Search Results (21,187)

This study aims to establish an origin identification method for Scutellariae radix that integrates multidimensional quality indicators and machine learning algorithms, enabling accurate and rapid traceability of Scutellariae radix medicinal materials from four production areas: Hebei (HB), Shanxi (SX), Shaanxi (SAX), and Chengde (CD). The study collected a total of 43 batches of Scutellariae radix samples from the aforementioned origins. It systematically measured 12 key quality indicators covering flavonoids, physicochemical parameters, chromaticity values, and biological activity. These specifically include four flavonoid components: baicalin, wogonoside, baicalein, and wogonin; three physicochemical parameters: moisture content, ash content, and alcohol-soluble extract; four chromaticity values: L*, a*, b*, and ΔE; and in vitro anti-inflammatory activity (IC₅₀ value for NO clearance). On the basis of these parameters, in this study there were five machine learning models constructed based on the following algorithms and methods: Random Forest (RF), Extreme Learning Machine (ELM), Backpropagation Neural Network (BP), and Radial Basis Function Neural Network (RBF). A comparative analysis was conducted to evaluate the origin identification performance of each model. The results indicate significant differences (p < 0.05) in the contents of baicalin, wogonoside, L*, a*, b*, ΔE, and alcohol-soluble extract among Scutellariae radix from different origins. The comparative analysis of four machine learning models reveals that RF outperforms ELM, BP, and RBF in multiclass classification, achieving a test accuracy of 75% and consistent precision, recall, and F1-score of 79.17%. In contrast, the three neural networks attain only 66.67% test accuracy, with RBF showing high precision but low recall, ELM delivering moderate performance, and BP performing poorly. These results underscore the strength of ensemble methods like RF in small-sample settings, where they mitigate overfitting and enhance generalization, whereas neural networks struggle with limited data. We therefore recommend RF for deployment under current data constraints and suggest future work should focus on data expansion, especially for under-performing classes, along with hyperparameter tuning to further improve classification. Full article

The Yellow River Basin is among the regions in China most severely affected by soil erosion. Elucidating the evolution trend of its ecological environment quality and identifying the key driving factors can provide a theoretical basis for the management and protection of the ecological environment in the Yellow River Basin. In this study, an improved remote sensing ecological index (ARSEI) was constructed by incorporating the soil erosion factor (A) into the original remote sensing ecological index (RSEI). Subsequently, the Theil–Sen slope estimator, Mann–Kendall trend test, coefficient of variation, Hurst index and Geodetector were employed to analyze the spatiotemporal evolution trend and driving factors of the ecological environment quality in the basin from 2002 to 2022. The results were as follows: (1) During the study period, the mean ARSEI of the basin increased from 0.518 to 0.568, representing an increase of 9.65%, with a spatial pattern of “poor in the north and excellent in the south.” (2) 62.12% of the basin exhibited improved ecological quality, 75.74% of the area showed medium or lower fluctuation levels, and 35.12% of the region is projected to be at risk of degradation in the future. (3) Annual precipitation was identified as the dominant factor influencing the spatial variation in ARSEI (q = 0.428), followed by land use type (q = 0.299). All interactions between factors exhibited either nonlinear enhancement or bi-factor enhancement. Specifically, the interaction between annual precipitation and land use type was the strongest, with a maximum q-value of 0.693. This study provides a novel approach for assessing the ecological environment quality in regions severely affected by soil erosion. Full article

Artificial Intelligence is advancing rapidly, raising critical concerns about the integrity of digital content, particularly in sensitive domains such as medical imaging. Recent AI techniques, such as Generative Adversarial Networks (GANs) and diffusion models, can generate highly realistic synthetic medical images, posing risks of misdiagnosis, inappropriate treatment, and other adverse outcomes. This paper presents a deep learning-based approach to distinguish between authentic and synthetic images of skin malignancies generated by DCGAN, Wasserstein GAN (WGAN), and Stable Diffusion. A comprehensive dataset was constructed using authentic malignant skin images from an open-source Kaggle repository, alongside artificially generated images. Multiple deep learning models were trained and evaluated, with DenseNet169 achieving the highest performance, reaching 99.67% training accuracy, 97.50% validation accuracy, and 98.50% test accuracy—along with substantial precision, recall, and F1 scores across all classes. These results demonstrate the model’s efficacy in identifying both real and fake medical images. This work contributes to the emerging field of medical image forensics, highlighting its potential integration into clinical and insurance workflows to prevent fraud, strengthen trust, and mitigate risks. Furthermore, it lays the groundwork for future studies involving larger datasets, additional Deepfake generation methods, and real-time clinical applications. Full article

To address the challenges of traditional flexible humidity sensors, such as reliance on external power supply, complex fabrication processes, and poor adaptability to energy-limited scenarios, this study successfully developed a low-cost, easily scalable, self-powered flexible humidity sensor based on hydroxypropyl trimethyl ammonium chitosan/lithium chloride (HACC/LiCl) composite electrolyte using a screen-printing process. The device employs A4 paper as the flexible substrate, and interdigitated manganese dioxide (MnO₂) positive electrodes, zinc (Zn) negative electrodes, and HACC/LiCl composite electrolyte layers are sequentially fabricated via screen-printing, ultimately constructing a simple primary battery structure. Through a series of performance screening and optimization, 0.1 mol/L LiCl-modified HACC (HL-1) is identified as the optimal electrolyte system. The test results show that the HL-1 sensor exhibits a wide humidity detection range of 11~97% relative humidity (RH), with the output voltage displaying a good quadratic function relationship with humidity (R² = 0.996), and a peak output voltage of up to 1.2 V. The device possesses excellent cyclic stability and long-term stability, with no significant fluctuation in output voltage under different bending deformation states. This sensor demonstrates broad application prospects in fields such as respiratory monitoring and non-contact sensing, providing a feasible technical path for the development of low-cost passive humidity monitoring equipment. Full article

Against the backdrop of global demographic transition and widening regional disparities, population shrinkage and population aging have become critical constraints on regional development, posing severe challenges to the socioeconomic resilience of shrinking areas. Taking China’s three northeastern provinces as the study area, this paper investigates the impacts of population aging on county-level socioeconomic resilience and its spatiotemporal heterogeneity. Based on population census and socioeconomic data from 143 counties in Northeast China during 2010–2020, an evaluation index system of socioeconomic resilience is constructed using the entropy weight method. Grey relational analysis, Tobit regression models, and geographically and temporally weighted regression (GTWR) are employed to conduct empirical tests. The results indicate that most counties simultaneously experience population decline and deep aging, and their interaction forms an intensified negative feedback mechanism that constrains the improvement of socioeconomic resilience. Compared with other shrinking regions in China—such as selected counties in the Yangtze River Delta and resource-rich counties in central and western China—Northeast China is distinguished by a unique set of compounded pressures, driven by the simultaneous and mutually reinforcing trends of sustained population decline and deep aging. Population aging exhibits a strong correlation with socioeconomic resilience across all dimensions, with the most pronounced association observed in transformation capacity. Population density also plays an important role, although its correlation strength is relatively weaker. Tobit regression results further confirm that population aging significantly suppresses socioeconomic resilience, whereas population density exerts a positive effect, with notable differences across various types of shrinking counties. GTWR analysis reveals significant spatial heterogeneity in the impacts of these factors on socioeconomic resilience. Overall, this study provides robust empirical evidence for formulating targeted policies and enhancing sustainable development capacity in shrinking and aging regions. Full article

To investigate the bearing characteristics of super-long and large-diameter cast-in-place piles with combined pile-end and pile-side post-grouting, double-load-box self-balanced static-load tests were conducted on two such piles of the Yellow River Bridge Project on Jiaoping Expressway both before and after grouting. This study aims to provide technical insights for the design and construction of similar pile foundations. The test results indicate that, after grouting, the ultimate bearing capacities of test piles SZ1 and SZ2 increased by 123.1% and 72.8%, respectively, with a significant reduction in pile top settlement under the same load level. Under each load level, the axial force of the pile shaft reaches its maximum near the upper load box, presenting a triangular distribution curve. Furthermore, the side frictions of SZ1 and SZ2 enhanced by 87.73% and 83.59%, respectively, after grouting, while their ultimate end resistances are improved by 362.6% and 120.6%. These findings demonstrate that post-grouting effectively optimizes the mechanical properties of the pile–soil interface and enhances the structural stiffness of the surrounding soil. Specifically, the grout hardens at the pile end, solidifies the sediment there, increases the density of the pile-end soil layer, and improves the bearing rigidity of the bearing stratum. This research validates the effectiveness of the combined pile-end and pile-side post-grouting technology in improving the bearing performance of super-long and large-diameter cast-in-place piles, providing valuable technical support for the safe and efficient construction of the Yellow River Bridge on the Jiaoping Expressway and similar engineering projects. Full article

Breaking the talent bottleneck in agriculture and forestry and establishing an effective channel for transmitting intellectual achievements from university graduates to rural areas are crucial for building a high-quality rural revitalization workforce. This study employs a mixed-methods approach, combining systematic surveys based on a five-point Likert scale (Cronbach’s α = 0.982) with machine learning modeling to analyze the factors influencing the employment of graduates from agricultural and forestry institutions. Key findings indicate that respondents generally recognize the importance of salary and benefits, express high satisfaction with occupational environments and living conditions, and acknowledge the effectiveness of training systems and promotion channels. The Genetic Algorithm-Back Propagation (GA-BP) predictive model constructed in this study demonstrates outstanding performance, achieving coefficients of determination (R²) of 0.983 and 0.960 on the training and test sets, significantly outperforming traditional measurement methods. This research not only provides data-driven support for optimizing employment policies in agricultural and forestry institutions but also showcases an innovative application of artificial intelligence in analyzing employment factors, offering an interdisciplinary research paradigm for talent strategies aimed at advancing smart agriculture. Full article

To meet the development requirements of the path planning unit for unmanned underwater vehicles (UUVs), research is conducted on UUV 3D obstacle avoidance path planning algorithms and software design. Firstly, aiming at the problem of underwater 3D obstacle avoidance path planning for UUVs, a global path planning algorithm based on the improved D* Lite is designed, and a local path planning algorithm combining the 3D obstacle avoidance strategy and the improved artificial potential field (APF) algorithm is designed. Secondly, based on the above path planning algorithms, a UUV 3D obstacle avoidance path planning software is developed under the Robot Operating System 2 (ROS2) framework and deployed on an Orange Pi 5B. To test the algorithms and the developed software, a UUV autonomous navigation hardware-in-the-loop (HIL) simulation system is constructed. Finally, based on this system, three types of HIL simulation experiments are conducted, including global path planning, local path planning, and comprehensive obstacle avoidance path planning. The simulation experiments show that the improved D* Lite-APF algorithm has better comprehensive performance than the traditional D* Lite-APF algorithm; the path planning software can guide the UUV to reach the goal point safely and runs stably and reliably. The designed UUV 3D obstacle avoidance path planning algorithm and software exhibit good obstacle avoidance performance and can be applied to the rapid development of actual UUV path planning units. Full article

Agile and flexible resource modelling is essential for informed decision-making in early-stage mineral project assessment, and in more advanced stages, particularly when compared with conventional deterministic geological modelling and single-estimate resource evaluations. This study presents a case of rapid scenario generation to view, interpret and test the impact of alternative geological and modelling assumptions, including the definition of geological domains, geological interpretation, grade estimation within domains, and the associated uncertainty. The workflows are implemented in Annapurna™ Resource, a cloud-native geostatistical platform designed to support agile, advanced, and multivariate modelling workflows. Focusing on the multi-commodity San Antonio–Potrerillos district, we demonstrate how rapid model construction enables the systematic evaluation of geological and statistical assumptions, contrasting deterministic estimates with probabilistic outcomes and testing their impact on estimated grades and tonnage under multiple scenarios for five elements: copper (Cu), molybdenum (Mo), gold (Au), silver (Ag), and arsenic (As). The approach provides quantitative measures of model reliability, identifies areas of high uncertainty, and supports the prioritization of new drilling to improve geological knowledge, exploration targeting, and resource classification. This case study highlights the value of fast-turnaround, probabilistic modelling not as a replacement for traditional resource reporting, but as a decision-support framework that enhances understanding of the geology, tests the sensitivity of assumptions, and accelerates learning throughout exploration and into operations. The main results suggest that additional drilling can be strategically placed to reduce the geological uncertainty derived from comparing the current interpretation with the probabilistic model built with indicator kriging. Furthermore, this has relevance in reducing the risk in the assessment of the metal content in each area of the deposit. Sensitivity analysis performed over key parameters of the estimation suggests that outliers’ treatment is the most impactful step during estimation. With current technological tools, it is possible to maintain a live resource model, which can be continuously updated to assess the impact of new data and decisions in near real time. Full article

This study addresses the common challenges of complex soil behavior and the difficulties in achieving precise control during the construction of large open caissons. A centrifugal model test was conducted to investigate open caisson–fine sandy soil interaction, and the findings were further verified through field testing. Results indicated that during the sinking process, the open caisson–soil interface exhibited slip failure characteristics, while the soil at the cutting edge of the open caisson showed a tendency for inward shear slippage. The horizontal earth pressure along the open caisson sidewall was found to correspond to static earth pressure in the upper section and gradually approached active earth pressure in the lower section. The maximum earth pressure occurred at approximately three-quarters of the embedded depth of the open caisson wall. Furthermore, the friction angle at the soil-open caisson interface was approximately 0.63 times that of the soil friction angle. Based on the observed distribution patterns of earth pressure and skin friction, theoretical calculation formulas were developed. Their accuracy was confirmed through field tests, providing valuable references for the design and construction of large open caisson projects. Full article

Expanded polystyrene (EPS) concrete, with excellent properties such as light weight, thermal insulation, and soundproofing, is widely applied in construction engineering. However, its complex heterogeneous internal structure makes it difficult to quickly and accurately assess compressive strength. Existing testing methods struggle to meet the real-time demands of on-site quality control in terms of both operational efficiency and accuracy. To address this, the present study proposes a method for predicting the compressive strength of EPS concrete based on image processing and Deep Convolutional Neural Networks (DCNN). By constructing a dataset consisting of 5600 preprocessed concrete slice images and addressing the issue of parameter redundancy in fully connected layers, the Broad Learning System (BLS) was employed to reconstruct and optimize the network architecture, thereby improving computational efficiency and enhancing prediction accuracy. The experimental results indicate that after introducing the BLS and related training optimization mechanisms, the training time was reduced by approximately 15%. Among all models, the BLS-Xception model performed the best, requiring only 1.9 s per training image. The coefficient of determination (R²) on the test set reached 0.95, representing an 18.7% improvement over traditional models. The study also indicates that the appropriate incorporation of coal ash, silica fume, and mineral powder significantly enhances the compressive strength of EPS concrete, with smaller EPS particles contributing more substantially to strength improvement. The model demonstrates excellent accuracy and reliability in predictions, providing an effective method for the rapid, non-destructive evaluation of the compressive strength of EPS concrete on construction sites. Full article

The paper aims to explore the relationship between emotions and reflexivity, with reference to the constructs of critical reflexivity and emotional granularity. These two constructs and their operationalization constitute the theoretical–methodological background of an empirical exploratory research study conducted on a sample of adult workers aged between 18 and 55, who were subjected to a diarist-style reflective writing course. The overall aim of the course was to ascertain whether, how and to what extent reflective practices of the narrative type can influence and modulate the stress response, both from the point of view of the participants’ assumption of awareness and from the point of view of the adoption of new behaviors. The central question that the present article proposes to discuss is related to the exploration of what the basic requirements/skills are on which the development of critical reflexivity is built over time, with particular attention to the role played by emotional competencies. This aspect represents one of the most relevant gaps in current research on critical reflexivity, which is severely limited by a general tendency towards the hyper-cognization of the models of analysis adopted in much of the research devoted to reflexivity, as well as by the little space given to the investigation of the emotional dynamics at play in its onset processes. The study carried out represents an initial exploration of this aspect, testing two main hypotheses: (a) the possibility of identifying and describing a preliminary threshold to the manifest development of critical reflexivity, prior to the development of process reflexivity; (b) the possibility that crossing this threshold may be facilitated by the acquisition of a good level of emotional competence, measurable through the emotional granularity construct. In the light of the quali-quantitative analyses carried out on the diaristic corpus, the hypotheses put forward have all been confirmed, consolidating the line of research aimed at investigating the role played by emotional competence in the development of critical reflexivity, in interaction and combination with the increasingly complex structuring of the cognitive processes underlying reflexivity. Full article

Background: Hearing aid (HA) outcome is a multidimensional construct that requires not only the analysis of auditory function improvement, but also a subjective evaluation of benefits from HAs. Indeed, subjective satisfaction of patients with HAs is not entirely predictable from audiometric outcomes such as real ear gain or functional gain. In light of this possible discrepancy the 1990 Consensus Statement for “Recommended Components of a Hearing Aid Selection Procedure for Adults” suggested that verification of hearing aids benefit also incorporate the subjective satisfaction with amplification. Objectives: The aim of this study was to test the validity and reliability of the Italian version of International Outcome Inventory for Hearing Aids (IOI-HA-It). Methods: Ninety-eight outpatients were randomly recruited to participate in this study. They all made regular use of HAs and were supplied with three different self-administered questionnaires. The International Outcome Inventory for Hearing Aids (IOI-HA), the Hearing Handicap Inventory for Adults (HHIA) or for elderly (HHIE) and the Italian translation of the MOS 36-Item Short Form Health Survey (SF-36). The epidemiological features and results were analyzed as descriptive statistics. Continuous variables were expressed as means with standard deviations (SDs). Reliability of the Italian version was assessed by the following two parameters: internal and test–retest consistencies. Internal consistency reliability was measured by Cronbach’s alpha coefficient. Results and Conclusions: This study evidenced that the IOI-HA-It is proved to offer adequate subjective outcome measures to better appreciate the integral evaluation of a patient’s rehabilitative experience. Furthermore, since it is a very brief questionnaire with low demand on time and cost involved in its compilation, it should be recommended in clinical practice. Full article

Nanobodies (VHHs or single-domain antibodies) are powerful affinity reagents, but their routine use is often limited by production constraints and by the lack of a conserved Fc region for secondary detection. We describe a simplified strategy in which functional GST–nanobody fusion proteins are expressed directly in the cytoplasm of Escherichia coli Origami^TM 2 (DE3), a strain that supports disulfide bond formation through trxB/gor mutations. Using well-characterized nanobodies against GFP (Lag2) and mCherry (C11), we designed N-terminal GST fusions and confirmed by AlphaFold3-based modeling that both constructs preserve the GST fold and the VHH (Variable domain of the Heavy-chain antibody of Heavy-chain-only antibodies) β-sandwich with defined CDR loops and a predicted intradomain disulfide bond. Following IPTG induction and purification by glutathione affinity and size-exclusion chromatography, we obtained soluble GST-nb-GFP and GST-nb-mCherry at ~8–12 mg/L. Isothermal titration calorimetry showed nanomolar binding to their antigens (K_d ~123 nM for GFP and ~199 nM for mCherry). Consistent with conformational epitope recognition, GST-nanobodies were reactive in native-state dot blots but not in denaturing Western blots under the conditions tested. The GST moiety enabled indirect immunofluorescence via anti-GST antibodies, yielding specific labeling of GFP- or mCherry-tagged TGN38 in HeLa and H4 cells. Finally, we demonstrate “GST-nanobody pulldown” as a robust method for affinity capture from cell lysates. Together, this platform provides a low-cost, versatile route to functional nanobody reagents without requiring tag removal, and complements other nanobody designs (e.g., VHH-Fc fusions) in an application-dependent manner. Full article

Blooming time is an important basis for constructing plant landscapes. The short flowering period of Osmanthus fragrans, recognized as one of the ten traditional flowers in China, considerably constrains the further utilization of its resources. To clarify O. fragrans flowering regulation, this study focused on OfCOR27, conducting cloning, expression analysis, and functional verification to explore its effects on O. fragrans flowering time. A COR27 phylogenetic tree was built across six species; OfCOR27 physicochemical properties, conserved structures, and promoter cis-elements were analyzed. OfCOR27 CDS was cloned, fusion vectors were transformed into Nicotiana benthamiana, and organ-specific expression was tested in two O. fragrans cultivars. Overexpression vectors were transformed into Arabidopsis thaliana, with qRT-PCR verifying gene function. Five OfCOR27s were identified, showing evolutionary conservation. OfCOR27, which localizes to the nucleus and is associated with flowering regulation, shows higher expression in ‘Sijigui’ than in ‘XiaoyeSugui’. Overexpression of OfCOR27 promoted flowering in A. thaliana, whereas the AtCOR27 mutant flowered later. This confirms OfCOR27 is a positive regulator of plant flowering, which may promote flowering by enhancing the expression of flowering-promoting genes and altering hormone levels, providing a theoretical basis and candidate gene for the genetic improvement of flowering traits in woody ornamental plants. Full article