Search Results (1,491)

The biological effects of nanoparticles are closely related to their intracellular content and location, both of which are influenced by various factors. This study investigates the effects of surface charge on the uptake, intracellular distribution, and exocytosis of CdSe/ZnS quantum dots (QDs) in Raw264.7 macrophages. Negatively charged 3-mercaptopropanoic acid functionalized QDs (QDs-MPA) show higher cellular uptake than positively charged 2-mercaptoethylamine functionalized QDs (QDs-MEA), and serum enhances the uptake of both types of QDs via protein corona-mediated receptor endocytosis. QDs-MEA primarily enter the cells through clathrin/caveolae-mediated pathways and predominantly accumulate in lysosomes, while QDs-MPA are mainly internalized through clathrin-mediated endocytosis and localize to both lysosomes and mitochondria. Exocytosis of QDs-MPA is faster and more efficient than that of QDs-MEA, though both exhibit limited excretion. In addition to endocytosis and exocytosis, cell division influences intracellular QD content over time. These results reveal the charge-dependent interactions between QDs and macrophages, providing a basis for designing biocompatible nanomaterials. Full article

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learning methods have been increasingly applied to Hs prediction, but challenges remain in hyperparameter tuning and spatial generalization. This study explores a novel effective approach for intelligent Hs forecasting for marine operations. Multiple automated machine learning (AutoML) frameworks, namely H2O, PyCaret, AutoGluon, and TPOT, have been systematically evaluated on buoy-based Hs prediction tasks, which reveal their advantages and limitations under various forecast horizons and data quality scenarios. The results indicate that PyCaret achieves superior accuracy in short-term forecasts, while AutoGluon demonstrates better robustness in medium-term and long-term predictions. To address the limitations of single-point prediction models, which often exhibit high dependence on localized data and limited spatial generalization, a multi-point data fusion framework incorporating Principal Component Analysis (PCA) is proposed. The framework utilizes Hs data from two stations near the California coast to predict Hs at another adjacent station. The results indicate that it is possible to realize cross-station predictions based on the data from adjacent (high relevance) stations. Full article

In this paper, the single-mode vortex beam is used to illuminate targets of different shapes, and the targets are recognized using machine learning algorithms based on the orbital angular momentum (OAM) spectral information of the echo signal. We innovatively utilize three neural networks—multilayer perceptron (MLP), convolutional neural network (CNN) and residual neural network (ResNet)—to train extensive echo OAM spectrum data. The trained models can rapidly and accurately classify the OAM spectrum data of different targets’ echo signals. The results show that the residual network (ResNet) performs best under all turbulence intensities and can achieve a high recognition rate when $C_n^2=1\times {10}^{-13} {\mathrm{m}}^{-2/3}$. In addition, even when the target size is $\eta =0.3$, the recognition rate of ResNet can reach 97%, while the robustness of MLP and CNN to the target size is lower; the recognition rates are 91.75% and 91%, respectively. However, although the recognition performance of CNN and MLP is slightly lower than that of ResNet, their training time is much lower than that of ResNet, which can achieve a good balance between recognition performance and training time cost. This research has a promising future in the fields of target recognition and intelligent navigation based on multi-dimensional information. Full article

This study investigated the effects of Lactococcus lactis subsp. 1.2472 (L)-, Streptococcus thermophilus 1.2718 (S)-, and thermostable Lactobacillus rhamnosus HCUL 1.1901-1912 (T)-fermented rice flour with inoculum levels of 3–11% (w/w) on rice bread staling. Optimal staling resistance was achieved, as follows: 9% L-fermented rice bread (LRB), 7% T-fermented rice bread (TRB), and 5% S-fermented rice bread (SRB). Lactic acid bacteria-fermented rice flour significantly enhanced hydration properties. LF-NMR analysis revealed that T₂₁ (strongly bound water) and T₂₂ (weakly bound water) relaxation times decreased, while T₂₃ (free water) increased with prolonged storage. Fermented-rice-flour groups had significantly more strongly bound water than the control group on 7 d. The optimized formulations exhibited exceptional volumetric stability with specific volume change rates of 17.63% (LRB), 17.60% (TRB), and 19.58% (SRB), coupled with maximal porosities of 10.34%, 9.05%, and 9.41%, respectively. This study provides a theoretical foundation for improving rice bread’s anti-staling properties. Full article

Fly-ash-based bubble lightweight soil is widely used due to its environmental friendliness, load reduction, ease of construction, and low costs. In this study, 41 sets of 28 d compressive strength data on lightweight soils with different water–cement ratios, blowing agent dosages, and fly ash dosages were collected through a literature search and indoor tests. Using the compressive strength index and SEM tests, the correlation between the mix ratio design and the microscopic particle components was investigated. The findings were as follows: carbonation reactions occurred in lightweight soil during the maintenance process, and the particles were spherical; increasing the dosage of blowing agent increased the soil’s porosity and pore diameter, leading to the formation of through-holes and reducing the compressive strength and mobility; increasing the fly ash dosage and water–cement ratio increased the soil’s mobility but reduced its compressive strength; and the strength decreased significantly when the fly ash dosage was more than 16% (e.g., the strength at a 20% dosage was 17.8% lower than that at a 15% dosage). Feature importance analysis showed that the water–cement ratio (57.7%), fly ash dosage (30.9%), and blowing agent dosage (11.1%) had a significant effect on strength. ExtraTrees, LightGBM, and Bayesian-optimized Random Forest models were used for 28d strength prediction with coefficients of determination (R²) of 0.695, 0.731, and 0.794, respectively. The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. The accuracy of the model is expected to be further improved with expansions in the database. A 28 d compressive strength prediction platform for fly-ash-based bubble lightweight soil was ultimately developed, providing a convenient tool for researchers and engineers to predict material properties and mix ratios. Full article

Phosphogypsum, a by-product of phosphate fertilizer production, was predominantly used as a supplementary additive in recycled construction materials. However, there are few detailed studies on utilizing phosphogypsum as the primary component in inorganic cementing materials while achieving cost-effective detoxification. This study aimed to develop a harmless phosphogypsum-based inorganic cementing material (PICM) mainly based on phosphogypsum, in which cement, quicklime, and a stabilizer were used as additives. Harmful ions and acidity were first detected through X-ray fluorescence and ion chromatography and then harmlessly treated with quicklime. Compaction parameters, mechanical performance, X-ray diffraction analysis, moisture, and freezing resistance were characterized successively. The results illustrated that fluoride and phosphate ions were the primary soluble contaminants, whose leaching solution concentration can be reduced to 15.31 mg/L and undetectable with 2% quicklime through the mass proportion of phosphogypsum added and mixed. Meanwhile, the corresponding pH value was also raised to over 8. Cement content and quicklime were positively correlated with PICM’s maximum dry density. PICM with 25% cement and 2.5% stabilizer presented the highest unconfined compression strength, and flexural strength did not show significant regularity. PICM was mainly composed of quartz, gypsum, ettringite, and calcite, whose content decreased as cement content and quicklime content increased. Stabilizer, quicklime and cement content were positively correlated with PICM’s freezing and moisture resistance. Full article

Background/Objectives: Within the range of spread-out Bragg peak (SOBP), LET (linear energy transfer) gradually increases from proton beam entrance point toward the beam exit direction. While it is expected that the change in LET would lead to correspondent change in RBE (relative biological effectiveness) on many human cell lines, the incomplete cell killing due to low LET can result in tumor recurrence. Hence, this study aimed to assess the RBE on different cancer cell lines along low-LET proton SOBP. Methods: The clonogenicity of A549 and Panc-1 cells after irradiation was evaluated for investigating cell radiosensitivity in response to different types of radiation. The isoeffect doses of 6-MV photon and low-LET proton beams that resulted in equivalent cell surviving fractions at proton dose of 2 or 4 Gy were compared. Results: Ratios of α/β of A549 and Panc-1 cells from photon irradiation are 51.69 and −0.7747, respectively; RBE (2 Gy proton SOBP) on A549 and Panc-1 cells are 0.7403 ± 0.3324 and 1.0986 ± 0.3984, respectively. In addition, the change in RBE with proton LET was in a cell-specific and dose-dependent manner (LET-RBE linear correlations: A549 cells [r = 0.4673, p = 0.2430] vs. Panc-1 cells at 4 Gy [r = 0.7085, p = 0.0492]; Panc-1 cells at 2 Gy [r = −0.4123, p = 0.3100] vs. 4 Gy [r = 0.7085, p = 0.0492]). Conclusions: Compared with A549 cells, Panc-1 cells present greater resistance to low-LET proton beams. In addition, currently employed generic RBE value at 1.1 for proton therapy neglected the variation in cell-/tumor-specific radiobiological responses toward different dose levels of proton beams. Full article

To reveal the meso-mechanical essence of deep rock mass failure and capture precursor information, this study focuses on soft rock failure mechanisms. Based on the discontinuous medium discrete element method (DEM), we employed digital image correlation (DIC) technology, acoustic emission (AE) monitoring, and particle flow code (PFC) numerical simulation to investigate the failure evolution characteristics and AE quantitative representation of soft rocks. Key findings include the following: Localized high-strain zones emerge on specimen surfaces before macroscopic crack visualization, with crack tip positions guiding both high-strain zones and crack propagation directions. Strong force chain evolution exhibits high consistency with the macroscopic stress response—as stress increases and damage progresses, force chains concentrate near macroscopic fracture surfaces, aligning with crack propagation directions, while numerous short force chains coalesce into longer chains. The spatial and temporal distribution characteristics of acoustic emissions were explored, and the damage types were quantitatively characterized, with ring-down counts demonstrating four distinct stages: sporadic, gradual increase, stepwise growth, and surge. Shear failures predominantly occurred along macroscopic fracture surfaces. At the same time, there is a phenomenon of acoustic emission silence in front of the stress peak in the surrounding rock of deep soft rock roadway, as a potential precursor indicator for engineering disaster early warning. These findings provide critical theoretical support for deep engineering disaster prediction. Full article

Fault diagnosis technology is a crucial technique for ensuring the reliability of multiprocessor systems. Many previous studies have paid close attention to the permanent faults of systems while ignoring the rise of intermittent faults. Meanwhile, there is a lack of a rapid diagnostic algorithm tailored for intermittent faults. In this paper, we propose multiple theorems to evaluate the intermittent fault diagnosability of different topologies under the $PMC$ model. Through these theorems, we demonstrate that the intermittent fault diagnosability of an n-dimensional augmented cube ($A{Q}_n$) is $(2n-2)$ when n is greater than or equal to 4. Furthermore, we present a fast intermittent fault diagnosis algorithm, which is named as $IFDA$, to identify the processors with intermittent fault in the networks. Finally, we evaluate the performance of the algorithm in terms of the parameters Accuracy and Precision. The simulation experimental results show that the algorithm $IFDA$ has good performance and efficiency. Full article

A new species of Aprostocetus (Hymenoptera: Eulophidae), Aprostocetus bipolaris sp. nov., is recognized to be fortuitously present on a population of the invasive Eucalyptus (E. grandis × E. urophylla) gall wasp Ophelimus bipolaris Chen & Yao, in Guangxi, China. To classify this species, an integrated approach of morphological characteristics and molecular data was applied. The morphology of the new species is described and illustrated, and an identification key for female and male adults is also presented. Regarding phylogenetic analyses, the position of A. bipolaris sp. nov. within the Aprostocetus group of genera was reaffirmed based on 28S and COI gene sequences. All these lines of evidence indicate that A. bipolaris sp. nov. is a new species. Full article

Thermal sensors are widely used in medical, industrial and other fields, where the requirements for high sensitivity and portability continues to increase. Here we propose a suspended bridge structure fabricated using MEMS, which effectively shrinks the size and reduces heat loss. This study reviews current sensor-related theories of heat conduction, convective heat transfer and thermal radiation. Heat loss models for suspended and non-suspended bridge structures are established, and finite element analysis is conducted to evaluate their thermal performance. The thermal performance of the suspended bridge structure is further validated through infrared temperature measurements on the manufactured sensor device. Theoretical calculations demonstrate that the proposed suspension bridge structure reduces heat loss by 88.64% compared with traditional designs. Benefiting from this improved heat retention, which was also confirmed by infrared thermography, the thermal sensor fabricated based on the suspension bridge structure achieves an ultra-high sensitivity of 0.38 V/W and a fast response time of less than 200 ms, indicating a high accuracy in thermal characterization. The correlation coefficient obtained for the sensor output voltage and input power of the sensor is approximately 1.0. Based on this design, multiple microfluidic channels with suspended bridge structures can be integrated to realize multi-component detection, which is important for the development of multifunctional biomedical detection. Full article

7,12-Dimethylbenzo[a]anthracene (DMBA-7,12), a highly toxic and environmentally persistent polycyclic aromatic hydrocarbon (PAH), poses significant threats to marine biodiversity and human health due to its bioaccumulation through the food chain. Conventional chromatographic methods, while achieving comparable detection limits, are hindered by the need for expensive instrumentation and prolonged analysis times, rendering them unsuitable for rapid on-site monitoring of DMBA-7,12 in marine environments. Therefore, the development of novel, efficient detection techniques is imperative. In this study, we have successfully developed an electrochemical immunosensor based on a polydopamine (PDA)–chitosan (CTs) composite interface to overcome existing technical limitations. PDA provides a robust scaffold for antibody immobilization due to its strong adhesive properties, while CTs enhances signal amplification and biocompatibility. The synergistic integration of these materials combines the high efficiency of electrochemical detection with the specificity of antigen–antibody recognition, enabling precise qualitative and quantitative analysis of the target analyte through monitoring changes in the electrochemical properties at the electrode surface. By systematically optimizing key experimental parameters, including buffer pH, probe concentration, and antibody loading, we have constructed the first electrochemical immunosensor for detecting DMBA-7,12 in seawater. The sensor achieved a detection limit as low as 0.42 ng/mL. In spiked seawater samples, the recovery rates ranged from 95.53% to 99.44%, with relative standard deviations (RSDs) ≤ 4.6%, demonstrating excellent accuracy and reliability. This innovative approach offers a cost-effective and efficient solution for the in situ rapid monitoring of trace carcinogens in marine environments, potentially advancing the field of marine pollutant detection technologies. Full article

To achieve regional sustainable development, the low-carbon transformation of agriculture is essential, as it serves both as a significant carbon source and as a potential carbon sink. This study calculated the agricultural carbon emissions in Liaocheng from 2010 to 2022 by analyzing processes including crop cultivation, animal husbandry, and agricultural input. Additionally, a simulation model of the water–energy–food–carbon nexus (WEFC-Nexus) for Liaocheng’s agricultural production process was developed. Using Vensim PLE 10.0.0 software, this study constructed a WEFC-Nexus model encompassing four major subsystems: economic development, agricultural production, agricultural inputs, and water use. The model explored four policy scenarios: business-as-usual scenario (S1), ideal agricultural development (S2), strengthening agricultural investment (S3), and reducing agricultural input costs (S4). It also forecast the trends in carbon emissions and primary sector GDP under these different scenarios from 2023 to 2030. The conclusions were as follows: (1) Total agricultural carbon emissions exhibited a three-phase trajectory, namely, “rapid growth (2010–2014)–sharp decline (2015–2020)–gradual rebound (2021–2022)”, with sectoral contributions ranked as livestock farming (50%) > agricultural inputs (27%) > crop cultivation (23%). (2) The carbon emissions per unit of primary sector GDP (CEAG) for S2, S3, and S4 decreased by 8.86%, 5.79%, and 7.72%, respectively, compared to S1. The relationship between the carbon emissions under the four scenarios is S3 > S1 > S2 > S4. The relationship between the four scenarios in the primary sector GDP is S3 > S2 > S4 > S1. S2 can both control carbon emissions and achieve growth in primary industry output. Policy recommendations emphasize reducing chemical fertilizer use, optimizing livestock management, enhancing agricultural technology efficiency, and adjusting agricultural structures to balance economic development with environmental sustainability. Full article

The mushroom Stropharia rugoso-annulata is one of the most popular varieties in the international market because it is highly nutritious and has a delicious flavor. However, grading is still performed manually, leading to inconsistent grading standards and low efficiency. In this study, deep learning and computer vision techniques were used to develop an automated air-blown grading system for classifying this mushroom into three quality grades. The system consisted of a classification module and a grading module. In the classification module, the cap and stalk regions were extracted using the YOLOv8-seg algorithm, then post-processed using OpenCV based on quantitative grading indexes, forming the proposed SegGrade algorithm. In the grading module, an air-blown grading system with an automatic feeding unit was developed in combination with the SegGrade algorithm. The experimental results show that for 150 randomly selected mushrooms, the trained YOLOv8-seg algorithm achieved an accuracy of 99.5% in segmenting the cap and stalk regions, while the SegGrade algorithm achieved an accuracy of 94.67%. Furthermore, the system ultimately achieved an average grading accuracy of 80.66% and maintained the integrity of the mushrooms. This system can be further expanded according to production needs, improving sorting efficiency and meeting market demands. Full article