Search Results (7,175)

This study describes the development of an electrochemical sensor for quantitatively measuring acetaminophen (ACOP) in drug tablets. The sensor design is based on the modification of glassy carbon electrode (GCE) using zinc oxide nanoparticles (ZnONPs) embedded in a naturally occurring clay matrix (Sa) functionalized with phenylalanine (Phe). To ensure that the ZnONPs are homogeneously dispersed on the clay surface, the nanocomposite was synthesized using an impregnation approach and low-temperature heat treatment. The amino acid promotes specific interactions with ACOP through hydrogen bonding and π-π stacking, acting as both a stabilizing agent and a molecular recognition moiety. FTIR, UV-Vis, XRD, and FESEM/EDX mapping were employed to fully characterize the developed material (ZnONPs-Sa/Phe). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for the electrochemical determination of ACOP using the modified electrode GCE/ZnONPs-Sa/Phe. Parameters susceptible to affecting the sensitivity of the developed sensor were optimized, revealing that 5 µL of the suspension ZnONPs-Sa/Phe immobilized on GCE was ideal for the sensing of ACOP in a phosphate buffer solution at pH 2.0. The calibration curve obtained by plotting peak current intensity against ACOP concentration exhibited linear behavior within the concentration range between 0.02 µM and 0.28 µM, enabling determination of the limits of detection (LOD) and quantitation (LOQ) at 8.54 × 10⁻⁹ M and 2.84 × 10⁻⁸ M, respectively. The reproducibility, stability, and selectivity of the sensor were evaluated, followed by its application to the nano-sensing of ACOP in Africure and Doliprane tablets, yielding satisfactory results. The simplicity, affordability, and high analytical sensitivity of the developed sensor make this sensing platform a promising tool for pharmaceutical quality control applications. Full article

This paper presents a lightweight semantic retrieval framework driven by an in-memory knowledge graph (IMKG) to overcome the limitations of traditional keyword matching and the prohibitive hardware costs of deep learning models in digitizing ancient Chinese literature. By extracting structured metadata from canonical texts, we construct a dense, bidirectional graph schema. Diverging from resource-intensive neural architectures, our system abandons heavyweight vector embeddings in favor of a highly optimized, template-based heuristic matching engine natively implemented in Java. This purely symbolic approach ensures deterministic execution, zero-dependency deployment, and seamless operation on standard CPU-only servers. To handle complex historical inquiries, the framework integrates a context-aware dialogue manager for multi-turn anaphora and ellipsis resolution, alongside a synergistic tiered caching mechanism. Extensive evaluations on a benchmark of 13,652 annotated queries demonstrate that the system achieves an exceptional intent recognition accuracy of 97.14%, robust context retention, and ultra-low response latency (≤17 ms). Ultimately, this architecture provides a sustainable, highly reproducible, and cost-effective paradigm for the semantic exploration of classical textual heritage, exceptionally suited for small-to-medium cultural institutions. Full article

Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article

Background: Standardized and ethically compliant animal models remain essential for improving translational research in Alzheimer’s disease. Although Aβ_1–42-induced rodent models are widely used, methodological variability continues to limit reproducibility. Methods: We explored the feasibility of a stereotactic intrahippocampal Aβ_1–42 rat model established by bilaterally injecting pre-aggregated peptide into the hippocampus of adult Sprague Dawley rats. Model feasibility and targeting accuracy were assessed intraoperatively. Cognitive performance was evaluated using the Y-maze for spatial recognition memory and the novel object recognition (NOR) test. Histological examination was performed using hematoxylin–eosin (H&E) and Congo red staining to assess cytoarchitecture and to provide supportive evidence of amyloid-like deposits. Results: The surgical procedure was well-tolerated, and the injected animals showed reduced performance in behavioural testing, including reduced spatial recognition memory in the Y-maze and decreased discrimination indices in the NOR test. The animals also showed histological changes, including Congo red-positive birefringent structures consistent with amyloid-like congophilic material. Conclusions: This study presents a feasible experimental framework for intrahippocampal Aβ_1–42 administration, showing behavioural and histological changes under the present experimental conditions. However, further validation, including sham-operated controls and molecular characterization, will be required before these findings can be interpreted as specific to Aβ-driven pathology. Full article

As smart eyewear becomes increasingly widespread, the need for hands-free input interfaces is growing. Although eye-based input is a promising approach, many everyday interactions do not necessarily require the high-precision gaze-point estimation used in mainstream camera-based systems; instead, what is often needed is the recognition of coarse eyeball direction. In this study, we propose a method for recognizing coarse eyeball direction using infrared distance sensors mounted on eyewear. The proposed method leverages deformation patterns in the eyelid and surrounding skin associated with changes in eyeball direction. The evaluation results show that the proposed method achieved macro-F1 scores of 0.9 or higher in the best-performing conditions for the five- and nine-direction settings. These results demonstrate the feasibility of recognizing coarse eyeball direction from eyelid-skin deformation using infrared distance sensors on eyewear. Rather than replacing high-precision gaze-point estimation, the proposed method can be positioned as a low-cost, non-contact, and low-dimensional sensing approach for command-type eye-based input on eyewear devices. Full article

Congenital anomalies of the reproductive system represent a heterogeneous group of structural and functional abnormalities affecting both male and female genital organs. These anomalies typically arise during embryogenesis and may remain asymptomatic until they are incidentally identified during evaluation for infertility, recurrent pregnancy loss, or disorders of sexual development. In females, abnormalities include Müllerian duct anomalies and congenital malformations of the uterus, cervix, vagina, and ovaries, such as Mayer–Rokitansky–Küster–Hauser syndrome, septate, unicornuate, bicornuate, and didelphys uteri, and ovarian agenesis and undescended ovaries. In males, congenital conditions such as anorchia, cryptorchidism, hypospadias, ejaculatory duct obstruction, and ejaculatory dysfunction may be associated with impaired spermatogenesis and reduced fertility. Early recognition of these conditions may facilitate timely clinical evaluation and individualized management, which can include surgical correction, hormonal therapy, and reproductive counseling. When appropriate, early diagnosis may support multidisciplinary care, with the aim of optimizing sexual development, preserving reproductive potential, and reducing long-term morbidity associated with congenital anomalies. However, the clinical impact of early detection varies depending on the type and severity of the anomaly. A systematic and multidisciplinary approach may contribute to improved reproductive outcomes and better overall reproductive health in affected individuals. Further research is needed to better define the optimal timing and clinical utility of systematic evaluation strategies in this patient population. Full article

Identification of unknown individuals is challenging, and radiological imaging databases provide rich anatomical information for automated recognition. This study evaluated whether a single routine magnetic resonance imaging (MRI) slice contains sufficient person-specific features to identify individuals in large databases. It analyzed 11,078 head MRI examinations from 5770 individuals (age 52 ± 18 years, 2714 men) acquired between 2002 and 2025. For identification, 112 individuals were randomly selected across eight 10-year age groups, and one slice from four anatomical regions was extracted. The remaining 10,966 MRI examinations with 247,804 slices formed the reference database. Distinctive anatomical features were automatically extracted using computer vision (CV), and the identification rate was evaluated by rank. Using a single MRI slice, the identification rate at rank 1 reached 96% (107/112) for the best-performing region, the maxillary sinus, among 5770 potential identities. Across all regions, the rank 1 identification rate ranged from 91% to 96%; combining them increased rank 1 and 10 identification rates to 98% (110/112) and 99% (111/112). Identification rate remained stable over several years, with only two cases showing reduced rank 1 performance, likely due to age-related morphological changes. A single MRI slice contains stable, individualized features sufficient for reliable identification in large databases, supporting automated CV-based personal identification across years. Full article

Background: In older people, syphilis diagnosis might be undervalued due to both clinical conditions and age-related changes that obscure symptom presentation and physician discomfort with sexual history-taking, creating a dual barrier to timely recognition. Methods: Case presentation with literature review. Results: An 80-year-old woman was referred to the Dermatology Department of Cagliari University by her oncologist, with a 2-month history of intermittent episodes of pruritus associated with papular–nodular skin lesion eruptions, accompanied with asthenia, night sweats, and unintentional weight loss, indicative of a paraneoplastic syndrome or an adverse drug reaction. Careful evaluation indicated the need to perform serological testing, which confirmed secondary syphilis (RPR 1:64 and TPHA 1:5120). Specific questioning regarding sexual behaviors pointed out oral and anal intercourse. The 83-year-old husband did not have active lesions at visit but reported a self-healing generalized skin rash, episodes of asthenia, arthralgia, and headache he had never suffered before. Blood tests showed positive RPR 1:64 and TPHA 1:5120. Targeted sexual history assessment disclosed patient’s engaging with commercial sex workers, clarifying the chain of transmission in this conjugal STI case. Treatment with Benzathine penicillin G 2.4 million units IM in a single dose resulted in complete recovery in both patients. Conclusions: The observation highlights the importance of maintaining a high index of suspicion for syphilis even at advanced age. Persistent stigma regarding elderly sexuality should be faced, and targeted interventions are necessary to improve the clinician’s ability to identify STIs in older adults, but also to reduce sexual stigma and taboo persistence in the general population. Full article

The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. Full article

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series. Full article

The integration of artificial intelligence with unobtrusive sensing technologies is transforming occupational health monitoring by enabling continuous, objective assessment of worker activities in real industrial environments. This study focuses on the accurate recognition of subtle motion actions within logistics workflows using multichannel optical motion-capture data. We investigate several deep learning architectures commonly employed for temporal motion analysis, including tCNN, Transformer, CNN–LSTM, and ConvLSTM. To enhance robustness and fairness across workers with varying movement styles, a subject-independent evaluation protocol is adopted, and a multiscale temporal learning strategy is explored to better capture fine-grained and low-saliency actions. Experimental results show that the proposed multiscale tCNN achieves the highest accuracy, obtaining per-class recall range between 73% and 83% and an overall accuracy of approximately 79%, consistently outperforming recurrent and attention-based architectures. These findings demonstrate the effectiveness of multiscale convolution-based temporal modeling for recognizing subtle motion actions and highlight the potential of combining optical motion capture with AI analytics to support unobtrusive, reliable occupational health monitoring in smart industry environments. Full article

Modern neural networks have achieved remarkable success in visual recognition; however, due to their sensitivity to domain shifts, Unsupervised Domain Adaptation (UDA) remains an open research problem. A key reason for this limitation is that source-trained models rely primarily on texture, lacking the explicit geometric information required for object recognition. To overcome this problem, we introduce CoDA, an object-centric learning framework inspired by infant cognitive development, specifically the process of object individuation. By introducing a geometric prior, our approach employs a physically grounded generation pipeline that uses a textureless “Sculpture Mode” and object isolation to complement textural information with 3D geometric features, capturing shape information that is often ignored during training. To enable robust training from scratch, we further integrate two control mechanisms: a Network Stability Scheduler to orchestrate training progression based on convergence stability, and a Dynamic Top-K Pseudo-Labeling strategy that adapts confidence thresholds for each individual class. Extensive evaluations on three real-world target datasets (VegFru, Fruits-262, and Open Images v7) demonstrate that CoDA, trained on a source dataset of just 12,000 synthetic images, achieves comparable results to (and in specific domains surpasses) ImageNet-pretrained models (leveraging 1.2 million images), significantly outperforming state-of-the-art adversarial and semi-supervised domain adaptation methods. Full article

Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including linguistic-based and embedding-based language clustering, have attempted to address this; yet, they remain constrained by their static design and lack of task-specific feedback. In this study, we propose a novel computational strategy inspired by molecular design that constructs molecules with targeted properties. Languages are modeled as nodes in an undirected graph, with edges representing the transfer strength. This language molecule is optimized via Reinforcement Learning to adjust edge connections and weights to enhance positive transfer and minimize interference, where graph clustering is applied, and clusters are then evaluated on the Named Entity Recognition and POS tagging tasks using 25 languages from the WikiANN dataset and 12 typologically diverse languages from the UDPOS dataset. Compared to linguistic and embedding-based language clustering baselines, our method yields substantial improvements, especially for low-resource languages, with some showing over 35% increase in F1 score, while high-resource languages benefit moderately, confirming reduced transfer–interference trade-off. Our atom–language model offers a novel path for multilingual learning, inspired by molecular principles from physical sciences. Full article

Permian shale is the largest and most promising shale gas exploration target in southern China after Silurian shale. The fine evaluation of shale reservoirs is a prerequisite for large-scale exploration and development. Based on the fractal method, this study, through the combined technology of nuclear magnetic resonance cryoporometry (NMRC) and Image recognition software (Image-J), clarifies the pore size distribution of Permian shale in the HGG Basin. The purpose of this study is to characterize the macroscopic parameters of Permian shale and reveal the level of reservoir space development in Permian shale. The controlling factors of porosity and pore structure are demonstrated. It is suggested that Permian shales in the HGG Basin have organic carbon contents similar to marine shales. In the favorable interval of the Dalong Formation, the average organic carbon content is comparable to that of the LMX pay zone. The lower Longtan shales have the highest organic carbon and the greatest gas generation potential, followed by the Dalong shales. TOC is the primary control on porosity in the lower Longtan and Dalong formations, whereas clay minerals dominate the control in the upper Longtan. Abundant pores between grains and between layers within clay minerals account for most of the porosity in Upper Longtan shale. In the lower Longtan and Dalong formations, organic pores are pervasive, explaining the difference in the dominant controls on porosity between these intervals. Clay minerals are a key control on the development of Permian shale reservoirs. The fractal dimension of adsorption pores (D_A) has no clear relationship with the total clay content, is negatively correlated with the illite content, and shows no clear relationship with the chlorite content. In contrast, the fractal dimension of flow pores (D_S) shows a weak positive correlation with the total clay content, a clear positive correlation with the illite content, and a negative correlation with the chlorite content. When illite interacts with water, it tends to break down and plug pores, an effect that is especially pronounced in the smallest pores hosted by organic matter; this accounts for the negative correlation between D_A and the illite content. In larger, flow-bearing pores, disintegrated illite roughens otherwise smooth walls between and within grains, increasing structural complexity and raising D_S. By contrast, reactions between chlorite and pore fluids tend to smooth the walls of flow pores, reducing structural complexity and lowering D_S. Full article

Partial discharge (PD) measurements are crucial for evaluating the condition of the insulation systems of medium-voltage (MV) cables and their accessories. However, identifying PD defect types from phase-resolved partial discharge (PRPD) patterns still largely relies on expert knowledge. In this paper, the authors critically evaluate lightweight deep neural network architectures for automated classification of insulation defects from PRPD patterns: YOLOv8n, the MobileNetV2–YOLO hybrid network, and a compact SqueezeNet-based model. PD measurements were performed in a controlled environment in a factory laboratory for MV power cables in order to better evaluate the capability of the investigated models. The results demonstrate that lightweight deep neural architectures can effectively classify PRPD patterns and be deployed in a real measurement environment. The proposed approach has been integrated with the OMICRON MPD Suite measurement system, enabling automated defect recognition and visualisation during routine testing of MV cable. Full article