Search Results (3,289)

In this work, a novel nanostructured Mn₃O₄-based electrochemical sensor was developed for the determination of heavy metals in aqueous media. The Mn₃O₄ nanostructure was solvothermally synthesized in the sole presence of propylene glycol (PG). Under the specific synthetic conditions, PG provided surface coating and stabilization by decomposition products and/or residual PG molecules that have been adsorbed on Mn₃O₄ NPs surfaces, creating a thin organic layer. This imparts a negative surface charge (zeta potential), enhancing colloidal stability in dispersions and electrochemical performance. The physicochemical properties of the resulting NPs were characterized via X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Thermogravimetric Analysis (TGA), and Dynamic light scattering (DLS) and ζ-potential measurements, as well as SEM imaging of the modified electrode surface, confirming its successful formation and favorable structural properties. The LODs of Cd²⁺, Pb²⁺, Zn²⁺, and Cu²⁺ for their simultaneous determination are 2.9 μg·L⁻¹, 5.2 μg·L⁻¹, 7.1 μg·L⁻¹, and 2.5 μg·L⁻¹, respectively, with relative standard deviations of about 5.24%, 4.43%, 7.74%, and 4.53%, respectively. As a result of this study, a simple, sensitive, and reproducible electrochemical sensor based on a carbon paste electrode (CPE) modified with novel synthesized manganese nanoparticles and employing voltammetric techniques was applied in water and wastewater. Full article

Volcanic magma plumbing systems is essential for understanding crustal–mantle material exchange and the dynamics of volcanic activity. The magnetotelluric method (MT) offers an effective tool for imaging conductive features from the crust to the lithospheric mantle. However, current survey strategies face a tradeoff between imaging resolution and acquisition cost. Here, we construct a lithosphere-scale synthetic model of a magma plumbing system and use 3D MT inversion, sensitivity analysis, and point spread function evaluation to assess the resolving capability of sparse versus dense arrays. Our results show that large-scale conductive anomalies in the mid–lower crust and lithospheric mantle can be reliably imaged using a sparse regional array with targeted densification in the crustal anomaly zone. This approach reduces field costs and computational demand. Guided by these findings, we conducted MT observations across the Longgang volcanic field and identified low-resistivity anomalies extending from the lithospheric mantle into the mid–lower crust. These features are consistent with the dense array MT inversion results. Our study demonstrates that an array strategy combining wide-area sparse coverage with targeted densification offers a cost-effective approach to image deep conductive structures, which may provide practical guidance for optimizing MT survey design in volcanic regions. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) and its consequences represent a growing global health burden that urgently requires physiologically relevant in vitro models beyond conventional 2D culture systems. In this study, we report the successful establishment of 45 patient-derived liver organoid lines. These organoids were generated from healthy, steatotic and cirrhotic tissues collected from 207 liver surgeries at RWTH University Hospital Aachen, with an initiation success rate of 82%. The organoids were propagated for at least six passages using an optimized protocol. Multiplex immunofluorescence analysis revealed highly proliferative structures with approximately 40% Ki-67-positive cells expressing hepatocyte (Albumin and HNF4α) and cholangiocyte (CK19) markers. Intermittent LGR5 staining suggested the presence of liver progenitor cell features. Quantitative PCR results confirmed variable HNF4α expression, indicating inter-patient heterogeneity in differentiation status. Time-lapse imaging combined with mathematical modeling uncovered a biphasic growth dynamic with an initial linear expansion in the first 15 h, followed by exponential growth (doubling time ≈ 20.6 h) between 30 and 72 h. Overall, our workflow produced genetically and phenotypically stable liver organoids that recapitulate essential features of various hepatic conditions. This provides a solid foundation for disease modeling, potential drug testing, and quantitative systems biology. Full article

The brain is a complex structural network. The employment of fluorescent labeling techniques in conjunction with advanced imaging methodologies facilitates comprehensive analysis of multiscale brain anatomy, thereby offering insights into fundamental principles of function and addressing neurological disorders. This review summarizes technological advances in fluorescent labeling methods in the field of neuroscience, and their applications in neural circuit analysis, cerebrovascular imaging, neuronal activity monitoring, and fluorescence-guided treatment of brain tumors. A challenging trend in integrating smart fluorescent labeling with tissue clearing, wide-field 3D imaging, artificial intelligence-assisted data processing/reconstruction, and multimodal information fusion is highlighted and discussed. The future direction of combining high-resolution, low-damage, dynamic imaging with big data analysis is envisioned, providing tools for understanding brain structure and function and their roles in disease. Full article

Background/Objectives: Aggressive variants of prostate cancer pose significant diagnostic and prognostic challenges due to atypical imaging appearances, variable prostate-specific antigen behavior, and distinct molecular features. Conventional imaging may underestimate their biological aggressiveness. This review aimed to synthesize current evidence on imaging characteristics, biomarker dynamics, tumor localization, histology, and radiomic features of aggressive prostate cancer variants, and to evaluate the potential role of radiomics in early recognition and risk stratification. Methods: A structured narrative review was performed of studies reporting imaging, clinical, and molecular features of aggressive prostate cancer variants. Imaging modalities included multiparametric magnetic resonance imaging, positron emission tomography with prostate-specific membrane antigen or fluorodeoxyglucose, bone scintigraphy, and transrectal ultrasound. Data on prostate-specific antigen levels and kinetics, intraprostatic tumor location, tumor size, metastatic patterns, and molecular alterations were extracted. Evidence for rare entities such as basaloid and primary squamous carcinomas was derived from published case reports and series, while selected variants were complemented by institutional imaging and histopathologic observations. Results: Neuroendocrine and small cell carcinomas frequently showed low prostate-specific antigen levels, high fluorodeoxyglucose uptake, low prostate-specific membrane antigen expression, and central or transitional zone involvement with large tumor size at diagnosis. Ductal adenocarcinoma demonstrated marked diffusion restriction and elevated prostate-specific antigen, whereas basal cell carcinoma often appeared inconspicuous on conventional imaging. Radiomic analysis consistently captured tumor heterogeneity and spatial complexity beyond standard qualitative metrics. Conclusions: Aggressive prostate cancer variants represent a diagnostic blind spot in routine imaging. Radiomics offers complementary quantitative information that may improve early detection, subtype differentiation, and risk stratification when integrated into multimodal imaging workflows. Further prospective and radiogenomic studies are warranted to validate these findings. Full article

China possesses abundant marine fishery resources, which play a vital role in the national economy. Achieving rapid and high-precision classification of underwater targets in complex aquatic environments is of significant importance for enhancing aquaculture intelligence and operational efficiency. To address the challenges of insufficient feature extraction and inefficient classifier parameter optimization in underwater image classification, this study proposes a classification method integrating local binary patterns (LBP), kernel principal component analysis (KPCA), and an improved sparrow search algorithm (SSA). The method first extracts image texture features using LBP and then applies KPCA for nonlinear dimensionality reduction. Subsequently, three optimization strategies—dynamic weighting, boundary contraction, and adaptive mutation—are introduced to enhance SSA, which is then employed to optimize the core parameters of the Support Vector Machine (SVM). Experiments were conducted on an underwater image dataset containing four types of targets: sea urchins, fish, rocks, and scallops. The results demonstrate that, compared with the traditional KPCA-SVM method, the integration of LBP features and the improved SSA increases classification accuracy from 55% to 94.37%, validating the effectiveness of the proposed approach in extracting underwater image features and optimizing classifier parameters. This provides technical support for improving the feasibility of automatic underwater target recognition in aquaculture applications. Full article

Background: Childhood and adolescence constitute a critical period for craniofacial growth. Understanding its developmental patterns is essential for clinical decision-making in orthodontics and maxillofacial surgery. Traditional cephalometric analysis relies on manual landmarking, which oversimplifies complex morphology and introduces subjectivity. Although deep learning, a key artificial intelligence (AI) technology, has demonstrated remarkable performance in image analysis and classification, most methods still depend on manual annotations during training, perpetuating subjectivity and limiting model generalizability and robustness on large datasets. This hinders the development of objective, comprehensive methods to quantify craniofacial growth that account for its multi-tissue complexity. Methods: To address these limitations, this study developed an end-to-end deep learning framework based on lateral cephalometric radiographs from 41,625 individuals aged 4–18 years. Without relying on manual annotations, the model is designed to autonomously extract dynamic imaging features associated with continuous age intervals in craniofacial development and further discern features related to sexual dimorphism. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize the learned features, generating population-averaged saliency maps that highlight age-related and sex-related patterns. Furthermore, we introduced two novel quantitative metrics, the Age-related Saliency Index (ASI) and the Sex-related Saliency Index (SSI), to evaluate the significance of developmental and dimorphic characteristics in key craniofacial regions. Results: Age-related saliency maps extended the focus from external contours to internal anatomical details of the bones, intuitively visualizing the relative importance of multiple bone regions during dynamic development, with the ASI providing a quantitative prioritization of these regions. The Sex-related Saliency Index (SSI) quantified the dynamic evolution of sexual dimorphism, demonstrating that early-stage differences were widely distributed across cranial bones and gradually became concentrated in the mandibular region by adulthood. Conclusions: This study established an end-to-end deep learning framework for analyzing large-scale lateral cephalometric radiographs. By generating age- and sex-related average saliency maps and their corresponding quantitative indices, we visualized and quantified the spatiotemporal growth dynamics and sexual dimorphism across distinct craniofacial skeletal regions throughout development. These findings not only validate established developmental theories but also provide novel insights into the coordinated growth patterns of craniofacial bones and sex-specific radiological characteristics, offering clinicians objective quantitative references for assessing developmental stages and guiding the timing of interventions targeting specific craniofacial regions. Full article

A productivity simulation for hydraulically fractured wells with complex fracture geometry involves a heavy computational burden and is therefore not suitable for engineering-scale fracture-optimization designs and production-analysis applications. This paper develops a productivity-prediction surrogate model based on a deep convolution–bidirectional gated recurrent unit temporal network (DC-BiGRU) framework where a deep convolutional neural network is used to extract features from fracture images, while a BiGRU model was designed to fully capture valuable information from the production sequence. Some additional inputs, e.g., cluster spacing and stage spacing, that account for different fracture-placement designs in horizontal wells were also considered. A large number of shale-gas production data samples at different times were generated using a fractured-horizontal-well productivity simulator under diverse hydraulic-fracture geometries and bottom-hole flowing pressures. The surrogate model had relative errors below 10% with an average error of about 6%. Compared to high-fidelity capacity prediction simulators, the computational efficiency of the deep learning surrogate models was improved by two to three orders of magnitude. The runtime of the high-fidelity numerical simulator was about 20 min, while the surrogate model, which was run on an NVIDIA Tesla P100 GPU (NVIDIA, Santa Clara, CA, USA), took less than 1 s, which is almost negligible. The proposed surrogate model resolved the low efficiency of the productivity simulation for complex-fracture hydraulic fracturing wells in unconventional reservoirs, enabling rapid dynamic forecasting of fractured-well productivity. Full article

This article explores Albert the Great’s understanding of human cognition as a hierarchical, semiotic structure, made of light. It examines his response to the question “What is good for man?”, tracing his shift from a moral–theological to an anthropological and epistemological perspective in dialogue with Aristotelian, Neoplatonic, and Arabic sources. Through close textual analysis of his writings on the soul and intellect, the article reconstructs man’s hierarchical constitution and highlights the central role of signs and of the imagery of light and shadows in his understanding of cognition. It argues that, for Albert, each level of apprehension functions as a semiotic link that dynamically leads the human intellect from lower to higher degrees of comprehension, intentionally pointing toward the divine source of all being, understood as light. Albert’s conception of signs, intentionality, and intellectual illumination is shown to anticipate and go beyond later semiotic theories. Consequently, the article proposes that he should be regarded as a “proto-semiotic” thinker whose original anthropological synthesis, centered on epistemology and sign-theory, illuminates the intrinsic role of signs in human perfection and clarifies how words and images can express the cognitive relation between created and uncreated being. Full article

The negative impacts caused by synthetic herbicides have necessitated research on environment-friendly and sustainable alternatives. In this study, a novel botanical nanoherbicide was developed through green synthesis of silver nanoparticles (Ag NPs) assisted by aqueous extract of Brucea javanica (BJ) residue. The BJ-Ag NPs were characterized using ultraviolet–visible (UV–Vis) absorption spectroscopy, dynamic light scattering (DLS), zeta potential analysis, X-ray diffraction (XRD), and transmission electron microscopy (TEM) attached with energy dispersive X-ray spectroscopy (EDX). TEM images indicated that the BJ-Ag NPs were spherical with an average particle size of 12.75 nm. Meanwhile, the herbicidal activity against two paddy weeds (Echinochloa crusgalli and Bidens pilosa L.) and phytotoxicity to rice (Oryza sativa L.) were evaluated using the Petri dish method. Compared to the BJ residue extract, the BJ-Ag NPs exhibited enhanced inhibitory activity on the seed germination and seedling growth of two target weeds, while showing alleviated phytotoxicity and partially restored seedling vigor in rice. Obviously, positive impacts on both the weed and crop were obtained after synthesizing Ag NPs using the BJ residue extract. The results in this study demonstrated the potential of the BJ-Ag NPs as a sustainable, crop-friendly nanoherbicide for weed management in paddy fields. Full article

Precise modeling and simulation of pedestrian flow are crucial for public space safety design and emergency management. This study proposes an interdisciplinary method integrating computer vision and cellular automata (CA). First, unidirectional pedestrian flow video data with different densities were collected from an overpass scene via controlled experiments. High-precision pedestrian trajectory extraction and tracking were achieved using the YOLO 11 model and DeepSORT algorithm, with image distortion corrected by perspective transformation. For the first time, the probability distribution of pedestrian turning angles derived from trajectory analysis was converted into data-driven transition probabilities for the Moore neighborhood in the CA model. An improved evacuation model was then constructed, comprehensively considering real-data-based transition probabilities, speed–density distribution, panic coefficient, individual life value, and hazard source dynamics. Multi-scenario simulations show that moderate panic may shorten evacuation time, while excessive panic causes behavioral disorders; group movement is constrained by the slowest individual, and increased hazard source speed reduces the proportion of safe pedestrians. This study provides new insights and methodological support for refined pedestrian evacuation simulation and safety management. Full article

This paper introduces VERA (Vision Expert Real Analysis), a privacy-supporting cyber-physical framework designed for real-time data collection and visual analysis in healthcare environments. VERA limits exposure to identifiable RGB content by ensuring that annotators interact only with non-identifiable edge-based representations, while original images remain encrypted at rest using AES-CFB, with integrity verification performed before in-memory decryption. The system integrates edge-based obfuscation, secure annotation, in-memory decryption, and dynamic data augmentation to train YOLO-based person detection models without compromising patient privacy. Experimental results on a curated COCO subset show that VERA enables effective person detection, improving mean Average Precision (mAP) from an intentionally minimal baseline of 0.61 percent to 99.94 percent after full training and augmentation. This baseline is used solely to illustrate the contribution of the secure data preparation pipeline and is not intended to represent a fully optimized YOLO configuration. The results demonstrate that privacy-supportive workflows can maintain strong model performance while aligning with data protection practices common in regulated environments. Although this work focuses on person detection as a foundational stage, the VERA architecture is designed to support future extensions toward privacy-preserving Human Activity Recognition (HAR) tasks in clinical and assisted-living settings. Full article

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

In the split main transformer room of the indoor substation studied in this paper, the heat dissipation area of the transformer main body and part of the convection pipeline accounts for approximately 5.4% of the total heat dissipation area, with the outdoor radiator responsible for releasing most of the heat. Compared with the integrated main transformer room of indoor substations, the split-type design features a smaller building size and lower ventilation energy consumption, thus it is widely applied in urban areas. This study employs computational fluid dynamics (CFD) simulation to investigate the natural ventilation and heat dissipation performance of the main transformer room in a 110 kV indoor substation located in the Shijiazhuang area. A thermal imager is used to capture the surface temperature distribution of the main transformer, and the data is fitted into a polynomial function. During the numerical simulation, the surface temperature of the main transformer is set using a user-defined function (UDF), and the total heat dissipation of each heat-dissipating surface of the transformer is extracted via FLUENT(Ansys 2024 R2) software as the basis for evaluating the ventilation and heat dissipation effectiveness. The effects of different ventilation window sizes on the natural ventilation heat dissipation and air change rate of the indoor substation’s main transformer room under thermal pressure are compared. The feasibility of this numerical simulation method is verified through experimental measurements and theoretical analysis. Full article

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article