought stress is conducive to enhancing drought resistance in plants. PpSWEET1b plays a vital role in regulating the sugar transport process of drought tolerance in turfgrass. Full article

Research on converting methane to hydrogen has gained more attention due to the availability of methane reserves and the global focus on sustainable and environmentally friendly energy sources. The decomposition of methane through catalysis (CDM) has excellent potential to produce clean hydrogen and valuable carbon products. However, developing catalysts that are both active and stable is a highly challenging area of research. Using titanium isopropoxide as a precursor and different loadings of TiO₂ (10 wt.%, 20 wt.%, and 30 wt.%), alumina has been coated with TiO₂ in a single-step hydrothermal synthesis procedure. These synthesized materials are examined as possible support materials for CDM; different wt.% of iron is loaded onto the synthesized support material using a co-precipitation method to enhance the methane conversion via a decomposition reaction. The result shows that the 20 wt.% Fe/20 wt.% Ti-Al (20Fe/20Ti-Al) catalyst demonstrates remarkable stability and exhibits superior performance, reaching a conversion rate of methane of 94% with hydrogen production of 84% after 4 h. The outstanding performance is primarily due to the moderate interaction between the support and the active metal, as well as the presence of the rutile phase. The 20Fe/30Ti-Al catalyst exhibited lower activity than the other catalysts, achieving a methane conversion of 85% and hydrogen production of 79% during the reaction. Raman and XRD analysis revealed that all the catalysts generated graphitic carbon, with the 20Fe/20Ti-Al catalyst specifically producing single-walled carbon nanotubes. Full article

Due to their low greenhouse effect and exceptional insulating properties, C₄F₇N-CO₂ gas mixtures have garnered significant attention. In particular, understanding the decomposition characteristics of C₄F₇N-CO₂ is crucial for their practical use as an eco-friendly dielectric medium. At elevated temperatures, the pyrolysis of C₄F₇N produces high concentrations of CFN, CF₃, and C₂F₂, along with lower levels of C₃F₅, C₄F₆N, C₂F, and CN. A further increase in temperature may lead to the decomposition of CO₂ into CO and additional components such as C₂, C₂F₃, C₃F₄, C₄F₇ and C₃F₆, CF, CO, C₃F₇, C₃F₂, C₃F, C₃F₃, C₃F₃N, C₃, CF₂, and CF₂N. Under electrical discharge conditions, the decomposition of CO₂ becomes more pronounced, forming products like CO, C₂O, O₂, C₂O₂, and C₂O₄, with up to 25 decomposition components observed. These include products originated from both C₄F₇N and CO₂ and their combinations. In ultra-high electric field intensities, only small molecules such as O₂, C₂, C₃, and N₂ are detected among the decomposition products. This study aims to provide theoretical insights and valuable data to advance research into the decomposition behavior and practical engineering applications of C₄F₇N-CO₂ gas mixtures under extreme conditions. Full article

Gampsocleis gratiosa Brunner von Wattenwyl, 1862, is a type of omnivorous chirping insect with a long history of artificial breeding. It has high economic value and is also an excellent orthopteran model organism. In this study, 12 types of culture media combined with 16S rRNA sequencing were employed to isolate 838 bacterial strains from the gut of G. gratiosa. After sequence comparison, a total of 98 species of bacteria were identified, belonging to 3 phyla, 5 classes, 11 orders, 20 families, and 45 genera. Firmicutes and Proteobacteria accounted for the majority (92.86%). At the order level, Enterobacteriaceae, Bacillales, and Lactobacillales predominated (79.59%). At the genus level, Klebsiella (11.22%) and Enterococcus (7.14%) predominated. This study also enumerated the strain morphological, physiological and biochemical properties of 98 species of bacteria, including colony morphology, Gram staining, bacterial motility test, temperature gradient growth, pH gradient growth, citrate utilization test, temperature oxidase test, contact enzyme test, methyl red test, V-P test, indole test, gelatin liquefaction test, nitrate reduction test, hydrogen sulfide test, starch hydrolysis test, cellulose decomposition test, esterase (corn oil) test and antibiotic susceptibility testing. Additionally, 16 antibiotics were utilized to test the bacterial susceptibility of the strains. This study explored the types and community structure of some culturable microorganisms in the intestinal tract of G. gratiosa and recorded their physiological characteristics. These data reflect the physiological functions of the intestinal microorganisms of G. gratiosa and provide support for subsequent research on the interaction mechanism between microorganisms and their hosts. Full article

Anchor bolts, such as rock bolts and concrete anchors, are widely used in civil, geotechnical, and mining engineering for anchorage and ground support. They are used in retaining walls, dry docks, dams, mines, and prestressed concrete structures. Evaluating the grouting condition of anchor bolts is essential to ensure the safety of these applications. Spectrum techniques have been used to develop non-destructive methods for estimating the grouting quality of grouted anchor bolts. The spectrum methods include fast Fourier transform, time–frequency analysis, wavelet transform analysis, and empirical mode decomposition. In this study, we introduce the parameter-optimized variational mode decomposition (VMD) method for the spectrum analysis of impact echo signals of anchor bolts. This method overcomes the difficulty of conventional spectrum methods that cannot separate highly coupled natural modes for advanced analysis. The parameter-optimized VMD method enables the generation of a new evaluation index for quantifying bolt grouting conditions, which has the potential to significantly enhance the quality evaluation of anchor bolts compared with conventional analysis of natural frequencies. This study uses impact response to establish a new benchmark for the integrity diagnosis of anchor bolts, paving the way for more accurate and reliable safety assessments. Full article

The interaction between secondary phyllosilicate minerals and straw is crucial for preserving soil organic carbon (SOC) and fertility. However, the specific mechanism through which these minerals affect straw decomposition and its products in northeast China’s black soil remains unclear. In this study, montmorillonite, illite, and vermiculite were mixed with quartz sand and maize straw, inoculated with microbes, and incubated to analyze the effects of different secondary phyllosilicate minerals on the degradation of organic components in maize straw and the formation of soil humus. The results showed that montmorillonite significantly facilitated the decomposition of maize straw hemicellulose and lignin, which decreased by 95.85% and 76.38%, respectively. Conversely, vermiculite decelerated hemicellulose and lignin degradation. Regarding soil organic acids, lactic acid and malic acid were predominant, with the highest content being found after the montmorillonite treatment. Montmorillonite was the most effective in enhancing extractable humic-like substances, which increased by 71.68%. Montmorillonite increased the content of G0 (water dispersion group), G1 (sodium ion dispersion group), and G2 (sodium grinding dispersion group) complexes. The addition of secondary phyllosilicate minerals increased the organic carbon (OC) content in the G0, G1, and G2 samples, with montmorillonite demonstrating the most pronounced effect. Secondary phyllosilicate minerals increased the abundance of fungi, particularly Ascomycota, with the highest abundance being found after the montmorillonite treatment. In conclusion, our results indicated that montmorillonite facilitated the decomposition of lignocellulose in maize straw, enhanced the accumulation of humus, and promoted the formation of organic–mineral complexes. These findings provide valuable insights into the interaction between secondary phyllosilicate minerals and maize straw and have important implications for improving the quality of black soil in northeast China. Full article

The use of plastics, valued for its affordability, durability, and convenience, has grown significantly with the advancement of industry. Paradoxically, these very properties of plastics have also led to significant environmental challenges. Plastics are highly resistant to decomposition, resulting in their accumulation on land, where they eventually enter aquatic environments, due to natural processes or human activities. Among these plastics, microplastics, which are tiny plastic particles, are particularly concerning when they enter aquatic ecosystems, including rivers and seas. Their small size makes them easily ingestible by aquatic organisms, either by mistake or through natural feeding behaviors, which poses serious risks. Moreover, microplastics readily adsorb other pollutants present in aquatic environments, creating pollutant complexes that can have a synergistic impact, magnifying their harmful effects compared to microplastics or pollutants acting alone. As a result, extensive research has focused on understanding the effects of microplastics on aquatic organisms. Numerous studies have demonstrated that aquatic organisms exposed to microplastics, either alone or in combination with other pollutants, exhibit abnormal hatching, development, and growth. Additionally, many genes, particularly those associated with the antioxidant system, display abnormal expression patterns in these conditions. In this review, we examine these impacts, by discussing specific studies that explore changes in phenotype and gene expression in aquatic organisms exposed to microplastics, both independently and in combination with adsorbed pollutants. Full article

In recent years, the adverse effects of climate change have increased rapidly worldwide, driving countries to transition to clean energy sources such as solar and wind. However, these energies face challenges such as cloud cover, precipitation, wind speed, and temperature, which introduce variability and intermittency in power generation, making integration into the interconnected grid difficult. To achieve this, we present a novel hybrid deep learning model, CEEMDAN-CNN-ATT-LSTM, for short- and medium-term solar irradiance prediction. The model utilizes complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN) to extract intrinsic seasonal patterns in solar irradiance. In addition, it employs a hybrid encoder-decoder framework that combines convolutional neural networks (CNN) to capture spatial relationships between variables, an attention mechanism (ATT) to identify long-term patterns, and a long short-term memory (LSTM) network to capture short-term dependencies in time series data. This model has been validated using meteorological data in a more than 2400 masl region characterized by complex climatic conditions south of Ecuador. It was able to predict irradiance at 1, 6, and 12 h horizons, with a mean absolute error (MAE) of 99.89 W/m² in winter and 110.13 W/m² in summer, outperforming the reference methods of this study. These results demonstrate that our model represents progress in contributing to the scientific community in the field of solar energy in environments with high climatic variability and its applicability in real scenarios. Full article

This study addresses the limitations of traditional polyimides (PIs) in high-frequency and high-temperature soft electronic applications, and then introducing trifluoromethylbenzene (TFMB) into the molecular structure and employing various diamines as connecting components to solve the bottleneck. The innovative molecular design enhances thermal, mechanical, and dielectric properties, overcoming challenges in balancing these performances. The optimized fluorinated PI (TPPI50) exhibits exceptional properties, including a glass transition temperature of 402 °C, thermal decomposition temperature of 563 °C, tensile strength of 232.73 MPa, elongation at break of 26.26%, and dielectric constant of 2.312 at 1 MHz with a dielectric loss as low as 0.00676. These improvements are attributed to the unique synergy between TFMB’s fluorinated groups, which reduce molecular polarization, and the biphenyl structure, which reinforces chain stability. Compared to conventional PIs, TPPI50 demonstrates superior comprehensive performance, making it highly suitable for soft circuits, high-frequency signal transmission, and advanced applications such as wearable devices and biosensors. This study provides a robust framework for industrial applications, offering a path to next-generation soft electronics with enhanced reliability and performance. Full article

Educational institutions are grappling with declining enrollments and low mathematical achievements. This study investigates how a math placement exam (ALEKS) influences enrollment in Calculus 1 and student persistence, taking into account academic preparation and demographic factors. It also evaluates the effects of remedial math courses for students near the placement cutoff. Using Astin’s input–environment–outcome model, this study analyzed data from 3380 students employing a Kitagawa-Oaxaca-Blinder decomposition and fuzzy regression discontinuity. These methods were used to identify unexplained differences across demographic groups and capture outcomes near the math placement cutoff. Based on the findings, a cutoff of 80% for the ALEKS exam is appropriate. This study underscores the role of math placement exams in shaping engineering enrollment and student success. These findings prompt reevaluating placement strategies and support mechanisms, particularly for URM, first-generation, and female students, to enhance equity and retention in engineering. Full article