Search Results (981)

Objective. This randomized controlled trial evaluated the efficacy of lavender–neroli oil aromatherapy in managing dental anxiety and pain in children undergoing inferior alveolar nerve block (IANB) anesthesia. Methods. Fifty-four children aged 6–11 years were randomly assigned to either a control group or an aromatherapy group. Children in the control group were asked to wear a regular scented-free nitrous oxide mask. Children in the control group were asked to wear a regular scented-free nitrous oxide mask. Children in the intervention group inhaled lavender–neroli oil via a nitrous oxide nasal mask for 5 min before and during IANB administration. Anxiety and pain levels were assessed pre-and post-treatment using the Facial Image Scale (FIS), Face–Legs–Activity–Cry–Consolability (FLACC) scale, and vital signs (heart rate, blood pressure, oxygen saturation). The collected data were statistically analyzed using SPSS software 20. The Mann–Whitney U test was used for analyzing FIS results, and the independent T test and T Paired test were used for analyzing heart rate, blood pressure, and oxygen saturation results. Results. Results demonstrated significantly lower anxiety, heart rate, blood pressure, and pain scores in the aromatherapy group compared to the control group (p < 0.05), with no significant change in oxygen saturation. Conclusions. Lavender–neroli aromatherapy is a safe, low-cost, and effective adjunct to reduce anxiety and discomfort during pediatric dental anesthesia. Full article

Epidemics caused by respiratory infections have become a global and systemic threat since humankind has become highly connected via modern transportation systems. Any new pathogen with human-to-human transmission capabilities has the potential to cause public health disasters and severe disruptions of social and economic activities. During the COVID-19 pandemic, we learned that proper mask-wearing in closed, restricted areas was one of the measures that worked to mitigate the spread of respiratory infections while allowing for continuing economic activity. Previous research approached this issue by designing hardware–software systems that determine whether individuals in the surveilled restricted area are using a mask; however, most such solutions are centralized, thus requiring massive computational resources, which makes them hard to scale up. To address such issues, this paper proposes a novel decentralized, federated learning (FL) solution to mask-wearing detection that instantiates our lightweight version of the MobileNetV2 model. The FL solution also ensures individual privacy, given that images remain at the local, device level. Importantly, we obtained a mask-wearing training accuracy of 98% (i.e., similar to centralized machine learning solutions) after only eight rounds of communication with 25 clients. We rigorously proved the reliability and robustness of our approach after repeated K-fold cross-validation. Full article

The widespread use of masks in the community was occasioned by the COVID-19 global pandemic. This study examined bacterial contamination on surgical and face masks used in Githurai Market during daily activities, focusing on the sources, accumulation, and antibiotic resistance of bacteria. Sixteen respondents were selected to wear masks, from which bacteria were isolated from the inside and outside surfaces, as well as from swabs of their nose, mouth, and skin. The bacterial load was monitored at intervals of 0 h, 2 h, 4 h, and 6 h using culture-dependent methods. The identified bacteria included Staphylococcus, Klebsiella, Stenotrophomonas, Enterococcus, and Bacillus, amongst others sourced from the users’ mouth, skin, nose, and the environment. Bacterial accumulation increased with time, peaking at 6 h of mask use. Most of the bacteria isolates showed multidrug resistance to commonly used antibiotics including cefotaxime, streptomycin, and amoxicillin. This raises concerns about potential role of masks as reservoirs for pathogenic and antibiotic-resistant bacteria. The study emphasizes the need for better mask hygiene practices to reduce microbial contamination and the risk of spreading antibiotic-resistant bacteria. It also highlights the importance of developing strategies to address these risks and ensure the continued effectiveness of masks as a part of public health measures Full article

Background: Recently, a new type of full-face snorkeling mask (FFSM), called “Easy-breath” masks, has become extremely popular both in adults and children due to their effective marketing and relative comfort. However, these masks are complex engineering systems that, in case of malfunctioning or if used by young children, may readily cause CO₂ rebreathing, especially in young children. Case Presentation: We present three cases of children under six years of age admitted to the emergency department, with two of them due to non-fatal drowning incidents and one following a cardiac arrest induced by drowning. All incidents occurred during brief submersions while using full-face snorkeling masks. Conclusions: When inappropriately used by younger children, full-face snorkeling masks may have a mechanical dead space larger than tidal volume, with a significant increased risk of rebreathing of CO₂ and consequent risk for hypercapnic hypoxia. The hypercapnia may cause dizziness and respiratory distress, while hypoxia may cause confusion. Both may lead to loss of consciousness, which could be a potential cause of drowning, particularly in younger children. Full article

The paper critically reviews face recognition models that are based on deep learning, specifically security and surveillance. Existing systems are susceptible to pose variation, occlusion, low resolution and even aging, even though they perform quite well under controlled conditions. The authors make a systematic review of four state-of-the-art architectures—FaceNet, ArcFace, OpenFace and SFace—through the use of five benchmark datasets, namely LFW, CPLFW, CALFW, AgeDB-30 and QMUL-SurvFace. The measures of performance are evaluated as the area under the receiver operating characteristic (ROC-AUC), accuracy, precision and F1-score. The results reflect that FaceNet and ArcFace achieve the highest accuracy under well-lit and frontal settings; when comparing SFace, this proved to have better robustness to degraded and low-resolution surveillance images. This shows the weaknesses of traditional embedding methods because bigger data sizes reduce the performance of OpenFace with all of the datasets. These results underscore the main point of this study: a comparative study of the models in difficult real life conditions and the observation of the trade-off between generalization and specialization inherent to any models. Specifically, the ArcFace and FaceNet models are optimized to perform well in constrained settings and SFace in the wild ones. This means that the selection of models must be closely monitored with respect to deployment contexts, and future studies should focus on the study of architectures that maintain performance even with fluctuating conditions in the form of the hybrid architectures. Full article

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

Accurate genomic prediction of complex phenotypes is crucial for accelerating genetic progress in swine breeding. However, conventional methods like Genomic Best Linear Unbiased Prediction (GBLUP) face limitations in capturing complex non-additive effects that contribute significantly to phenotypic variation, restricting the potential accuracy of phenotype prediction. To address this challenge, we introduce a novel framework based on a self-supervised, pre-trained encoder-only Transformer model. Its core novelty lies in tokenizing SNP sequences into non-overlapping 6-mers (sequences of 6 SNPs), enabling the model to directly learn local haplotype patterns instead of treating SNPs as independent markers. The model first undergoes self-supervised pre-training on the unlabeled version of the same SNP dataset used for subsequent fine-tuning, learning intrinsic genomic representations through a masked 6-mer prediction task. Subsequently, the pre-trained model is fine-tuned on labeled data to predict phenotypic values for specific economic traits. Experimental validation demonstrates that our proposed model consistently outperforms baseline methods, including GBLUP and a Transformer of the same architecture trained from scratch (without pre-training), in prediction accuracy across key economic traits. This outperformance suggests the model’s capacity to capture non-linear genetic signals missed by linear models. This research contributes not only a new, more accurate methodology for genomic phenotype prediction but also validates the potential of self-supervised learning to decipher complex genomic patterns for direct application in breeding programs. Ultimately, this approach offers a powerful new tool to enhance the rate of genetic gain in swine production by enabling more precise selection based on predicted phenotypes. Full article

Although prior research has examined how facial covering affects observers’ cognition and attitude, the psychological experiences of individuals with facial coverings themselves and their subsequent behavioral consequences still need to be more explored. From the embodied cognition perspective, we propose facial covering as a direct external stimulus, triggering a psychological gap between the current level of self-expression needs and the diminished self-expression pathways. Using face masks as a specific form of facial covering, five experiments were conducted in China. The results reveal that under facial covering, the surfaced need for self-expression can be transformed into the consumer preference for conspicuousness; and the self-construal type moderates this effect, with independent self-construals exhibiting a stronger covering-induced need for self-expression and subsequent conspicuous preferences compared to interdependent self-construals. The research makes a contribution by enriching the new perspective on the theoretical impact of facial covering. Practically, this research can also provide actionable insights for enterprises in the realms of marketing strategy design and behavior interventions. Full article

Large Language Models (LLMs) excel in diverse text generation tasks but still face limited controllability, opaque decision processes, and frequent hallucinations. This paper presents a structural causal intervention framework that models input–hidden–output dependencies through a structural causal model and performs targeted interventions on hidden representations. By combining counterfactual sample construction with contrastive training, our method enables precise control of style, sentiment, and factual consistency while providing explicit causal explanations for output changes. Experiments on three representative tasks demonstrate consistent and substantial improvements: style transfer accuracy reaches 92.3% (+7–14 percentage points over strong baselines), sentiment-controlled generation achieves 90.1% accuracy (+1.3–10.9 points), and multi-attribute conflict rates drop to 3.7% (a 40–60% relative reduction). Our method also improves causal attribution scores to 0.83–0.85 and human agreement rates to 87–88%, while reducing training and inference latency by 25–30% through sparse masking that modifies ≤10% of hidden units per attribute. These results confirm that integrating structural causal intervention with counterfactual training advances controllability, interpretability, and efficiency in LLM-based generation, offering a robust foundation for deployment in reliability-critical and resource-constrained applications. Full article

This paper presents an autonomous mobile service robot designed by the Conscious Technologies research group at Tecnologico de Monterrey, integrating advanced navigation and real-time surveillance capabilities. The primary objective was to evaluate the robot’s performance in both navigation and vision tasks within dynamic environments. The research compared two navigation algorithms: the Navstack package, and a State Feedback Controller (SFC). While the Navstack algorithm excelled in obstacle avoidance, it showed variability in location precision and trajectory repeatability. Conversely, the SFC demonstrated superior precision and repeatability, but lacked obstacle-detection capabilities. Furthermore, the vision system’s efficacy in face mask detection and social distancing compliance was assessed under varying robot and human speeds. An ANOVA analysis revealed the significant impact of these dynamic variables on the performance of the vision model. The integration of robust navigation algorithms with intelligent surveillance methods highlights the potential for real-world applications in protocol enforcement and autonomous navigation in semi-structured environments. Full article

The lane graph is critical for applications such as autonomous driving and lane-level route planning. While previous research has focused on extracting lane-level graphs from aerial imagery using convolutional neural networks (CNNs) followed by post-processing segmentation-to-graph algorithms, these methods often face challenges in producing sharp and complete segmentation masks. Challenges such as occlusions, variations in lighting, and changes in road texture can lead to incomplete and inaccurate lane masks, resulting in poor-quality lane graphs. To address these challenges, we propose a novel approach that refines the lane masks, output by a CNN, using diffusion models. Experimental results on a publicly available dataset demonstrate that our method outperforms existing methods based solely on CNNs or diffusion models, particularly in terms of graph connectivity. Our lane mask refinement approach enhances the quality of the extracted lane graph, yielding gains of approximately 1.5% in GEO F1 and 3.5% in TOPO F1 scores over the best-performing CNN-based method, and improvements of 28% and 34%, respectively, compared to a prior diffusion-based approach. Both GEO F1 and TOPO F1 scores are critical metrics for evaluating lane graph quality. Additionally, ablation studies are conducted to evaluate the individual components of our approach, providing insights into their respective contributions and effectiveness. Full article

Cross-modal semantic segmentation of individual tree LiDAR point clouds is critical for accurately characterizing tree attributes, quantifying ecological interactions, and estimating carbon storage. However, in forest environments, this task faces key challenges such as high annotation costs and poor cross-domain generalization. To address these issues, this study proposes a cross-modal semantic transfer framework tailored for individual tree point cloud segmentation in forested scenes. Leveraging co-registered UAV-acquired RGB imagery and LiDAR data, we construct a technical pipeline of “2D semantic inference—3D spatial mapping—cross-modal fusion” to enable annotation-free semantic parsing of 3D individual trees. Specifically, we first introduce a novel Multi-Source Feature Fusion Network (MSFFNet) to achieve accurate instance-level segmentation of individual trees in the 2D image domain. Subsequently, we develop a hierarchical two-stage registration strategy to effectively align dense matched point clouds (MPC) generated from UAV imagery with LiDAR point clouds. On this basis, we propose a probabilistic cross-modal semantic transfer model that builds a semantic probability field through multi-view projection and the expectation–maximization algorithm. By integrating geometric features and semantic confidence, the model establishes semantic correspondences between 2D pixels and 3D points, thereby achieving spatially consistent semantic label mapping. This facilitates the transfer of semantic annotations from the 2D image domain to the 3D point cloud domain. The proposed method is evaluated on two forest datasets. The results demonstrate that the proposed individual tree instance segmentation approach achieves the highest performance, with an IoU of 87.60%, compared to state-of-the-art methods such as Mask R-CNN, SOLOV2, and Mask2Former. Furthermore, the cross-modal semantic label transfer framework significantly outperforms existing mainstream methods in individual tree point cloud semantic segmentation across complex forest scenarios. Full article

Target-oriented opinion words extraction (TOWE) is a novel subtask of aspect-based sentiment analysis (ABSA), which aims to extract opinion words corresponding to a given opinion target within a sentence. In recent years, neural networks have been widely used to solve this problem and have achieved competitive results. However, when faced with complex and long sentences, the existing methods struggle to accurately identify the semantic relationships between distant opinion targets and opinion words. This is primarily because they rely on literal distance, rather than semantic distance, to model the local context or opinion span of the opinion target. To address this issue, we propose a neural network model called DTOWE, which comprises (1) a global module using Inward-LSTM and Outward-LSTM to capture general sentence-level context, and (2) a local module that employs BiLSTM combined with DT-LCF to focus on target-specific opinion spans. DT-LCF is implemented in two ways: DT-LCF-Mask, which uses a binary mask to zero out non-local context beyond a dependency tree distance threshold, α, and DT-LCF-weight, which applies a dynamic weighted decay to downweigh distant context based on semantic distance. These mechanisms leverage dependency tree structures to measure semantic proximity, reducing the impact of irrelevant words and enhancing the accuracy of opinion span detection. Extensive experiments on four benchmark datasets demonstrate that DTOWE outperforms state-of-the-art models. Specifically, DT-LCF-Weight achieves F1-scores of 73.62% (14lap), 82.24% (14res), 75.35% (15res), and 83.83% (16res), with improvements of 2.63% to 3.44% over the previous state-of-the-art (SOTA) model, IOG. Ablation studies confirm that the dependency tree-based distance measurement and DT-LCF mechanism are critical to the model’s effectiveness, validating their ability to handle complex sentences and capture semantic dependencies between targets and opinion words. Full article

In this study, non-isothermal pyrolysis of a mixture of disposable surgical face masks (FMs) and nitrile gloves (NGs) was conducted, using a heating rate of 100 °C/min, N₂ flowrate of 100 mL/min, and temperatures between 500 and 800 °C. Condensable product yield peaked at 600 °C (76.9 wt.%), with gas yields rising to 31.0 wt.%, at 800 °C. GC-MS of the condensable product confirmed the presence of aliphatic compounds (>90%), while hydrogen, methane, and ethylene dominated the gas composition. At 600 °C, gasoline (C₄ to C₁₂)-, diesel (C₁₃ to C₂₀)-, motor oil (C₂₁ to C₃₅)-, and heavy hydrocarbon (C₃₅₊)-range compounds accounted for 23.7, 46.7, 12.5, and 17.1%, of the condensable product, respectively. Using model-free methods, the average activation energy and pre-exponential factor were found to be 309.7 ± 2.4 kJ/mol and 2.5 ± 3.4 × 10²⁵ s⁻¹, respectively, while a 2-dimensional diffusion mechanism was determined. Scale-up runs confirmed high yields of condensable product (60–70%), with comparable composition to that obtained from lab-scale tests. The pyrolysis oil exceeds acceptable oxygen, nitrogen, chlorine, and fluorine levels for industrial steam crackers—needing pre-treatment—while other contaminants like sulphur and metals could be managed through mild blending. In summary, this work offers a sustainable approach to address the environmental concerns surrounding disposable FMs and NGs. Full article

Accurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical safety applications. To address the dual challenges of prediction accuracy and model interpretability, a multi-agent reinforced symbolic regression (MA-RSR) framework is proposed by integrating multi-agent reinforcement learning with symbolic regression (SR) techniques. Specifically, MA-RSR employs a collaborative mechanism that decomposes complex mathematical expressions into parallel components constructed by independent agents, effectively addressing the search space explosion problem in traditional SR. The system incorporates Transformer-based architecture to enhance symbolic selection capabilities, while an intelligent masking mechanism ensures mathematical rationality through multi-level constraints. To demonstrate effectiveness of the proposed method, validation is conducted using SAE4340 steel multiaxial fatigue data and landing gear finite element simulation. The MA-RSR framework successfully discovers two mathematical expressions achieving R² of 0.96. Compared to traditional empirical formulas, MA-RSR achieves prediction accuracy improvements exceeding 50% while providing complete interpretability that machine learning methods lack. Furthermore, the multi-agent collaborative mechanism significantly enhances search efficiency through parallel expression construction compared to existing symbolic regression approaches. Full article