Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems.

Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.

Background: Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care.

Durability of immunity and clinical protection in nursing home residents following bivalent SARS-CoV-2 vaccination.

Background: Bivalent SARS-CoV-2 vaccines were developed to counter increasing susceptibility to emerging SARS-CoV-2 variants. We evaluated the durability of immunity and protection following first bivalent vaccination among nursing home residents.

Methods: We evaluated anti-spike and neutralization titers from blood in 653 community nursing home residents before and after each monovalent booster, and a bivalent vaccine.

Infertility and Impaired Fecundity in Women and Men in the United States, 2015-2019.

Objectives-Using National Survey of Family Growth data from 2015-2019, this report presents updated national estimates of infertility in U.S. women and men and estimates of impaired fecundity (physical ability to have children) in U.

Electrocardiographic predictors of response to sacubitril/valsartan therapy in heart failure with reduced ejection fraction.

Background: Sacubitril/valsartan (SV) is currently recommended as a first-line therapy in patients with heart failure and reduced ejection fraction (HFrEF) due to its significant clinical and prognostic benefit; however, not all patients respond to therapy and predictors of clinical response to SV remain under-studied.

Aims: To identify electrocardiographic (ECG) predictors of response to SV therapy in HFrEF patients.

Methods: A retrospective analysis of a hospital heart failure registry was undertaken.

Broad immunogenicity to prior SARS-CoV-2 strains and JN.1 variant elicited by XBB.1.5 vaccination in nursing home residents.

Background: SARS-CoV-2 vaccination has reduced hospitalization and mortality for nursing home residents (NHRs). However, emerging variants coupled with waning immunity, immunosenescence, and variability of vaccine efficacy undermine vaccine effectiveness. We therefore need to update our understanding of the immunogenicity of the most recent XBB.

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study.

Background: Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices.

Data augmentation for Human Activity Recognition with Generative Adversarial Networks.

Currently, Human Activity Recognition (HAR) applications need a large volume of data to be able to generalize to new users and environments. However, the availability of labeled data is usually limited and the process of recording new data is costly and time-consuming. Synthetically increasing datasets using Generative Adversarial Networks (GANs) has been proposed, outperforming cropping, time-warping, and jittering techniques on raw signals.

Designing technology to support greater participation of people living with dementia in daily and meaningful activities.

Background: People living with dementia should be at the center of decision-making regarding their plans and goals for daily living and meaningful activities that help promote health and mental well-being. The human-computer interaction community has recently begun to recognize the need to design technologies where the person living with dementia is an active rather than a passive user of technology in the management of their care.

Methods: Data collection comprised semi-structured interviews and focus groups held with dyads of people with early-stage dementia (n = 5) and their informal carers (n = 4), as well as health professionals (n = 5).

Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings.

A nurse-led approach to urgent results management at Sydney Sexual Health Centre demonstrates benefits to client outcomes and cost savings: a time efficiency and health system cost analysis.

Background: The timely provision of test results to facilitate early access to treatment is an essential component of sexually transmissible infection (STI) control and contributes to a significant proportion of the workload at sexual health services. We aimed to estimate the time to deliver client results and treatment as well as the health system costs of the nurse-led urgent results management model at the Sydney Sexual Health Centre (SSHC) compared to an alternative 'ordering clinician' model.

Methods: We conducted a retrospective analysis of urgent results managed by the results nurse over 2weeks in 2019 and an observational study over 2weeks in 2021, where 10 clinicians managed five of their own urgent results.

Data Mining and Fusion Framework for In-Home Monitoring Applications.

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework.

Fine-scale environmentally associated spatial structure of lumpfish () across the Northwest Atlantic.

Lumpfish, , have historically been harvested throughout Atlantic Canada and are increasingly in demand as a solution to controlling sea lice in Atlantic salmon farms-a process which involves both the domestication and the transfer of lumpfish between geographic regions. At present, little is known regarding population structure and diversity of wild lumpfish in Atlantic Canada, limiting attempts to assess the potential impacts of escaped lumpfish individuals from salmon pens on currently at-risk wild populations. Here, we characterize the spatial population structure and genomic-environmental associations of wild populations of lumpfish throughout the Northwest Atlantic using both 70K SNP array data and whole-genome re-sequencing data (WGS).

Clinical outcomes of isolation from neonates, England, 2015-2021: a retrospective case-control study.

Objective: , a coagulase-negative staphylococci (CoNS) species, has been increasingly detected from UK sterile site samples and has caused neonatal unit outbreaks worldwide. We compared survival to discharge and 30-day mortality for the detection of versus other CoNS species.

Methods: In this retrospective case-control study, we included hospitalised infants with any CoNS species detected from a normally sterile body site up to 90 days of age.

Durability of immunity and clinical protection in nursing home residents following bivalent SARS-CoV-2 vaccination.

Background: Vaccines have substantially mitigated the disproportional impact of SARS-CoV-2 on the high morbidity and mortality experienced by nursing home residents. However, variation in vaccine efficacy, immune senescence and waning immunity all undermine vaccine effectiveness over time. The introduction of the bivalent vaccine in September 2022 aimed to counter this increasing susceptibility and consequences of breakthrough infection, however data on the durability and protection of the vaccine are limited.

A multimodal fusion enabled ensemble approach for human activity recognition in smart homes.

How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows.