Search Results (10,336)

Serum amyloid A (SAA) is one of the most important precursor amyloid proteins discovered during the study of amyloidosis, but its underlying aggregation mechanism has not yet been well elucidated. Since SAA aggregation is a key step in the pathogenesis of AA amyloidosis, amyloid inhibitors can be used as a tool to study its pathogenesis. Previously, we reported a novel microliter-scale high-throughput screening (MSHTS) system for screening amyloid β (Aβ) aggregation inhibitors based on quantum dot (QD) fluorescence imaging technology. In this study, we report the aggregation of human SAA (hSAA) in phosphate-buffered saline, in which we successfully visualized hSAA aggregation by QD using fluorescence microscopy and confocal microscopy. Two-dimensional and three-dimensional image analyses showed that most aggregations were observed at 40 μM hSAA, which was the optimal aggregation concentration in vitro. The accuracy of this finding was verified by a Thioflavin T assay. The transmission electron microscopy results showed that QD uniformly bound to hSAA aggregation. hSAA aggregation inhibitory activity was also evaluated by rosmarinic acid (RA). The results showed that RA, which is a compound with high inhibitory activity against Aβ aggregation, also exhibited high inhibitory activity against 40 μM hSAA. These results indicate that the MSHTS system is an effective tool for visualizing hSAA aggregation and for screening highly active inhibitors. Full article

The automatic video recognition of depression is becoming increasingly important in clinical applications. However, traditional depression recognition models still face challenges in practical applications, such as high computational costs, the poor application effectiveness of facial movement features, and spatial feature degradation due to model stitching. To overcome these challenges, this work proposes a lightweight Time-Context Enhanced Depression Detection Network (TCEDN). We first use attention-weighted blocks to aggregate and enhance video frame-level features, easing the model’s computational workload. Next, by integrating the temporal and spatial changes of video raw features and facial movement features in a self-learning weight manner, we enhance the precision of depression detection. Finally, a fusion network of 3-Dimensional Convolutional Neural Network (3D-CNN) and Convolutional Long Short-Term Memory Network (ConvLSTM) is constructed to minimize spatial feature loss by avoiding feature flattening and to achieve depression score prediction. Tests on the AVEC2013 and AVEC2014 datasets reveal that our approach yields results on par with state-of-the-art techniques for detecting depression using video analysis. Additionally, our method has significantly lower computational complexity than mainstream methods. Full article

Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model’s complexity and improve the model’s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model’s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network’s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment. Full article

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant. Full article

Sustainable agricultural practices are needed to find a solution to the problem of soil erosion and decreased soil quality. A study was conducted during the 2021/2022 and 2022/2023 cropping seasons to evaluate the synergistic effect of the tillage system (TS) and manure rates (MR) on selected soil properties at the University of Limpopo Experimental Farm (Syferkuil) and University of Venda Experimental Farm (UNIVEN). The experiment had a split plot design with three replications. The main plots used conventional (CON) and in-field rainwater harvesting (IRWH) tillage systems, while subplots used poultry and cattle manure at rates of 0, 20, and 35 t ha⁻¹. Bulk density (BD), aggregate stability (AS), pH, total N, organic carbon (OC), available P, and exchangeable cations (Ca, Mg, and K) were determined. IRWH significantly increased AS in the 0–20 cm soil layer at Syferkuil. TS × MR interaction significantly influenced AS and total N in the 20–40 cm soil layer during the 2022/2023 season at Syferkuil. IRWH significantly increased Mg content in the 2021/2022 season and total N, OC, and Mg content in the 2022/2023 season at Syferkuil over CON. At UNIVEN, CON significantly increased total N, whereas IRWH increased available P in the 2022/2023 season. MR significantly increased AS, exchangeable Ca, Mg, and K at both sites. At Syferkuil, MR significantly increased total N, OC, and available P during both seasons, whereas at UNIVEN the significant increase was observed on OC and available P during both seasons and total N in the 2021/2022 season. It was found that IRWH and poultry manure (35 t ha⁻¹) improved most soil properties at both sites; however, this study recommends long-term experiments to investigate the combined effect of IRWH and manure rate on soil properties to validate the findings observed in this study. Full article

The fatigue performance of the asphalt mixture was the main focus of this study, with five typical factors—phase angle, cumulative dissipated energy, failure strain, failure stiffness modulus, and strain rate—identified as potential design indexes. The effect of asphalt content on the parameters under different gradation and stress ratios was tested. It was observed that the selected parameters exhibited varying levels of sensitivity and relevance to the fatigue behavior of asphalt mixtures under cyclic loads. By comparison, the strain rate proved sensitive to the asphalt content and independent of the other parameters, namely aggregate gradations and stress ratio, thus establishing the strain rate as a critical design index based on fatigue performance. On this basis, a design method based on the fatigue performance for the asphalt mixtures is herein proposed. It was confirmed that the asphalt mixture formulated using the proposed method exhibited enhanced fatigue endurance compared to those designed using the conventional method. Full article

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA’s EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress–damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure. Full article

To clarify the effects of weathered coal humic acid on water-stable aggregates and pore characteristics of reclaimed cambisol, this research analyzed the evolution characteristics of soil aggregates and pores. Effects of different humic acid dosages (0, 1%, 3%, and 5% by weight) and application period (1 year, 2 years, and 3 years) on soil aggregates and organic carbon components in soil water-stable aggregates were investigated. The results showed that it is advisable to have an addition of 5% weathered coal humic acid in reclaimed cambisol. The humic acid of weathered coal promoted the disintegration and transformation of water-stable aggregates and increased soil pore p > 75 μm. At 3 years, the structure of reclaimed soil was the most stable, with more robust connected pores, and the irregular pores increased. The humic acid of weathered coal has the potential to be used as an effective organic amendment for improving the quality of reclaimed cambisol. Full article

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training across multiple devices while preserving data privacy. However, the decentralized nature of FL introduces significant security challenges, making it vulnerable to various attacks targeting models, data, and privacy. This survey provides a comprehensive overview of the defense strategies against these attacks, categorizing them into data and model defenses and privacy attacks. We explore pre-aggregation, in-aggregation, and post-aggregation defenses, highlighting their methodologies and effectiveness. Additionally, the survey delves into advanced techniques such as homomorphic encryption and differential privacy to safeguard sensitive information. The integration of blockchain technology for enhancing security in FL environments is also discussed, along with incentive mechanisms to promote active participation among clients. Through this detailed examination, the survey aims to inform and guide future research in developing robust defense frameworks for FL systems. Full article

Seomijn River Basin has numerous hydraulic structures designed to satisfy water demands and mitigate future droughts. However, the increasing water demand and export to neighboring areas cause water deficits and conflicts between water users. Therefore, practical strategies to mitigate the potential damage from climate change are essential. In this study, we aimed to propose practical strategies under climate change by examining the future water security of the Seomjin River Basin under five different water allocation scenarios referenced from the practical policies of various countries. Future climate models determined based on extreme precipitation indices of the ETCCDI were used to investigate their impact on water security, which was evaluated using unmet demand; demand coverage; reliability, resilience, and vulnerability; and aggregation index metrics. We found that prioritizing domestic and industrial water use is the optimal water security strategy, and unconditional allocation of instream flow can cause a significant water deficit for other water uses. However, prioritizing all water uses equally also proved effective under some conditions. Thus, our study highlights the importance of adaptive management and suggests that the optimal water allocation strategy lies in its flexibility in response to varying circumstances. Full article

As water scarcity intensifies due to climate change, improving the efficiency of agricultural water use has become increasingly critical. Current irrigation systems often experience significant water losses, especially in paddy fields in South Korea that largely rely on open-channel water supply networks. However, previous studies have simulated irrigation improvement strategies by aggregating multiple paddy fields into larger unites rather than modeling them individually, which limits the accurate representation of field conditions. To address this limitation, we applied a granular simulation approach, collecting detailed input data through field surveys. Using this granular model, the study evaluated strategies to enhance irrigation efficiency in paddy fields serviced by the Baekma Agricultural Reservoir in South Korea. We assessed four scenarios: the current open-channel system, conversion of open channels to closed conduits, installation of farm ponds, and a combination of closed conduits and farm ponds. These scenarios were simulated using the Strom Water Management Model (SWMM), meticulously configured to represent individual paddy fields and channel networks. The results show that converting open channels to closed conduits increased irrigation efficiency by 5.4% compared to the current open-channel system. Combining closed conduits with farm ponds achieved the highest efficiency, although the independent effect of farm ponds was minimal. These findings suggested that converting open-channels to closed conduits was a highly effective solution for reducing water losses, while farm ponds played a limited role. This study provides valuable insights into the development of precise irrigation strategies, offering a detailed assessment of real-world conditions in paddy fields. Full article

This article studies the practical road performance of recycled materials from construction waste, relying on the paving test section of the supporting project for the Qingdao Cross-Sea Bridge. The research focuses on the construction technology and road performance of using recycled construction waste materials in urban road sub-base construction. Through indoor tests such as sieving and unconfined compressive strength tests, relevant technical indicators were obtained and analyzed. Additionally, periodic core sampling, compaction tests, and rebound deflection tests were conducted on-site according to relevant standards to thoroughly investigate the specific effects of using construction waste in practice and to analyze and evaluate the actual feasibility of the materials for road use. The results indicate that the particle gradation of the construction mix in the test section aligns well with the target gradation, and the dosage of the mixing agent meets the design requirements. The 7-day unconfined compressive strength already satisfied the technical requirements for heavy and extremely heavy traffic on highways as specified in the “Technical Specifications for Construction of Highway Pavement Subbase” (JTG/T F20-2015), with the 14-day strength generally reaching 7 MPa. Core sampling revealed good aggregate gradation, smooth and straight profiles, and the thickness and strength of all parts meet the specifications. The compaction levels met the testing requirements, the surface deflection values showed a decreasing trend, and the deformation resistance was good, consistent with the general development patterns of semi-rigid sub-bases. Full article

Coarse-grained molecular dynamics (CGMD) simulations were employed to investigate the effects of phospholipids on the aggregation of hydrophilic, modified carbon-nanoparticle fillers in cis-polyisoprene (cis-PI) composites. The MARTINI force field was applied to model dipalmitoylphosphatidylcholine (DPPC) lipids and hydrophilic modified fullerenes (HMFs). The simulations of DPPC in cis-PI composites show that the DPPC lipids self-assemble to form a reverse micelle in a rubber matrix. Moreover, HMF molecules readily aggregate into a cluster, in agreement with the previous studies. Interestingly, the mixture of the DPPC and HMF in the rubber matrix shows a cluster of HMF is encapsulated inside the DPPC reverse micelle. The HMF encapsulated micelles disperse well in the rubber matrix, and their sizes are dependent on the lipid concentration. Mechanical and thermal properties of the composites were analyzed by calculating the diffusion coefficients (D), bulk modulus (κ), and glass transition temperatures (T_g). The results suggest that DPPC acts as a plasticizer and enhances the flexibility of the HMF-DPPC rubber composites. These findings provide valuable insights into the design and process of high-performance rubber composites, offering improved mechanical and thermal properties for various applications. Full article

As the world’s largest producer of construction waste, China’s recycling and related policies are of the biggest concern to the world. However, the effective disposal and reuse of this waste has become an important issue since currently China still has a very low recycling ratio compared to developed countries, and most of the waste concrete was only simply broken and used as low-grade recycled aggregates for subgrade cushion, cement stabilized crushed stone, and filler wall. In this paper, a concrete cycle model focusing on how to effectively recycle and utilize waste concrete is put forward to prepare high quality recycled concrete, especially through a series of technical means, such as effective separation, carbon sequestration, and reactivation. Producing high quality recycled concrete can not only replace traditional concrete but also effectively reduce the consumption and waste of raw materials. What’s more, the calculation results show a potential of significantly carbon sink; for every ton of recycled cement produced, the CO₂ emission could be reduced by 0.35–0.77 tons compared to ordinary Portland cement, corresponding to a reduction of 47%–94%; and for every ton of recycled concrete produced, the CO₂ emission could be reduced by 0.186 tons compared to normal concrete. A yearly CO₂ sequestration of 1.4–3.08 gigatonnes could happen if the ordinary Portland cement could be replaced by the recycled cement around the world. Taking the currently accumulated construction and demolition (C&D) wastes globally, the production of recycled cement, recycled aggregates, and recycled concrete could induce a significant carbon sink in the world. Full article

Volatile organic compounds (VOCs), particularly aromatic hydrocarbons, pose significant environmental risks due to their toxicity and role in the formation of secondary pollutants. This study explores the potential of catalytic pyrolysis as an innovative strategy for the effective remediation and conversion of aromatic hydrocarbon pollutants. The research investigates the high-efficiency removal and resource recovery of the VOC toluene using a Ni/Al₂O₃ catalyst. The Ni/Al₂O₃ catalyst was synthesized using the impregnation method and thoroughly characterized. Various analytical techniques, including scanning electron microscopy, X-ray diffraction, and N₂ adsorption–desorption isotherms, were employed to characterize the Al₂O₃ support, NiO/Al₂O₃ precursor, Ni/Al₂O₃ catalyst, and the resulting solid carbon. Results indicate that Ni predominantly occupies the pores of γ-Al₂O₃, forming nano/microparticles and creating interstitial pores through aggregation. The catalyst demonstrated high activity in the thermochemical decomposition of toluene into solid carbon materials and CO_x-Free hydrogen, effectively addressing toluene pollution while recovering valuable resources. Optimal conditions were identified, revealing that a moderate temperature of 700 °C is most favorable for the catalytic process. Under optimized conditions, the Ni/Al₂O₃ catalyst removed 1328 mg/g of toluene, generated 915 mg/g of carbon material, and produced 1234 mL/g of hydrogen. The prepared carbon material, characterized by its mesoporous structure and high specific surface area graphite nanofibers, holds potential application value in adsorption, catalysis, and energy storage. This study offers a promising approach for the purification and resource recovery of aromatic volatile organic compounds, contributing to the goals of a circular economy and green chemistry. Full article