Search Results (2,646)

With the rapid development of artificial intelligence technology, its widespread application in the field of business management has become a significant issue faced by contemporary enterprises. Based on the Technology Acceptance Model, this study explores the impact of AI technology acceptance on organizational decision-making efficiency, performance, and the depth of technology application. It also reveals the driving mechanisms of top management support, perceived usefulness, and perceived ease of use on AI technology adoption through path analysis. To validate the research hypotheses, the study employed structural equation modeling (SEM) based on survey data collected from 420 respondents across various industries. The study found that top management support significantly enhances technology acceptance through perceived variables, while perceived usefulness is the core factor driving technology adoption. Although perceived ease of use has a weaker effect, it is equally important in lowering the psychological barriers during the initial stages of technology adoption. The adoption of AI technology has significantly improved organizational decision efficiency and overall performance, promoting the deep application of technology by optimizing resource allocation and enhancing scientific decision-making capabilities. This study further validates the applicability of the TAM theory in the context of AI technology, expanding its theoretical explanatory power in complex technology-adoption mechanisms. At the same time, the research provides practical guidance for enterprises in the introduction and application of technology, emphasizing that managers need to shape an open and innovative organizational culture at a strategic level and enhance employees’ willingness to accept technology through technical training and value transmission. Future research can incorporate cross-cultural and multi-level analytical frameworks to explore the dynamic adaptation paths of AI technology adoption and its potential risks in sustainable development. Full article

Background: Gastrointestinal (GI) disorders present significant healthcare challenges, requiring rapid, accurate, and effective diagnostic methods to improve treatment outcomes and prevent complications. Wireless capsule endoscopy (WCE) is an effective tool for diagnosing GI abnormalities; however, precisely identifying diverse lesions with similar visual patterns remains difficult. Methods: Many existing computer-aided diagnostic (CAD) systems rely on manually crafted features or single deep learning (DL) models, which often fail to capture the complex and varied characteristics of GI diseases. In this study, we proposed “EndoNet,” a multi-stage hybrid DL framework for eight-class GI disease classification using WCE images. Features were extracted from two different layers of three pre-trained convolutional neural networks (CNNs) (Inception, Xception, ResNet101), with both inter-layer and inter-model feature fusion performed. Dimensionality reduction was achieved using Non-Negative Matrix Factorization (NNMF), followed by selection of the most informative features via the Minimum Redundancy Maximum Relevance (mRMR) method. Results: Two datasets were used to evaluate the performance of EndoNer, including Kvasir v2 and HyperKvasir. Classification using seven different Machine Learning algorithms achieved a maximum accuracy of 97.8% and 98.4% for Kvasir v2 and HyperKvasir datasets, respectively. Conclusions: By integrating transfer learning with feature engineering, dimensionality reduction, and feature selection, EndoNet provides high accuracy, flexibility, and interpretability. This framework offers a powerful and generalizable artificial intelligence solution suitable for clinical decision support systems. Full article

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities. Full article

During rapid-onset environmental catastrophes, scientists may not always have sufficient time to conduct proper environmental surveys in all representative areas. Although coral bleaching events can be predicted to a certain extent in some areas by tracking sea surface temperatures (SSTs), current models from NOAA’s Coral Reef Watch tend to underestimate severity of bleaching in the Indian Ocean, as was evident in March 2024 when corals began bleaching after only experiencing 1–2 degree-heating weeks. To characterize the impacts of this event, I conducted citizen science-style surveys at 22 sites along a 600-km stretch of the Kenyan coastline. Thereafter, I trained an artificial intelligence (AI) to extract coral abundance and bleaching data from 2300 coral reef images spanning 11–12 hectares of reef area to estimate both coral cover and bleaching prevalence. The AI’s accuracy was >80%, though it was prone to false-positive bleaching classifications. Bleaching severity varied significantly across sites, as well as over time, as seawater continued to warm over the duration of the study period; on average, over 75% of all reef-building scleractinians had bleached. Across the 22 sites, the mean healthy coral cover was only 7–8%, vs. >30% at sites in the same areas in the late 1990s. Whether these corals can recover, and then withstand such heatwaves in the future, will be known all too soon. Full article

Beneath the promise of clean energy, the rapid rise of wind energy farms has stirred mounting concern for pollinator-dependent livelihoods—particularly in beekeeping. This study investigates the effect of wind energy farms on honey-related income using data from six provinces in Turkiye’s Aegean region and the propensity score matching method. Results show that beekeepers operating near wind energy farms experience significantly higher incomes—an average treatment gain of 45,107 TL, with treated groups earning 56,515 TL more—backed by several robust statistical evidence such as placebo and bootstrap techniques. Certain groups—such as younger, nomadic, and family-trained beekeepers, and those receiving financial support—exhibit greater resilience. The findings highlight the need for land-use strategies that balance renewable energy development with ecological and economic concerns. Introducing bee-friendly vegetation around turbines is proposed as a practical solution. This approach can foster a mutually beneficial relationship between wind energy farms and beekeeping, supporting both rural livelihoods and the broader goals of sustainable development. Full article

The acid number evaluates the degree of deterioration of lubricating oil. Existing methods for evaluating the performance degradation of lubricating oils are mostly based on the detection of traditional physical and chemical indicators, which often only reflect a single dimension of the degradation process, thus affecting the accuracy and repeatability of the results. Integrating multi-dimensional information can more comprehensively reflect the essence of degradation, which can improve the accuracy and reliability of the evaluation results. Mid-infrared spectroscopy is an effective means of monitoring the acid number. In this study, a combination of infrared spectroscopy quantitative analysis and chemometrics was used. The oil sample data was divided into training set and validation set by the Kennard–Stone method. In the experiment, a Fourier transform infrared spectrometer equipped with an attenuated total reflection accessory (ATR-FTIR) was used to collect spectral data of the samples in the wavenumber range of 1750–1700 cm⁻¹ (this range corresponds to the characteristic absorption of carboxyl groups and is directly related to the acid number). Meanwhile, a G20S automatic potentiometric titrator was used to determine the acid number as a reference value in accordance with GB/T 7304. The study compared various preprocessing methods. A regression prediction model between the spectra and acid number was established using partial least squares regression (PLSR) within the selected wavenumber range, with the root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), and coefficient of determination (R) as evaluation indicators. The experimental results showed that the PLSR model established after preprocessing with second derivative combined with seven-point smoothing exhibited the optimal performance, with an RMSECV of 0.00505, an RMSEP of 0.14%, and an R of 0.9820. Compared with the traditional titration method, this prediction method is more suitable for real-time monitoring of production lines or rapid on-site screening of equipment. It can in a timely manner warn of the deterioration trend of lubricating oil, reduce the risk of equipment wear caused by oil failure, and provide efficient technical support for lubricating oil life management. Full article

Currently, train component fault detection is predominantly carried out through manual inspection, a process that is inefficient, prone to high omission rates, and carries safety risks. This study proposes an innovative fault detection model for train components based on YOLOv8, aiming to overcome the inefficiencies and high omission rates associated with traditional manual methods. By optimizing the YOLOv8 network architecture and integrating the ADown module, C2f-Rep, and DHD, the model significantly improves computational efficiency and detection accuracy. Experimental results demonstrate that the optimized Train-YOLO model achieves a peak accuracy of 92.9% in train component fault detection. Additionally, it features a smaller model size and reduced computational demands, making it ideal for rapid on-site deployment. A comparison with other leading detection models further highlights the superiority of Train-YOLO in both accuracy and lightweight design. Full article

Gas leak detection in oil and gas processing facilities is a critical component of the safety production monitoring system. Non-contact detection technology based on infrared imaging has emerged as a vital real-time monitoring method due to its rapid response and extensive coverage. However, existing pixel-level segmentation networks face challenges such as insufficient segmentation accuracy, rough gas edges, and jagged boundaries. To address these issues, this study proposes a novel pixel-level segmentation network training framework based on anchor box annotation and enhances the segmentation performance of the YOLOv8-seg network for gas detection applications. First, a dynamic threshold is introduced using the Visual Background Extractor (ViBe) method, which, in combination with the YOLOv8-det network, generates binary masks to serve as training masks. Next, a segmentation head architecture is designed, incorporating dynamic kernels and multi-branch collaboration. This architecture utilizes feature concatenation under deformable convolution and attention mechanisms to replace parts of the original segmentation head, thereby enhancing the extraction of detailed gas features and reducing dependency on anchor boxes during segmentation. Finally, a joint Dice-BCE (Binary Cross-Entropy) loss, weighted by ViBe-CRF (Conditional Random Fields) confidence, is employed to replace the original Seg_loss. This effectively reduces roughness and jaggedness at gas edges, significantly improving segmentation accuracy. Experimental results indicate that the improved network achieves a 9.9% increase in F1 score and a 7.6% improvement in the mIoU (mean Intersection over Union) metric. This advancement provides a new, real-time, and efficient detection algorithm for infrared imaging of gas leaks in oil and gas processing facilities. Furthermore, it introduces a low-cost weakly supervised learning approach for training pixel-level segmentation networks. Full article

In the present study, the changes in the groundwater quality in a Hungarian settlement, Báránd, were examined, nine years after the construction of a sewerage network. The sewerage network in the study area was completed in 2014, with a household connection rate exceeding 97% in 2023. In the summer of 2023, water samples were taken from 37 dug groundwater wells. Changes in the water quality were assessed using three water quality indicators (the Water Quality Index (WQI), Contamination degree (Cd), and Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI)) and geographic information (GIS), data visualization systems, and artificial intelligence (AI). During the evaluation of the quality of the groundwater, eight water chemical parameters were used (pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, COD, Na⁺). Based on interpolated maps and water quality indices, it was established that while an increasing portion of the area exhibits adequate or good water quality compared to the pre-sewerage period, a deterioration has occurred relative to recent years. Even nine years after the sewerage network construction, elevated concentrations of inorganic nitrogen forms and organic matter persist, indicating the continued presence of accumulated pollutants, as confirmed by all three water quality indicators to varying degrees and spatial patterns. The interactive data visualization and cloud-based sharing of the data of the water quality geodatabase were made freely available with the help of Tableau Public. A Feed-Forward Neural Network (FFNN) was developed to predict the groundwater quality, estimating the water quality statuses of three water quality indicators based on water chemistry parameters. The results showed that the applied training algorithms and activation functions proved to be the most effective in the case of different network structures. The most accurate prediction of the WQI and CCME WQI indicators was provided by the Bayesian control algorithm (trainbr), which achieved the lowest mean-squared error (RMSE_WQI = 0.1205, RMSE_{CCME WQI} = 0.1305) and the highest determination coefficient (R²_WQI = 0.9916, R²_{CCME WQI} = 0.9838). For the Cd index, the accuracy of the model was lower (RMSE = 0.1621, R² = 0.9714), suggesting that this indicator is more difficult to predict. With regard to our study, it should be emphasized that data visualization is a particularly practical tool for the post-processing of spatial monitoring data, as it is suitable for displaying information in an intuitive, visual form, for discovering spatial patterns and relationships, and for performing real-time analyses. AI is expected to further increase visualization efficiency in the future, enabling the rapid processing of large amounts of data and spatial databases, as well as the identification of complex patterns. Full article

University students face increased stress and potential school burnout amid rapid digital transformation and competitive academic environments, yet little is known about how socioemotional processes explain the link between burnout and life satisfaction. This study examined how school burnout affects life satisfaction, mediated by loneliness and fear of alienation. A cross-sectional survey of 1783 students was conducted to measure school burnout, loneliness, fear of alienation, and life satisfaction. Structural equation modeling showed that school burnout had a significant negative direct effect on life satisfaction, mediated by loneliness. Higher burnout predicted greater loneliness, which in turn lowered life satisfaction. Although school burnout positively predicted fear of alienation, fear of alienation showed a complex association, with a positive direct path to life satisfaction. However, when loneliness was considered in the full mediation model, the overall indirect effect remained significantly negative. The sequential mediation pathway (school burnout → loneliness → fear of alienation → life satisfaction) highlighted how students’ social disconnection can intensify concerns about exclusion, ultimately affecting their well-being. These findings extend the literature by clarifying the socioemotional mechanisms linking school burnout and life satisfaction. Interventions should address academic demands and bolster emotional support, including resilience training, social skills development, and community-building programs, to mitigate loneliness and manage alienation concerns, thereby promoting students’ life satisfaction and psychological wellness. Full article

Purpose: Mucormycosis is a life-threatening fungal infection, where rapid diagnosis is critical. We developed a deep learning approach using paranasal computed tomography (CT) images to test whether mucormycosis can be detected automatically, potentially aiding or expediting the diagnostic process that traditionally relies on biopsy. Methods: In this retrospective study, 794 CT images (from patients with mucormycosis, nasal polyps, or normal findings) were analyzed. Images were resized and augmented for training. Two transfer learning models (ResNet50 and ConvNeXt Small) were fine-tuned to classify images into the three categories. We employed a 70/30 train-test split (with five-fold cross-validation) and evaluated performance using accuracy, precision, recall, F1-score, and confusion matrices. Results: The ConvNeXt Small model achieved 100% accuracy on the test set (precision/recall/F1-score = 1.00 for all classes), while ResNet50 achieved 99.16% accuracy (precision ≈0.99, recall ≈0.99). Cross-validation yielded consistent results (ConvNeXt accuracy ~99% across folds), indicating no overfitting. An ablation study confirmed the benefit of transfer learning, as training ConvNeXt from scratch led to lower accuracy (~85%) Conclusions: Our findings demonstrate that deep learning models can accurately and non-invasively detect mucormycosis from CT scans, potentially flagging suspected cases for prompt treatment. These models could serve as rapid screening tools to complement standard diagnostic methods (histopathology), although we emphasize that they are adjuncts and not replacements for biopsy. Future work should validate these models on external datasets and investigate their integration into clinical workflows for earlier intervention in mucormycosis. Full article

With the rapid advancement of computer vision and deep learning technologies, the accuracy and efficiency of real-time gesture recognition have significantly improved. This paper introduces a gesture-controlled robot system based on thermal imaging sensors. By replacing traditional physical button controls, this design significantly enhances the interactivity and operational convenience of human–machine interaction. First, a thermal imaging gesture dataset is collected using Python3.9. Compared to traditional RGB images, thermal imaging can better capture gesture details, especially in low-light conditions, thereby improving the robustness of gesture recognition. Subsequently, a neural network model is constructed and trained using Keras, and the model is then deployed to a microcontroller. This lightweight model design enables the gesture recognition system to operate on resource-constrained embedded devices, achieving real-time performance and high efficiency. In addition, using a standalone thermal sensor for gesture recognition avoids the complexity of multi-sensor fusion schemes, simplifies the system structure, reduces costs, and ensures real-time performance and stability. The final results demonstrate that the proposed design achieves a model test accuracy of 99.05%. In summary, through its gesture recognition capabilities—featuring high accuracy, low latency, non-contact interaction, and low-light adaptability—this design precisely meets the core demands for “convenient, safe, and natural interaction” in rehabilitation, smart homes, and elderly assistive devices, showcasing clear potential for practical scenario implementation. Full article

Taiwan accounts for 90% of the total oolong tea production and enjoys a good global reputation for its quality. In recent years, oolong tea from neighboring countries has been imported into Taiwan and sold as Taiwanese oolong at high prices. This study aimed to rapidly classify oolong tea from four geographical origins (Taiwan, Vietnam, China, and Indonesia) using an electronic nose (E-nose) combined with machine learning. Color measurements were also conducted to support the classification. The electronic nose (E-nose) was utilized to analyze the aroma profiles of tea samples. To classify the samples, five machine learning models—linear discriminant analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), and random forest (RF)—were developed using 70% of the dataset for training and tested on the remaining 30%. Gray relational analysis (GRA) was applied to measure the relationship between sensor responses and reference tea origins. Multivariate analysis of variance (MANOVA) indicated a statistically significant effect of tea origin on color parameters, as confirmed by both Pillai’s trace and Wilks’ Lambda (Λ) tests (p = 0.000 < 0.05). Among the tested models, LDA and ANN achieved the highest overall classification accuracy (98.33%), with ANN outperforming in the discrimination of Taiwanese oolong tea, achieving 98.89% accuracy. GRA presented higher gray relational grade (GRG) values for Taiwanese tea samples compared to other origins and identified sensors S4, S6, and S14 as the dominant contributors. In conclusion, the E-nose combined with machine learning provides a rapid, non-destructive, and effective approach for geographical origin classification of oolong tea. Full article

International air traffic has contributed to the global spread of SARS-CoV-2 and its variants. In early 2023, wastewater-based epidemiology (WBE) has been implemented at airports as a surveillance tool to detect emerging variants at short notice. This study investigates the feasibility and challenges of applying WBE at Berlin Brandenburg (BER) Airport, including a rapid implementation of wastewater sampling and analysis under unprecedented circumstances. For this purpose, aircraft and airport wastewater was sampled over 13 weeks. Established sampling and analysis protocols for municipal wastewater treatment plants (WWTPs) had to be adapted to the specific conditions of the airport environment. SARS-CoV-2 RNA was quantified and sequenced, revealing SARS-CoV-2 mutations not previously observed in clinical surveillance data in Germany. Despite the logistical and methodological challenges, the study demonstrates that WBE can serve as an early warning system for pathogen introduction. However, our study also underscores the need for realistic timelines for the establishment and validation of WBE monitoring strategies in new contexts. Investments in the establishment of WBE systems, e.g., infrastructure, protocols, trained personnel, and a network of stakeholders at strategic nodes including airports, can act as an effective tool for pandemic preparedness and global health security. Full article

The rapid development of digital technologies is opening up new avenues for transforming education, particularly in fields that require practical training, such as electronic circuit design. In this context, this paper presents the development of a multiplayer virtual learning platform that makes use of digital twins technology to offer a realistic, collaborative experience in a simulated environment. Users can interact in real time through synchronized avatars, voice communication, and multiple viewing angles, simulating a physical classroom. Evaluation of the platform with undergraduate students showed positive results in terms of usability, collaboration, and learning effectiveness. Despite the limitations of the sample, the findings reinforce the prospect of virtual laboratories as a modern tool in technical education. Full article