Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods

Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.

A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

Similarity in sensory modality and information domain impair processing in a dual-task context: Evidence from behavior, pupillometry, and EEG

Perception and navigation frequently require us to maintain sensory information in memory, while also processing new sensory inputs as they arise. Recent fMRI research has uncovered regions in human frontal cortex that coordinate these diverse processes. Across various attention and working memory (WM) tasks, these regions can be separated into two distinct networks. Each shows a response preference for either auditory or visual stimuli, yet in addition, each can be flexibly recruited based on the information domain (i.e., temporal or spatial) of the task, regardless of sensory modality. Motivated by the sensory and functional specializations these networks display, we investigated whether dual-task interference is affected by similarity between the tasks in sensory modality (auditory or visual) and information domain. Participants performed a novel dual-task paradigm involving different combinations of WM and Intervening tasks, while two temporally sensitive physiological signals, pupillometry and electroencephalography (EEG), were measured. Convergent evidence from behavioral performance, pupil dilation amplitudes, and event-related potentials (ERPs) indicates that dual-task interference is greatest when the tasks match in both sensory modality and information domain. However, differences also arise in the patterns of dual-task interference across these metrics, highlighting the differential strengths and sensitivities of each. The results are consistent with increased interference when multiple tasks compete for shared cognitive control resources due to a common sensory modality or information domain.

Supportive Care Needs of Patients with Cervical Cancer in the Northeast of Thailand

In this study, a descriptive design was used to explain the supportive care needs of cervical cancer patients in the Northeast of Thailand. The purposive sample was recruited from inpatient and outpatient departments of a university hospital. A total of 144 patients with cervical cancer in stage I - IV or recurrent stage completed the Thai version of the Supportive Care Needs Survey-Short Form 34 (SCNSF-34 Thai version). Descriptive statistics were used to identify the domains and items of the greatest unmet need. Results showed that standardized five domain scores indicated that participant’s unmet needs were highest in the health system and information domain ( = 2.95, S.D. = 1.15), and lowest in the sexuality domain ( = 1.63, S.D. = 0.94). The five most frequently endorsed items of moderate to high unmet need were all from the health system and information domain as follows: receiving information about things can do to help in recovering from illness (74.3 %), receiving information from documents, charts, or paintings about illness management and side effects that may occur at home (66 %), receiving information about cancer which is under control or in remission (63.2 %), support from at least one health care staff that can discuss illness, treatment, and follow up (63.2 %), receiving written information about the essential aspects of care (59.7 %), and receiving written information about the importance of care (59.7 %). It was clear that a large proportion of cervical cancer patients in Thailand reported unmet supportive care needs. Therefore, nurses should design nursing care services that are consistent with the supportive care needs.

Metrics for Offline Evaluation of Prognostic Performance

Prognostic performance evaluation has gained significant attention in the past few years.*Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.

Making Cross-Domain Recommendations by Associating Disjoint Users and Items Through the Affective Aware Pseudo Association Method

This paper utilizes an ingenious text-based affective aware pseudo association method (AAPAM) to link disjoint pseudo users and items across different information domains and leverage them to make cross-domain content-based and collaborative filtering recommendations. This paper demonstrates that the AAPAM method could seamlessly join different information domain datasets to act as one without any additional cross-domain information retrieval protocols. Besides making cross-domain recommendations, the benefit of joining datasets from different information domains through AAPAM is that it eradicates cold start issues while making serendipitous recommendations.

Factors Contributing to the Unmet Needs of Primary Caregivers of Omani Children Diagnosed with Leukemia

Objectives: Childhood cancers affect 6.7% of those under 14 years of age in Oman, with leukemia being most common. The psychological distress of having a child with leukemia is often compounded by perceived unmet needs on the part of the child’s primary caregiver. This study aimed to identify factors contributing to perceived unmet needs among primary caregivers of Omani children with leukemia. Methods: A cross-sectional study was conducted among 119 Omani caregivers accompanying their children to the National Oncology Center, Muscat, Oman. An Arabic version of the Needs Assessment of Family Caregivers‐Cancer (NAFC‐C) questionnaire was utilized to assess perceived unmet needs in seven domains. Ethical approval for the study was obtained from the Ministry of Health in Oman. Results: A total of 119 of Omani primary caregivers were contacted; of these, 101 agreed to participate in the study (response rate: 84.9%). Linear regression analysis showed significant correlations between total NAFC‐C scores and the child’s age (p = 0.014) and caregiver’s age (p <0.001), employment status (p = 0.024), and income (p = 0.028). Unmet needs in the obtaining information domain correlated with caregiver’s age (p <0.001), caring time (p = 0.018), and number of family members (p <0.001), whereas needs in the maintaining own strength domain correlated with the child’s gender (p = 0.028), time since diagnosis (p = 0.004), caregiver’s age (p <0.001), and education level (p = 0.019). Unmet needs in the accompanying the sick child domain were correlated with the child’s gender (p = 0.049), caregiver’s gender (p <0.001), and income (p <0.001). Conclusions: Various sociodemographic variables were found to affect the unmet needs of primary caregivers of children with leukemias in Oman. Healthcare professionals in Oman should be aware of such factors in order to reduce caregiver distress.

Health Literacy and Sustainable Healthcare: Machine Learning Modelling of Health Literacy Disparities in Australia (Preprint)

BACKGROUND Health literacy is a key issue in sustainable healthcare support to reduce health inequality and disparity. In multicultural societies with large and changing migration populations, there is a pressing need to understand the disparity of health literacy among diverse, complex population segments. This study offers much-needed insights into the correlation and interaction among various underlying dimensions of health literacy among diverse populations in Australia. This is based on the 2018 Health Literacy Survey (HLS) conducted by the Australian Bureau of Statistics (ABS) with 5,790 fully responding Australian adults aged above 15. OBJECTIVE Using machine learning to identify major contributing factors (especially, specific value ranges of key health literacy domains) to health literacy disparities in Australia. METHODS Statistical machine learning models (XGBoost Tree) were used to identify and measure the disparity of health literacy between Australian populations characterised by demographic, educational and socio-economic attributes: age, sex, country of birth, main language spoken at home, labour force status, equivalised income of household (EIH), family composition of household, level of highest educational attainment, disability status, Australian states and territories, remoteness and index of relative socio-economic disadvantage (SED). RESULTS Our analysis found that among the nine domains of the 2018 Australian HLS, there were distinct patterns of disparities in health literacy among Australians. Populations which reported higher scores of self-health management ability (SHMA) (Domain 3: 3.08-3.22) were Australians aged under 35 or above 55, having Year 12 or above educational attainment, English-speaking, married with/without children, female, in the top two EIH quintiles, in the lowest two SED quintiles, having no disability or restrictive long-term health condition, and living in the states of Queensland, Victoria, Western Australia, South Australia, Northern Territory. Populations which reported lower scores of SHMA (Domain 3: 2.99-3.08) were Australians aged between 35 and 55; having Year 11 or below education; speaking languages other than English at home; living alone or single parents with dependent children, male, in the bottom three EIH quintiles, in the highest three SED quintiles; having profound or severe core activity limitation, or other disability or restrictive long-term health condition, and living in the Australian states of New South Wales, Tasmania, and the Australian Capital Territory. CONCLUSIONS Our study identified major contributing factors (especially, specific value ranges of key health literacy domains) to health literacy disparities in Australia. These include education (Year 10/11 or below), disability (profound/severe disability), household income (lowest quintiles), the relative SED index (highest quintiles), gender (male Australians), age (aged 35-55 years), main home language (other than English), geographical location (major cities, inner, outer regional, remote Australia). Higher value ranges of these variables are strongly associated with higher scores of key health literacy domains such as access to healthcare support (Domain 1), access to sufficient health information (Domain 2), ability to appraise health information (Domain 5), ability to find good health information (Domain 8) and ability to understand health information well to know how to apply the health information (Domain 9). Higher scores on these domains in turn can have real impact on the overall self-health management ability (Domain 3). CLINICALTRIAL n/a