Using machine learning to explore the characteristics of eye movement patterns and relationship with cognition ability of Chinese children aged 1–6 years

Researchers have begun to investigate the relationship between eye movement characteristics of gaze patterns and cognitive abilities, and have attempted to use eye-tracking technology as a new method to evaluate cognitive abilities. Traditional eye movement analysis methods typically separate spatial and temporal information of eye movements, mostly analyze averaged data, and consider individual differences as noise. In addition, current eye movement studies on gaze patterns mostly involve adults, while research on infants and toddlers is limited with small sample sizes and narrow age ranges. It is still unknown whether the conclusions drawn from adult-based research can be applied to children. Consequently, eye movement research on gaze patterns in children is necessary. To address the concerns stated above, this study used the Hidden Markov machine learning method to model gaze patterns of 330 children aged 1–6 years while observing faces freely, and analyzed characteristics of eye movement gaze patterns. Additionally, we analyzed the correlation between gaze patterns of 31 toddlers aged 1–3 years and 37 preschoolers aged 4–6 years, and the different dimensions of cognitive abilities. The findings indicated that children exhibited holistic and analytic gaze patterns while observing different faces freely. More children adopted a holistic gaze pattern, and there were age-specific gaze pattern characteristics and regularities. Gaze patterns of toddlers may be correlated with their adaptive abilities and gaze patterns of preschoolers may be correlated with their visual space abilities. Specifically, toddlers aged 1–3 years showed a moderate negative correlation between the H-A scale and the adaptive dimension, while preschoolers aged 4–6 years showed a low negative correlation between the H-A scale and the visual space dimension. This study may provide new insights into the characteristics of children’s eye-movement gaze patterns during face observation, and potentially offer objective evidence for future research aimed at promoting the use of eye-tracking technology in the assessment of toddlers’ adaptive abilities and preschoolers’ visual space abilities in the field of face perception

Bearing Fault Diagnosis for Time-Varying System Using Vibration–Speed Fusion Network Based on Self-Attention and Sparse Feature Extraction

Nowadays, most deep-learning-based bearing fault diagnosis methods are studied under the condition of steady speed, while the performance of these models cannot be fully played under time-varying conditions. Therefore, in order to facilitate the practical application of a deep learning model in bearing fault diagnosis, a vibration–speed fusion network is proposed, which utilizes a transformer with a self-attention module to extract vibration features and utilizes a sparse autoencoder (SAE) network to extract sparse features from speed pulse signal. The vibration–speed fusion network enables the efficient fusion of different signals in a high-dimensional vector space with a high degree of model interpretability, without additional signal processing steps. After tuning the hyperparameters of the network, the key segments of the bearing’s time-domain vibration signals can be optimally extracted, the network performance is much better than traditional deep learning methods, and the classification accuracy can reach 95.18% and 99.85% on the two public bearing datasets from the Xi’an Jiaotong University and the University of Ottawa

Bearing Fault Diagnosis for Time-Varying System Using Vibration–Speed Fusion Network Based on Self-Attention and Sparse Feature Extraction

Nowadays, most deep-learning-based bearing fault diagnosis methods are studied under the condition of steady speed, while the performance of these models cannot be fully played under time-varying conditions. Therefore, in order to facilitate the practical application of a deep learning model in bearing fault diagnosis, a vibration–speed fusion network is proposed, which utilizes a transformer with a self-attention module to extract vibration features and utilizes a sparse autoencoder (SAE) network to extract sparse features from speed pulse signal. The vibration–speed fusion network enables the efficient fusion of different signals in a high-dimensional vector space with a high degree of model interpretability, without additional signal processing steps. After tuning the hyperparameters of the network, the key segments of the bearing’s time-domain vibration signals can be optimally extracted, the network performance is much better than traditional deep learning methods, and the classification accuracy can reach 95.18% and 99.85% on the two public bearing datasets from the Xi’an Jiaotong University and the University of Ottawa

Two C–O Bond Formations on a Carbenic Carbon: Palladium-Catalyzed Coupling of <i>N</i>‑Tosylhydrazones and Benzo-1,2-quinones To Construct Benzodioxoles

A novel and efficient method for the formation of two C–O bonds on a carbenic carbon is reported. This palladium-catalyzed coupling of <i>N</i>-tosylhydrazones and benzo-1,2-quinones were involved the process of carbonyl ylides generation, aromatization, and intramolecular nucleophilic addition, delivering various useful benzodioxoles in high yields

Potentiating the heat inactivation of Escherichia coli O157:H7 in ground beef patties by natural antimicrobials

Escherichia coli O157: H7 (EHEC) is a major foodborne pathogen largely transmitted to humans through the consumption of undercooked ground beef. This study investigated the efficacy of two food-grade, plant-derived antimicrobials, namely rutin (RT), and resveratrol (RV) with or without chitosan (CH) in enhancing EHEC inactivation in undercooked hamburger patties. Further, the effect of aforementioned treatments on beef color and lipid oxidation was analyzed. Additionally, the deleterious effects of these antimicrobial treatments on EHEC was determined using scanning electron microscopy (SEM).Ground beef was inoculated with a five-strain mixture of EHEC (7.0 log CFU/g), followed by the addition of RT (0.05%, 0.1% w/w) or RV (0.1, 0.2% w/w) with or without CH (0.01% w/w). The meat was formed into patties (25 g) and stored at 4°C for 5 days. On days 1, 3 and 5, the patties were cooked (65°C, medium rare) and surviving EHEC was enumerated. The effect of these treatments on meat color and lipid oxidation during storage was also determined as per American Meat Science Association guidelines. The study was repeated three times with duplicate samples of each treatment.Both RT and RV enhanced the thermal destruction of EHEC, and reduced the pathogen load by at least 3 log CFU/g compared to control (P 0.05). Moreover, patties treated with RV plus chitosan were more color stable with higher a* values (P < 0.05). SEM results revealed that heat treatment with antimicrobials (CH+RV 0.2%) caused a complete destruction of EHEC cells with pore formation and extrusion of intracellular contents. Results suggest that the aforementioned antimicrobials could be used for enhancing the thermal inactivation of EHEC in undercooked patties; however, detailed sensory studies are warranted

Alendronate-Conjugated Amphiphilic Hyperbranched Polymer Based on Boltorn H40 and Poly(ethylene glycol) for Bone-Targeted Drug Delivery

A novel type of alendronate­(ALE)-conjugated amphiphilic hyperbranched copolymer based on a hydrophobic hyperbranched Boltorn H40 (H40) core with ALE targeting moiety and many hydrophilic poly­(ethylene glycol) (PEG) arms was synthesized as a carrier for bone-targeted drug delivery. The star copolymer H40-<i>star</i>-PEG/ALE was characterized using nuclear magnetic resonance (NMR), Fourier transformed infrared spectroscopy (FTIR), and gel permeation chromatography (GPC) analysis. Benefiting from its highly branched structure, H40-<i>star</i>-PEG/ALE could form micelles in aqueous solution, which was confirmed by transmission electron microscopy (TEM) and dynamic light scattering (DLS) techniques. The cytotoxicity and hemolysis of the H40-<i>star</i>-PEG/ALE micelles were evaluated via methylthiazoletetrazolium (MTT) assay against NIH/3T3 normal cells and red blood cell (RBC) lysis assay, respectively. As a model anticancer drug, doxorubicin (DOX) was encapsulated into the H40-<i>star</i>-PEG/ALE micelles. The anticancer activity of DOX-loaded micelles was evaluated by MTT assay against an HN-6 human head and neck carcinoma cell line. The strong affinity of H40-<i>star</i>-PEG/ALE micelles to bone was confirmed by the hydroxyapatite (HA) binding assay. These results indicate that the H40-<i>star</i>-PEG/ALE micelles are highly promising bone-targeted drug carriers for skeletal metastases