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Experimental and GEP-based evaluation of compressive strength in eco-friendly mortars with waste foundry sand and varying cement grades
Reservoir host community and vector density predict human tick-borne diseases across the Eastern United States
Relationship between body positivity and body neutrality with body image, self-esteem, mindfulness and gratitude
Abstract Despite the growing popularity of body positivity and body neutrality on social media, their scientific differentiation remains unclear. Body positivity involves acceptance and a positive attitude toward one’s body regardless of societal ideals, whereas body neutrality emphasizes a non-judgmental focus on body functionality. This study aimed to examine the relationships between body positivity and body neutrality, self-esteem, body image, mindfulness, and gratitude. A total of 201 adults (56.7% women; M = 28.11; SD = 11.84) completed the Rosenberg Self-Esteem Scale, Body Evaluation Scale, Five Facet Mindfulness Questionnaire: Short-Form, Gratitude Questionnaire and a survey measuring the self-reported level of body positivity and body neutrality. Body positivity and body neutrality were positively correlated with all psychological variables ( p < 0.05). Body positivity was predicted by self-esteem and body image ( R² = 0.41), whereas body neutrality was predicted by self-esteem, gratitude, and mindfulness ( R² = 0.30). The high and low groups differed significantly in all variables except for the Observing facet of mindfulness. The value of the shared variance coefficient between body positivity and neutrality ( ρ² = 0.23) indicated the presence of distinct constructs. The findings support the distinctiveness of body positivity and body neutrality, and highlight their relevance to psychological well-being.
Decoding the Water Harvesting Mechanism of MIL-100(Fe) Across Short- and Long-Range Length Scales
Energy-efficient transmit antenna selection with Fast-ABC-Boost
Bilateral paravertebral block reduces complications after pancreatectomy in retrospective cohort analysis
Unfolding North American spring weather extremes along a scale ladder
Electrical impedance phase variation in relation to articulation manner
CBCT based three dimensional odontometric mapping of maxillary anterior teeth in an Indian cohort to guide precision endodontic access cavity preparation
Anaerobic digestion of gliricidia sepium co-digested with pig manure using automated and portable digester
COX-2/PGE₂ signaling drives Toxocara canis-induced lung pathology by enhancing plasminogen activator activity and collagen VI degradation
Automatic diagnosis of heating in oil-filled terminals of cables
Abstract A new automatic diagnosis technique was proposed to address the problem of cable oil-filled terminal heating. The infrared and visible light images collected by DJI drones were registered using the scale-invariant feature transform (SIFT) algorithm. The progressive infrared and visible image fusion network based on the illumination aware (PIA Fusion) network was used to fuse the infrared and visible light images. The fused images were used to train the version 5 of You Only Look Once (YOLOv5) network for object detection. The areas prone to heating were identified and mapped to the infrared images before fusion, and the mapped areas of the infrared images were cropped. The cropped images were fed into the DJI infrared analysis software toolkit Thermal Software Development Kit (TSDK) to obtain the temperature information, and diagnosis was performed according to the relevant standards. The infrared images and fused images were separately used for training. The experimental results showed that the mean average precision calculated when the intersection over union (IoU) threshold was 0.5 (mAP@0.5) was 95.3% when training with fused images and the average detection time was 12 ms per image. This technique could replace traditional manual diagnosis to improve detection efficiency and precision.