Search Results (19,277)

Directly degrading the dyes in the wastewater is a missed opportunity. Herein, we propose a solution employing a microfluidic chip to construct a photocatalytic fuel cell (PFC) system, which can efficiently degrade tetracycline while generating electricity simultaneously under visible-light irradiation. This approach utilizes the photogenerated electrons from the dye Rhodamine B (RhB), which are effectively transferred through a gold layer to activate persulfate in water, leading to enhanced tetracycline degradation. Experimental results reveal that within one hour of reaction duration, the degradation efficiency of tetracycline within the PFC system was doubled. At a persulfate (PS) concentration of 2 mM, the system’s open-circuit voltage and short-circuit photocurrent density reached 0.26 V and 0.00239 mA·cm⁻² respectively, both exceeding the values detected at 0.5 mM PS. Additionally, the system’s power density was triple that at 0.5 mM PS. Notably, when the PS concentration in the system was elevated from 0.5 mM to 2 mM, the degradation efficiency of tetracycline witnessed a significant boost from 35.16% to 60.78%. This approach proffers a novel tactic for harnessing dye waste via microfluidic devices. The PFC system accomplishes not only the degradation of dyes and antibiotics but also the generation of electrical energy, substantially enhancing the energy utilization efficiency. Full article

Modern radar systems commonly utilize monopulse angle estimation techniques for target angle estimation, with the phase comparison method being one of the most widely adopted approaches. While the phase comparison method achieves high estimation precision, it is highly susceptible to noise and exhibits a suboptimal performance under low Signal-to-Noise Ratio (SNR) conditions, leading to a high SNR threshold. Moreover, conventional monopulse angle estimation methods provide limited target information, as a single measurement cannot reveal the target’s motion direction. To address these shortcomings, a novel approach based on the phase comparison method is proposed in this study, with the variation in the wave path difference modeled as a first-order motion model. By accumulating the conjugate-multiplied signals over multiple time steps, the Wave Path Difference Rate of Change Ambiguity Function (WPD-ROC AF) is constructed. A fast algorithm employing the 2D Chirp-Z Transform (2D-CZT) is proposed, enabling multi-pulse angle estimation through the identification of frequency and phase values corresponding to spectral peaks. Simulation results validate that the proposed method outperforms traditional monopulse angle estimation techniques under low-SNR conditions and effectively suppresses static clutter interference. Furthermore, the sign of the WPD-ROC AF is shown to be correlated with the target’s motion direction, providing practical utility for determining the direction of movement in remote sensing scenarios. Full article

Methanol synthesis can utilize the product gas from biomass gasification and the hydrogen generated from water electrolysis. Biomass gasification, as an upstream process, affects the subsequent hydrogen supplement amount and has a direct relationship with the methanol yield. Fluidized bed oxygen-enriched gasification has a particular advantage for biomass and is expected to utilize the remaining oxygen from water electrolysis. In this study, the effects of operating parameters, including the equivalence ratio ER, temperature T, oxygen percentage OP in oxygen-enriched air, steam-to-wood pellets mass ratio S/W, and fluidization velocity u_g, as well as the choice of bed materials, on the volume fractions of the gas products and the gas yield from the fluidized bed oxygen-enriched gasification of wood pellets were investigated. The effects of the generation characteristics of gas products on the hydrogen supplement amount and the methanol yield were also analyzed. The results showed that the volume fraction of H₂ reached its peak values of 10.47% and 18.49% at an ER value of 0.28 and a u_g value of 0.187 m/s, respectively. The methanol yield reached its peak value of 0.54 kg/kg at a u_g value of 0.155 m/s. The volume fraction of H₂ increased from 6.13% to 11.74% with an increasing temperature from 650 °C to 850 °C, increased from 5.72% to 10.77% with an increasing OP value from 21% to 35%, and increased from 12.39% to 19.06% with an increasing S/W value from 0.16 to 0.38. The methanol yield could be improved by increasing the ER value, T value, OP value, or S/W value. When the bed materials were changed from quartz sands to dolomite granules, the H₂ volume fraction significantly increased and the hydrogen supplement amount required for methanol synthesis reduced. Full article

Phosphorus is the primary contributor to eutrophication in water bodies, and identifying phosphorus sources in rivers is crucial for controlling phosphorus pollution and subsequent eutrophication. Although phosphate oxygen isotopes (δ¹⁸O_P) have the capacity to trace phosphorus sources and cycling in water and sediments, they have not been used in small- to medium-sized watersheds, such as the Xiaodongjiang River (XDJ), which is located in an agricultural watershed, source–complex region of southern China. This study employed phosphate oxygen isotope techniques in combination with a land-use-based mixed end-member model and the MixSIAR Bayesian mixing model to quantitatively determine potential phosphorus sources in surface water and sediments. The δ¹⁸O_P values of the surface water ranged from 5.72‰ to 15.02‰, while those of sediment ranged from 10.41‰ to 16.80‰. In the downstream section, the δ¹⁸O_P values of the surface water and sediment were similar, suggesting that phosphate in the downstream water was primarily influenced by endogenous sediment control. The results of the land-use–source mixing model and Bayesian model framework demonstrated that controlling phosphorus inputs from fertilizers is essential for reducing phosphorus emissions in the XDJ watershed. Furthermore, ongoing rural sewage treatment, manure management, and the resource utilization of aquaculture substrates contributed to reduced phosphorus pollution. This study showed that isotope techniques, combined with multi-model approaches, effectively assessed phosphorus sources in complex watersheds, offering a theoretical basis for phosphorus pollution management to prevent eutrophication. Full article

Sanitary sewer pipelines frequently experience blockages, structural failures, and overflows, underscoring the dire state of U.S. wastewater infrastructure, which has been rated a D-, while America’s overall infrastructure scores only slightly better at C-. Traditional open-trench excavation methods or excavation technology (ET) for replacing deteriorated pipes are notoriously expensive and disruptive, requiring extensive processes like route planning, surveying, engineering, trench excavation, pipe installation, backfilling, and ground restoration. In contrast, trenchless technologies (TT) provide a less invasive and more cost-effective alternative. Among these, cured-in-place pipe technology (CIPPT), which involves inserting resin-impregnated fabric into damaged pipelines, is widely recognized for its efficiency. However, a comprehensive life cycle cost analysis (LCCA) directly comparing ET and TT, accounting for the net present value (NPV) across installation, maintenance, and rehabilitation costs, remains unexplored. This study aims to establish an LCCA framework for both CIPPT and ET, specifically for sanitary sewer pipes ranging from 8 to 42 inches in diameter. The framework incorporates construction, environmental, and social costs, providing a holistic evaluation. The key costs for ET involve pipe materials and subsurface investigations, whereas TT’s costs center around engineering and design. Social impacts, such as road and pavement damage, disruption to adjacent utilities, and noise, are pivotal, alongside environmental factors like material use, transportation, project duration, and equipment emissions. This comprehensive framework empowers decision makers to holistically assess economic and environmental impacts, enabling informed choices for sustainable sewer infrastructure renewal. Full article

Online recommendation agents have demonstrated their value in various contexts by helping users navigate information overload, supporting decision-making, and influencing user behavior. There is a lack of studies focusing on recommendation systems for green hotels that utilize user-generated content from social networking and e-commerce platforms. While numerous studies have explored the use of real-world datasets for hotel recommendations, the development of recommendation systems specifically for green hotels remains underexplored, particularly in the context of Saudi Arabia. This study attempts to develop a new approach for green hotel recommendations using text mining and Long Short-Term Memory techniques. Latent Dirichlet Allocation is used to identify the main aspects of users’ preferences from the user-generated content, which will help the recommender system to provide more accurate recommendations to the users. Long Short-Term Memory is used for preference prediction based on numerical ratings. To better perform recommendations, a clustering technique is used to overcome the scalability issue of the proposed recommender system, specifically when there is a large amount of data in the datasets. Specifically, a spectral clustering algorithm is used to cluster the users’ ratings on green hotels. To evaluate the proposed recommendation method, 4684 reviews were collected from Saudi Arabia’s green hotels on the TripAdvisor platform. The method was evaluated for its effectiveness in solving sparsity issues, recommendation accuracy, and scalability. It was found that Long Short-Term Memory better predicts the customers’ overall ratings on green hotels. The comparison results demonstrated that the proposed method provides the highest precision (Precision at Top @5 = 89.44, Precision at Top @7 = 88.21) and lowest prediction error (Mean Absolute Error = 0.84) in hotel recommendations. The author discusses the results and presents the research implications based on the findings of the proposed method. Full article

The turbulent energy dissipation rate (EDR) is a quantitative measure of turbulence intensity, and it is widely used across various fields. Accurate estimation of EDR using Doppler lidar depends on the choice of estimation technique and scanning strategy. Therefore, a comparison of the techniques is still required to achieve an accurate estimation. However, the effect of the choice on estimation accuracy remains uncertain. This study systematically evaluates the accuracy of EDR estimation techniques by utilizing two distinct scanning strategies: a vertically pointing scan (EDRVP) and a Plan Position Indicator scan (EDRVAD). We assess four different EDRVP estimation techniques and assess the accuracy of EDRVAD for each elevation angle by comparing it with the measurements from sonic anemometers on a 300 m tall meteorological tower. EDRVAD shows a positive correlation coefficient exceeding 0.5 with the sonic anemometers. EDRVAD demonstrates dependency on the elevation angle, with lower angles resulting in higher EDR values. Conversely, all of the EDRVP techniques exhibit high agreement, with correlation coefficients above 0.9. This study provides a comprehensive assessment of the accuracy of each technique, highlighting their respective characteristics and practical considerations. Full article

Accurate predictions of Initial Public Offerings (IPOs) aftermarket performance are essential for making informed investment decisions in the financial sector. This paper attempts to predict IPO short-term underperformance during a month post-listing. The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. The framework leverages data-driven methodologies to optimize decision-making in complex financial systems, integrating ANOVA F-value for feature selection, Randomized Search for hyperparameter optimization, and SMOTE for class balance. The framework’s effectiveness is assessed using a hand-collected dataset that includes features from both pre-IPO prospectus and firm-specific financial data. We thoroughly evaluate the results using single-split evaluation and 10-fold cross-validation analysis. For the single-split validation, ET achieves the highest accuracy of 86%, while for the 10-fold validation, BC achieves the highest accuracy of 70%. Additionally, we compare the results of the proposed framework with deep-learning models such as MLP, TabNet, and ANN to assess their effectiveness in handling IPO underperformance predictions. These results demonstrate the framework’s capability to enable robust data-driven decision-making processes in complex and dynamic financial environments, even with limited and imbalanced datasets. The framework also proposes a dynamic methodology named Investor Preference Prediction Framework (IPPF) to match tree-based ensemble models to investors’ risk preferences when predicting IPO underperformance. It concludes that different models may be suitable for various risk profiles. For the dataset at hand, ET and Ada are more appropriate for risk-averse investors, while BC is suitable for risk-tolerant investors. The results underscore the framework’s importance in improving IPO underperformance predictions, which can better inform investment strategies and decision-making processes. Full article

Drought stress is the most common threat to plant growth, while physiological integration can significantly enhance the drought tolerance of clonal plants, making it essential to research the behavior of clones under drought conditions and explore the potential applications of clonal plants. This study applied polyethylene-glycol-6000-induced stress to proximal, middle and distal clonal ramets of Kentucky bluegrass (Poa pratensis L.) and used an isotope labeling technique to evaluate the water physiological integration and photosynthetic capacity. When the proximal ramet was subjected to drought stress treatment, the decrease in ²H isotopes in the roots from 4 h to 6 h was significantly smaller than the increase in ²H isotopes in their own leaves. Additionally, the reductions in δ²H values of middle and distal ramets roots were 4.14 and 2.6 times greater, respectively, than the increases in their respective leaf δ²H values. The results indicate that under drought stress, water physiological integration was observed among different clonal ramets. In addition, drought stress inhibits the photosynthetic-related indicators in clonal ramets, with varying degrees of response and trends in photosynthetic characteristics among different clonal ramets. The proximal ramet treatment group, treated with polyethylene glycol 6000, was most affected by drought stress, while the distal ramet treatment group was least affected. The proximal ramet treatment group, treated with polyethylene glycol 6000, showed a decrease in water use efficiency after 6 h of drought treatment, while the other groups exhibited some increase. This indicates differences in water utilization and regulation among the different clonal ramets under drought stress. This study holds significant theoretical importance for exploring the characteristics of physiological integration and the photosynthetic mechanisms of Kentucky bluegrass clones under drought stress. Full article

SnO₂-based semiconductor gas-sensing materials are regarded as some of the most crucial sensing materials, owing to their extremely high electron mobility, high sensitivity, and excellent stability. To bridge the gap between laboratory-scale SnO₂ and its industrial applications, low-cost and high-efficiency requirements must be met. This implies the need for simple synthesis techniques, reduced energy consumption, and satisfactory gas-sensing performances. In this study, we utilized a surfactant-free simple method to modify SnO₂ nanoparticles with PdPt noble metals, ensuring the stable state of the material. Under the synergistic catalytic effect of Pd and Pt, the composite material (1.0 wt%-PdPt-SnO₂) significantly enhanced its response to HCHO. This modification decreased the optimal working temperature to as low as 180 °C to achieve a response value (R_a/R_g = 8.2) and showcased lower operating temperatures, higher sensitivity, and better selectivity to detect 10 ppm of HCHO when compared with pristine SnO₂ or single noble metal-decorated SnO₂ sensors. Stability tests verified that the gas sensor signals based on PdPt-SnO₂ nanoparticles exhibit good reliability. Furthermore, a portable HCHO detector was designed for practical applications, such as in newly purchased cushions, indicating its potential for industrialization beyond the laboratory. Full article

Grid complexity is expected to increase in the near future, and therefore, research on it is highly increasing due to the interest in optimizing power distribution along with the implementation of renewable energy sources. The grid presented in the current work uses a hybrid storage system with batteries and a generator set. A supervisor is also added to the model in order to distribute the load between the batteries and the generator when a power grid outage is detected. The main objective of this study is to find optimal supervisor operating values and battery capacity sizing. To that end, a recently developed intelligent algorithm, called Basque optimization (BO), is applied to model the battery capacity sizing and its depth of discharge. The results obtained provided an optimum value of 0.7267, which implies a battery sizing of 72.67% of the maximum battery capacity proposed in the optimization algorithm. Additionally, an optimal state of charge (SoC_lim) of the battery of 3.87% is obtained, corresponding to a depth of discharge (DoD_lim) of 96.13%. A sensitivity analysis is also performed to evaluate different time horizons and load profiles. The results showed that longer simulation horizons reduce the DoD, preserving battery life, while battery utilization increases in longer time horizons and variable load conditions, ensuring energy availability. Full article

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions. Full article

With varying tangential winds and combinations of stratiform and convective clouds, tropical cyclones (TCs) can be difficult to accurately portray when mosaicking data from ground-based radars. This study utilizes the Dual-frequency Precipitation Radar (DPR) from the Global Precipitation Measurement Mission (GPM) satellite to evaluate reflectivity obtained using four sampling methods of Weather Surveillance Radar 1988-Doppler data, including ground radars (GRs) in the GPM ground validation network and three mosaics, specifically the Multi-Radar/Multi-Sensor System plus two we created by retaining the maximum value in each grid cell (MAX) and using a distance-weighted function (DW). We analyzed Hurricane Laura (2020), with a strong gradient in tangential winds, and Tropical Storm Isaias (2020), where more stratiform precipitation was present. Differences between DPR and GR reflectivity were larger compared to previous studies that did not focus on TCs. Retaining the maximum value produced higher values than other sampling methods, and these values were closest to DPR. However, some MAX values were too high when DPR time offsets were greater than 120 s. The MAX method produces a more consistent match to DPR than the other mosaics when reflectivity is <35 dBZ. However, even MAX values are 3–4 dBZ lower than DPR in higher-reflectivity regions where gradients are stronger and features change quickly. The DW and MRMS mosaics produced values that were similar to one another but lower than DPR and MAX values. Full article

Background: Centranthera grandiflora Benth is commonly utilized in China to take advantage of its purported health benefits. Methods: Here, the chemical composition, nutritional value, and bioactivity of C. grandiflora Benth extract (CGE) are characterized, and the mechanisms through which it functions were explored. Results: CGE was found to exhibit a favorable nutritional and biosafety profile, especially due to its high amino acid and mineral contents. A UPLC-ESI-Q-TOF/MS approach identified 20 compounds. Through network pharmacology analyses, the antioxidant activity of CGE was found to be mediated through the PI3K/Akt pathway, with molecular docking results providing support for mussaenoside and azafrin as important bioactive compounds. At the cellular level, antioxidant activity of key protective antioxidants including GSH-Px and SOD while suppressing ROS accumulation, levels of damage-related factors (MDA, NO, TNF-α, IL-1β, and IL-6), and iNOS and COX-2 in RAW264.7 cells treated with LPS. These findings offer potential evidence for using CGE to lower oxidative stress and inflammation. Further analyses demonstrated the ability of CGE to promote Nrf2 and HO-1 upregulation, whereas Keap1 levels were suppressed, as were PI3K/Akt/NF-κB proteins. In light of these results, CGE appears to be able to act via simultaneously enhancing Nrf2/HO-1 activity and reducing that of PI3K/Akt/NF-κB. Conclusions: CGE, as a rich source of iridoid glycosides and other nutrients, may thus be a valuable dietary supplement for use in food applications. Full article

The deployment of large installed power capacities from intermittent renewable energy sources requires balancing to ensure the steady and safe operation of the electrical grid. New methods of energy storage are essential to store excess electrical power when energy is not needed and later use it during high-demand periods, both in the short and long term. Power-to-Gas (P2G) is an energy storage solution that uses electric power produced from renewables to generate gas fuels, such as hydrogen, which can be stored for later use. Hydrogen produced in this manner can be utilized in energy storage systems and in transportation as fuel for cars, trams, trains, or buses. Currently, most hydrogen is produced from fossil fuels. Solid-oxide electrolysis (SOE) offers a method to produce clean hydrogen without harmful emissions, being the most efficient of all electrolysis methods. The objective of this work is to determine the optimal operational parameters of an SOE system, such as lower heating value (LHV)-based efficiency and total input power, based on calculations from a mathematical model. The results are provided for three different operating temperature levels and four different steam utilization ratios. The introductory chapter outlines the motivation and background of this work. The second chapter explains the basics of electrolysis and describes its different types. The third chapter focuses on solid-oxide electrolysis and electrolyzer systems. The fourth chapter details the methodology, including the mathematical formulations and software used for simulations. The fifth chapter presents the results of the calculations with conclusions. The final chapter summarizes this work. Full article