Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input.

The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users' SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series of dialogues and modify with time. To address these challenges, our study introduces a novel, modified rule-based Natural Language Processing (MR-NLP) model augmented with human input. This reproducible approach capitalizes on human-derived insights to establish a lexicon of critical keywords and to formulate rules for the iterative refinement of the NLP model. English speakers, aged 50 or older and residing alone, were enlisted to engage with Amazon Alexa™ via predefined daily routines for a minimum of 30 min daily spanning three months (N = 35, mean age = 77). We amassed time-stamped, textual data comprising participants' user commands and responses from Alexa™. Initially, a subset constituting 20% of the data (1,020 instances) underwent manual coding by human coder, predicated on keywords and commands. Separately, a rule-based Natural Language Processing (NLP) methodology was employed to code the identical subset. Discrepancies arising between human coder and the NLP model programmer were deliberated upon and reconciled to refine the rule-based NLP coding framework for the entire dataset. The modified rule-based NLP approach demonstrated notable enhancements in efficiency and scalability and reduced susceptibility to inadvertent errors in comparison to manual coding. Furthermore, human input was instrumental in augmenting the NLP model, yielding insights germane to the aging adult demographic, such as recurring speech patterns or ambiguities. By disseminating this innovative software solution to the scientific community, we endeavor to advance research and innovation in NLP model formulation, subsequently contributing to the understanding of older people's interactions with SVA and other AI-powered systems.

Reducing Loneliness and Improving Social Support among Older Adults through Different Modalities of Personal Voice Assistants.

This study examines the potential of AI-powered personal voice assistants (PVAs) in reducing loneliness and increasing social support among older adults. With the aging population rapidly expanding, innovative solutions are essential. Prior research has indicated the effectiveness of various interactive communication technologies (ICTs) in mitigating loneliness, but studies focusing on PVAs, particularly considering their modality (audio vs. video), are limited. This research aims to fill this gap by evaluating how voice assistants, in both audio and video formats, influence perceived loneliness and social support. This study examined the impact of voice assistant technology (VAT) interventions, both audio-based (A-VAT) and video-based (V-VAT), on perceived loneliness and social support among 34 older adults living alone. Over three months, participants engaged with Amazon Alexa™ PVA through daily routines for at least 30 min. Using a hybrid natural language processing framework, interactions were analyzed. The results showed reductions in loneliness (Z = -2.99, p < 0.01; pre-study loneliness mean = 1.85, SD = 0.61; post-study loneliness mean = 1.65, SD = 0.57), increases in social support post intervention (Z = -2.23, p < 0.05; pre-study social support mean = 5.44, SD = 1.05; post-study loneliness mean = 5.65, SD = 1.20), and a correlation between increased social support and loneliness reduction when the two conditions are combined (ρ = -0.39, p < 0.05). In addition, V-VAT was more effective than A-VAT in reducing loneliness (U = 85.50, p < 0.05) and increasing social support (U = 95, p < 0.05). However, no significant correlation between changes in perceived social support and changes in perceived loneliness was observed in either intervention condition (V-VAT condition: ρ = -0.24, p = 0.37; A-VAT condition: ρ = -0.46, p = 0.06). This study's findings could significantly contribute to developing targeted interventions for improving the well-being of aging adults, addressing a critical global issue.

Transport and deposition of solid phosphorus-based mineral particles in urine diversion systems.

The deposition of solid phosphorus-based mineral particles is a common problem in urine diversion systems, which occurs in transport systems, particularly in horizontal pipelines. In this work, particle deposition behaviour in turbulent flow in a 3D horizontal pipe was simulated by using the Euler-Lagrange method. The effects of particle diameter, particle density, particle shape factor and fluid flow velocity on particle deposition behaviour were investigated. The results showed that the deposition rate increased by 9.92%,6.88% and 6.88% with increasing particle diameter (10-90 µm), particle density (1400 kg/m3-2300 kg/m3), and particle shape factor (0.2-1), respectively. For particles with larger diameters (>90 µm) or larger density (>2300 kg/m3), the deposition rate of these particles was almost reached 100%. It was found that gravitational sedimentation was the dominant deposition mechanism in low fluid flow velocity range (0.1-0.5 m/s). As fluid flow velocity increased (>0.5 m/s), turbulent fluctuation became the dominant factor that affected particle motion behaviour, whereas the effect of gravitational sedimentation on particle deposition behaviour declined significantly, and the increase in fluid flow velocity no longer significantly affects deposition rate. It was found that the deposition rate decreased by 29.13% as the fluid flow velocity was increased from 0.1 m/s to 0.5 m/s, while the corresponding deposition rate only decreased by 14.24% when the fluid flow velocity was increased from 0.5 m/s to 2 m/s. The optimal flow velocity was found to range between 0.75 and 1.25 m/s, which may mitigate the deposition of mineral solids in urine diversion systems.

From Newtonian to non-Newtonian fluid: Insight into the impact of rheological characteristics on mineral deposition in urine collection and transportation.

The deposition of phosphorus-based mineral solids in urine diversion systems has been one of the main challenges for the large-scale practical applications of urine source separation. Accurate rheological characterization of urine slurry is of high importance for its practical flow performance. The rheological data of urine slurry was obtained using a narrow gap rotating rheometer. Based on current pipe flow theories and the obtained rheological data of urine slurry, the transition velocity was determined. The impacts of solid concentration and temperature on the rheological behavior of urine slurry were investigated in this study. Urine slurry behaved as a Newtonian fluid at low solid concentration. By contrast, urine slurry changed from Newtonian to non-Newtonian fluid with the increase in solid concentration, demonstrating a shearing thinning behavior and yielding stress fluid. The impact of temperature on the apparent viscosity of the urine slurry was described using an Arrhenius-type function. Moreover, the impact of solid concentration and temperature on the transition velocity was quantified, which indicated that the non-Newtonian behavior of the urine slurry in the compression settling region has a significant impact on the pipe flow behavior, leading to the formation of a compressed layer on the bottom of the pipe. The targeting understanding of transition velocity is particularly useful for the practical design and optimization of urine piping system, especially on how to mitigate pipe blockages. Based on the evaluation of different piping systems, this work proposed several potential urine collection and transportation modes.

Enhanced antimicrobial activity of ZnO nanofluids in sonophotocatalysis and its mechanism.

This study investigated the inactivation efficiency of ZnO nanofluids against E. coli in sonophotocatalysis with the aeration of nitrogen, oxygen, argon and their mixtures. The results showed that inactivation efficiency was increased when aeration was combined with sonophotocatalysis. Addition of different types of gases could lead to the different inactivation efficiency. The inactivation efficiencies were shown in the following order: no aeration < nitrogen < argon < oxygen < Ar/O2(3:7) < Ar/O2(7:3) < Ar/O2(5:5). The production of hydroxyl radicals was explored to understand the inactivation mechanism. Compared with sonophotocatalysis without aeration, more hydroxyl radicals were produced in sonophotocatalysis with aeration, which could lead to changes of cellular substances. Furthermore, characterization of E. coli cells using Raman spectroscopy and FTIR illustrated that sonophotocalysis could affect the cellular substances containing carbohydrates, proteins and P containing molecules. Results suggested that the enhanced antimicrobial activity with aeration was originated from stronger cavitational activity, together with the formation of hydroxyl radicals. Compared to sonophotocatalysis without aeration, more dissolved oxygen was existed in sonophotocatalysis with aeration, which could enhance the formation of hydroxyl radicals.

Sonophotocatalytic inactivation of E. coli using ZnO nanofluids and its mechanism.

The present study evaluated inactivation efficiency of a sonophotocatalytic process using ZnO nanofluids including ultrasonic parameters such as power density, frequency and time. The result showed that inactivation efficiency was increased by 20% when ultrasonic irradiation was combined with photocatalytic process in the presence of natural light. Comparison of inactivation efficiency in photocatalytic, ultrasonic and sonocatalytic processes using Escherichia coli as a model bacteria identified that inactivation efficiencies are shown in the following order: ultrasonic irradiation