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BackgroundPoor oral health has been a persistent problem in nursing home residents for decades, with severe consequences for residents and the health care system. Two major barriers to providing appropriate oral care are residents’ responsive behaviors to oral care and residents’ lack of ability or motivation to perform oral care on their own.ObjectivesTo evaluate the effectiveness of strategies that nursing home care providers can apply to either prevent/overcome residents’ responsive behaviors to oral care, or enable/motivate residents to perform their own oral care.Materials and methodsWe searched the databases Medline, EMBASE, Evidence Based Reviews–Cochrane Central Register of Controlled Trials, CINAHL, and Web of Science for intervention studies assessing the effectiveness of eligible strategies. Two reviewers independently (a) screened titles, abstracts and retrieved full-texts; (b) searched key journal contents, key author publications, and reference lists of all included studies; and (c) assessed methodological quality of included studies. Discrepancies at any stage were resolved by consensus. We conducted a narrative synthesis of study results.ResultsWe included three one-group pre-test, post-test studies, and one cross-sectional study. Methodological quality was low (n = 3) and low moderate (n = 1). Two studies assessed strategies to enable/motivate nursing home residents to perform their own oral care, and to studies assessed strategies to prevent or overcome responsive behaviors to oral care. All studies reported improvements of at least some of the outcomes measured, but interpretation is limited due to methodological problems.ConclusionsPotentially promising strategies are available that nursing home care providers can apply to prevent/overcome residents’ responsive behaviors to oral care or to enable/motivate residents to perform their own oral care. However, studies assessing these strategies have a high risk for bias. To overcome oral health problems in nursing homes, care providers will need practical strategies whose effectiveness was assessed in robust studies.

Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning-based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.

To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in panoramic images. Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded, and considerably minimize the rates of discovering the extra boxes. The proposed model is tested on both the testing dataset and the actual images and gets 94.5% of the correct signs recognition rate, the classification rate of those signs discovered was 99.41% and the rate of false signs was only around 0.11.

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