High-sensitivity and high extinction ratio fiber strain sensor with temperature insensitivity by cascaded MZI and FPI.

Low extinction ratio (ER) and high temperature cross-sensitivity are serious but common problems for most strain sensors based on Vernier effect. In this study, a hybrid cascade strain sensor of a Mach-Zehnder interferometer (MZI) and a Fabry-Perot interferometer (FPI) with high sensitivity and high ER based on Vernier effect is proposed. The two interferometers are separated by a long single-mode fiber (SMF). The MZI is used as the reference arm, which can be flexibly embedded in the SMF. The FPI is used as the sensing arm and the hollow-core fiber (HCF) as the FP cavity to reduce optical loss. Simulation and experiments have proven that this method can significantly increase ER. At the same time, the second reflective face of the FP cavity is indirectly spliced to increase the active length to improve the strain sensitivity. Through the amplification of Vernier effect, the maximum strain sensitivity is -649.18p m/µ ε, and the temperature sensitivity is only 5.76pm/∘C. The magnetic field was measured by combining the sensor with a Terfenol-D (magneto-strictive material) slab to verify the strain performance, and the magnetic field sensitivity is -7.53nm/mT. The sensor has many advantages and has potential applications in the field of strain sensing.

Registered nurses' perceptions and experience of working in aged care: A qualitative approach.

AIM: To understand and report on the perceptions and experiences of registered nurses in the aged care sector. DESIGN: An exploratory qualitative study. METHODS: Semi-structured telephone interviews were utilised as the primary data collection method. Fifteen registered nurses were interviewed. All interviews were recorded, transcribed verbatim and analysed using conventional content analysis. Participants were quoted verbatim to ensure authenticity. RESULTS: The results indicated a demand for increased administrative and staffing support in the aged care workplace. Poor morale and unethical practices contributed to negative perceptions and attitudes among nurses towards aged care. Managing and communicating with older people was reported as challenging, which impacts nursing staff recruitment and retention. Future work is needed to ensure that outstanding clinical role models and leadership support nursing staff recruitment and retention. Incorporating aged care content into the nursing curriculum and providing professional development opportunities to aged care professionals would be the foundation towards solutions, as the study primarily explored nurses' perspectives.

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.

Emotional and mood disturbances are common in people with dementia. Non-pharmacological interventions are beneficial for managing these disturbances. However, effectively applying these interventions, particularly in the person-centred approach, is a complex and knowledge-intensive task. Healthcare professionals need the assistance of tools to obtain all relevant information that is often buried in a vast amount of clinical data to form a holistic understanding of the person for successfully applying non-pharmacological interventions. A machine-readable knowledge model, e.g., ontology, can codify the research evidence to underpin these tools. For the first time, this study aims to develop an ontology entitled Dementia-Related Emotional And Mood Disturbance Non-Pharmacological Treatment Ontology (DREAMDNPTO). DREAMDNPTO consists of 1258 unique classes (concepts) and 70 object properties that represent relationships between these classes. It meets the requirements and quality standards for biomedical ontology. As DREAMDNPTO provides a computerisable semantic representation of knowledge specific to non-pharmacological treatment for emotional and mood disturbances in dementia, it will facilitate the application of machine learning to this particular and important health domain of emotional and mood disturbance management for people with dementia.

Malnutrition and its contributing factors for older people living in residential aged care facilities: Insights from natural language processing of aged care records.

BACKGROUND: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed. OBJECTIVE: This study explored the risk factors for malnutrition in structured and unstructured electronic health data. METHODS: Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to identify causative factors for malnutrition. Natural language processing (NLP) techniques were applied to progress notes to extract these causative factors. The NLP performance was evaluated by the parameters of sensitivity, specificity and F1-Score. RESULTS: The NLP methods were highly accurate in extracting the key data, values for 46 causative variables, from the free-text client progress notes. Thirty three percent (1,469 out of 4,405) of the clients were malnourished. The structured, tabulated data only recorded 48% of these malnourished clients, far less than that (82%) identified from the progress notes, suggesting the importance of using NLP technology to uncover the information from nursing notes to fully understand the health status of the vulnerable older people in residential aged care. CONCLUSION: This study identified 33% of older people suffered from malnutrition, lower than those reported in the similar setting in previous studies. Our study demonstrates that NLP technology is important for uncovering the key information about health risks for older people in residential aged care. Future research can apply NLP to predict other health risks for older people in this setting.