Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Water Policy Knowledge and Policy Preferences in the American West

The Western United States has made significant contributions to agricultural products both domestically and internationally. As the Western U.S. continues to grapple with water scarcity and extended periods of drought, evidence of misalignment between crop production and the volume of water necessary to maintain abundant food yields is becoming more pronounced. There are several policy nudges and mitigation strategies that can be employed to bring water availability and crop selection into alignment. Whether there is public support for these policies, or knowledge of how policies could impact water use in agriculture, it is important to understand what those preferences are and how people weigh tradeoffs between developing agricultural and water use. Using random household surveys of residents in the western U.S. states of Washington, Oregon, Idaho, and California, this study explores public water knowledge, the correlates of public water knowledge, and the impact knowledge has on preferred water policies while controlling for demographic characteristics, environmental efficacy, climate change belief, and political ideology. Findings show that knowledge does have an independent impact on preferred approaches to water policies while controlling for demographic characteristics, environmental efficacy, belief in climate change, and political ideology. Respondents who are knowledgeable about water recycling for food and water use for agriculture were significantly more supportive of water conservation policy approaches and less supportive of water supply-side approaches.
Common microbial isolates in an adult intensive care unit before and after its relocation and expansion

Objective: To describe the prevalence of common and clinically relevant microbial isolates before and after the migration of a 24-bed, open plan, adult intensive care unit (ICU) to a new extended design of 32 single rooms, supporting an expanded clinical oncology casemix while continuing all existing clinical services. Design: Retrospective, observational descriptive analysis covering the period 5 May 2014 to 4 May 2018 — the 2 years before and after the ICU relocation on 5 May 2016. Setting: A university-associated, tertiary teaching hospital and state trauma centre in Victoria, Australia. Patients: Adult ICU patients. Main outcome measures: Bacterial isolate frequency and incident rate ratios (IRRs) during the study period. Results: When compared with the old ICU, the incidence rates per 1000 occupied bed-days in the new ICU were lower for bacterial isolates overall (IRR, 0.88; 95% CI, 0.83–0.93), for coagulase-negative staphylococci (IRR, 0.64; 95% CI, 0.55–0.75) and for vancomycin-resistant enterococci (IRR, 0.50; 95% CI, 0.32–0.80). The incidence rates per 1000 occupied bed-days between ICU locations were unchanged for Staphylococcus aureus (IRR, 1.1; 95% CI, 0.91–1.3), extended-spectrum beta-lactamase-producing organisms (IRR, 1.4; 95% CI, 0.78–2.6) and carbapenemase-producing Enterobacterales (IRR, 0.85; 95% CI, 0.11–6.4). Conclusion: Within the limits of a before–after design and clinically directed sampling, relocation to a new ICU with single rooms and a growing oncological patient casemix was accompanied by no overall change in the apparent prevalence of the nosocomial pathogens S. aureus, extended-spectrum beta-lactamase-producing organisms or carbapenemase-producing Enterobacterales. These finding suggest that advanced physical infrastructure, including patient accommodation in single rooms, may play a role in overall safe delivery of critical care.
A Design Framework for Contextual and Embedded Information Visualizations in Spatial Augmented Reality

Spatial augmented reality (SAR) displays digital content in a real environment in ways that are situated, peripheral, and viewable by multiple people. These capabilities change how embedded information visualizations are used, designed, and experienced. But a comprehensive design framework that captures the specific characteristics and parameters relevant to SAR is missing. We contribute a new design framework for leveraging context and surfaces in the environment for SAR visualizations. An accompanying design process shows how designers can apply the framework to generate and describe SAR visualizations. The framework captures how the user’s intent, interaction, and six environmental and visualization factors can influence SAR visualizations. The potential of this design framework is illustrated through eighteen exemplar application scenarios and accompanying envisionment videos.
The Relationship Between E-Learning And Student Mathematics Disposition During The Covid-19 Pandemic Hubungan Antara E-Learning Dengan

The Covid-19 pandemic situation, initiated the Ministry of Education and Culture of the Republic of Indonesia to issue a circular regarding Learning From Home activities. However, 92% of students experience difficulties when carrying out online learning, including mathematics. This situation contributes to the dynamics of students' interest in learning mathematics during the COVID-19 pandemic and has the potential to affect the mathematical disposition of students themselves. This study aims to describe the relationship between e-learning and students' mathematical dispositions during the covid-19 pandemic. This study uses a quantitative approach with the Pearson correlation data analysis method, this study involved 28 students of class XII OTP SMK Salafiyah Plumbon Kab. Cirebon. Based on the results of the study, students' mathematical dispositions are directly proportional to the quality of the e-learning learning carried out. In this case, during the Covid-19 pandemic, class XII OTP SMK Salafiyah Plumbon Kab. Cirebon has a low mathematical disposition due to low e-learning. Based on the calculation results, there is a relationship between mathematical disposition and e-learning learning, the correlation coefficient is 0.464 with a significance of 0.013. In other words, the relationship formed between mathematical dispositions and learning outcomes is significant enough to indicate a relationship between e-learning and students' mathematical dispositions. This research can be used as an evaluation material for online learning (e-learning) mathematics during the Covid-19 Pandemic, by paying attention to aspects of students' mathematical dispositions, this research can also be the basis for further research on mathematical dispositions.
CIA_A_351068 211..221

Purpose: To investigate the association of sarcopenia index (SI) [(serum creatinine/serum cystatin C) × 100] with mortality, nutritional risk/malnutrition and sarcopenia among hospitalized older adults. Subjects and Methods: A prospective analysis was performed in 758 hospitalized older adults. Anthropometric measures and biochemical parameters were carried out for each patient. Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) 2019 algorithm. Nutritional risk/malnutrition was defined according to the European Society of Clinical Nutrition and Metabolism (ESPEN) criteria. The logistic regression analysis was employed for the analysis of correlation between the SI and other variables. Cox regression analysis was employed to analyze correlation between the SI and mortality. Results: A total of 758 participants agreed to participate in this study (589 men and 169 women; mean age: 85.6±6.1 years). The median of the follow-up period was 212 days. A total of 112 patients died. A high SI (per 1-SD was 22.1) was independently associated with all-cause mortality (HR per 1-SD = 0.61, 95% CI: 0.47–0.79), nutritional risk/malnutrition (OR per 1-SD = 0.38, 95% CI: 0.29–0.49) and sarcopenia (OR per 1-SD = 0.58, 95% CI: 0.45–0.74). High SI was positively correlated with albumin (r = 0.32, P < 0.001), hemoglobin (r = 0.24, P < 0.001), body mass index (BMI) (r = 0.12, P = 0.001), waist circumference (WC) (r = 0.08, P = 0.046), calf circumference (CC) (r = 0.45, P < 0.001), hand grip strength (HGS) (r = 0.52, P < 0.001) and negatively correlated with triglyceride glucose (TyG) (r = −0.11, P = 0.007). Conclusion: The SI based on serum cystatin C and creatinine is associated with long-term mortality, nutritional risk/malnutrition and sarcopenia in hospitalized older Chinese patients.
Literary mediation as ethics of care in adverse contexts

This article explores reading mediators’ literary mediation in adverse contexts and unconventional spaces (such as camps, hospitals, community centres, and others) in Mexico and Chile. Through a deductive thematic analysis of 20 interviews, a relationship is established between literary mediation and Joan Tronto’s (1993) four categories of the ethics of care (caring about, caring for, caregiving, and care receiving). It is concluded that literary mediation in these contexts takes the form of an accompaniment that is organized through an emerging methodology with communities. This methodology focuses on listening and is intuitively oriented and situated, which supports both recreational and diversion practices, as well as processes of self-knowledge and emotional containment. The discussion centres on how this mediation practice engages with both a humanitarian logic of care and with practices that decentralize the book’s authority and stature to pursue other relationalities.
An Investigation of the Needs Analysis and English Learning Process for the Hospitality Students at a Vocational High School

This study aims to find out the needs of the Hospitality students in learning English pertaining to the learning needs, present situation, and target needs. Furthermore, these findings would be elaborated on and find out the compatibility with the current learning process and the current condition of the Hospitality industry needs. The research was conducted in one vocational high school in Cirebon employing a mixed-method case study. The data were derived from the observation; questionnaire to thirty-five students in the 11 and 12 grades of Hospitality major; interview with one English teacher, six selected 11 and 12 grade students, one hotel general manager; and document analysis such as syllabus, handouts, and students’ assignment sheet. The findings revealed that speaking skills and vocabulary related to the specific terms for a Hospitality major are most needed by the students. In addition, the learning process in this study does not meet the needs of students specifically related to their major. This is also supported by the hotel general manager based on the Hospitality industry needs. The result suggests that English teaching for Hospitality majors in vocational schools should be provided with adequate knowledge and skills and, thus, the students possess the competencies needed in the current Hospitality job markets.
Ensemble Hybrid Learning Methods for Automated Depression Detection

Changes in human lifestyle have led to an increase in the number of people suffering from depression over the past century. Although in recent years, rates of diagnosing mental illness have improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding of depression detection requires effective feature representation and analysis of language use. In this article, text classifiers are trained for depression detection. The key objective is to improve depression detection performance by examining and comparing two sets of methods: hybrid and ensemble. The results show that ensemble models outperform the hybrid model classification results. The strength and effectiveness of the combined features demonstrate that better performance can be achieved by multiple feature combinations and proper feature selection.
Small-world Topological Model Based Vulnerability Assessment Algorithm for Large-scale Power Grid

The small-world network theory, which studies the intrinsic spreading mechanism of the large-scale chain-like fault in a power grid, attaches great significance to the inherent vulnerability of the power grid itself and its pertinent enhancement measures. On this basis, an integrated algorithm for vulnerability assessment based on the small-world topological model is proposed. And the independent algorithm for each part, including the topological modeling of the bulk power grid, the statistics of basic characteristic parameters as well as fault simulations and the vulnerability assessment and so on, is then designed separately, thus forms an integrated algorithm with good expansibility and the ability to provide convenience to a through study of the structure vulnerability of a bulk power grid itself and large-scale chain-like fault mechanism. The proposed algorithm has been verified with good results by several actual power grids.
IMPLEMENTATION AND ANALYSIS OF SEARCHING NEAREST KEYWORD SET USING PROJECTION AND MULTISCALE HASHING

: The existence of keywords allows us for the development of new tools to query and explore these multidimensional datasets. We are going to implement a method called Searching nearest Keyword Set using Projection and Multi Scale Hashing that uses hash-based index structures, and achieves high scalability and speedup. In multidimensional datasets, it is difficult for users to provide meaningful keys, and our work deals with another type of queries where users can only provide keywords as input. results are represented using text information (e
Process evaluation protocol for the SLEEP study: a hybrid digital CBT-I intervention for working people with insomnia

Introduction: The Supporting Employees with Insomnia and Emotional Regulation Problems (SLEEP) pilot uses hybrid digital Cognitive Behavioural Therapy for Insomnia (CBT-I) to help working people address their sleep and emotion regulation problems, using cognitive, behavioural, and psychoeducation techniques. A process evaluation within this trial will improve our understanding of how the intervention brought about change, including identifying barriers and facilitators to engagement and subsequent change, and the extent to which contextual factors played a role. Methods and analysis: This qualitative process evaluation will use semi-structured interviews, conducted via online videoconferencing, to explore participant experiences of the SLEEP intervention. Twenty-five participants who completed the SLEEP intervention (16 %) will be randomly sampled. Interviews will be analysed using a thematic and framework analytic approach. A codebook style thematic analysis will be used, and a framework focusing on the research questions will be applied to the codebook. The final report will present themes generated, alongside the finalised codebook. The report resulting from this research will adhere to CORE-Q quality guidelines. Ethics and dissemination: Full approval for the SLEEP study was given by the University of Warwick Biomedical and Research Ethics Committee (BSREC 45/20-21). All data collection adheres to data collection guidelines. Participants provided written informed consent for the main trial, and all interviewees provided additional written and verbal (audio-recorded) consent. The results of the process evaluation will be published in a peer-reviewed journal and presented at conferences. A lay report will be provided to all participants. Trial registration: ISRCTN13596153; Pre-results.
Prescription opioid-related alterations to amygdalar and thalamic functional networks in chronic knee pain: A retrospective case control resting-state connectivity study.

Objective: Long-term opioid use is associated with diminished pain relief, hyperalgesia, and addiction which is not well understood. This study aimed to characterise opioid-related brain network alterations in chronic pain, focused on the right amygdala, and left mediodorsal thalamic nuclei that play key roles in affective pain processing, and are particularly rich in mu opioid receptors (MOR). Subjects: Participants on opioid prescriptions with painful knee osteoarthritis and matched non-opioid using control pain participants. Methods and design: Seed-based functional connectivity (FC) maps from resting-state fMRI data were compared between groups. Results: We found right amygdala hyperconnectivity with the posterior default mode network (pDMN) and the dorsomedial prefrontal cortex in opioid users in contrast to anti-correlations in controls. Conversely, opioid users showed predominant hypoconnectivity of the left dorsomedial thalamic seed with the cingulate cortex except for the subgenual part displaying an anti-correlation in opioid users and no association in non-users. Opioid users also showed higher negative affect in exploratory post-hoc tests suggesting a potential contribution of trait anxiety to amygdala-pDMN FC alteration. Conclusion: Opioid use related hyperconnectivity of the right amygdalar network likely reflects maladaptive mechanisms involving negative affect and network plasticity. Hypoconnectivity of the mediodorsal thalamic nuclei with the anterior and mid cingulate on the other hand may reflect impaired resilience in line with previously reported compensatory MOR upregulation. In conclusion, this study provides new insight into possible brain mechanisms underlying adverse effects of prolonged opioids in chronic pain and offer candidate network targets for novel interventions.
Prevalence of bacterial coinfection and patterns of antibiotics prescribing in patients with COVID-19: A systematic review and meta-analysis

Background: Evidence around prevalence of bacterial coinfection and pattern of antibiotic use in COVID-19 is controversial although high prevalence rates of bacterial coinfection have been reported in previous similar global viral respiratory pandemics. Early data on the prevalence of antibiotic prescribing in COVID-19 indicates conflicting low and high prevalence of antibiotic prescribing which challenges antimicrobial stewardship programmes and increases risk of antimicrobial resistance (AMR). Aim: To determine current prevalence of bacterial coinfection and antibiotic prescribing in COVID-19 patients Data Source: OVID MEDLINE, OVID EMBASE, Cochrane and MedRxiv between January 2020 and June 2021. Study Eligibility: English language studies of laboratory-confirmed COVID-19 patients which reported (a) prevalence of bacterial coinfection and/or (b) prevalence of antibiotic prescribing with no restrictions to study designs or healthcare setting Participants: Adults (aged [≥] 18 years) with RT-PCR confirmed diagnosis of COVID-19, regardless of study setting. Methods: Systematic review and meta-analysis. Proportion (prevalence) data was pooled using random effects meta-analysis approach; and stratified based on region and study design. Results: A total of 1058 studies were screened, of which 22, hospital-based studies were eligible, compromising 76,176 of COVID-19 patients. Pooled estimates for the prevalence of bacterial co-infection and antibiotic use were 5.62% (95% CI 2.26 - 10.31) and 61.77% (CI 50.95 - 70.90), respectively. Sub-group analysis by region demonstrated that bacterial co-infection was more prevalent in North American studies (7.89%, 95% CI 3.30-14.18). Conclusion: Prevalence of bacterial coinfection in COVID-19 is low, yet prevalence of antibiotic prescribing is high, indicating the need for targeted COVID-19 antimicrobial stewardship initiatives to reduce the global threat of AMR.
Faster post-malnutrition weight gain during childhood is associated with risk of non-communicable disease in adult survivors of severe malnutrition

Background: Nutritional rehabilitation during severe malnutrition (SM) aims to rapidly restore body size and minimize poor short-term outcomes. We hypothesized that too rapid weight gain during and after treatment might however predispose to cardiometabolic risk in adult life. Methods: Weight and height during hospitalization and one year post-hospitalization were abstracted from hospital records of children who survived SM. Six definitions of post-malnutrition weight gain/growth were analysed as continuous variables, quintiles and latent classes in age-sex and minimum weight-for-age z-scores-adjusted regression models against adult anthropometry, body composition (DEXA), blood pressure, blood glucose, insulin and lipids. Results: 60% of 278 participants were male, mean (SD) age 28.2 (7.7) years, mean (SD) BMI 23.6 (5.2) kg/m2. Mean admission age for SM was 10.9 months (range 0.3-36.3 months) and 207/270 (77%) were wasted (weight-for-height z-score<-2). During childhood, mean rehabilitation weight gain (SD) was 10.1(3.8) g/kg/day and 0.8(0.5) g/kg/day in the first year post-hospitalization. Rehabilitation weight gain >12.9 g/kg/day was associated with higher adult BMI (difference=0.5kg/m2, 95%CI: 0.1-0.9, p = 0.02), waist circumference (difference=1.4cm, 95%CI: 0.4-2.4, p=0.005), fat mass (difference = 1.1kg, 95%CI: 0.2-2, p=0.02), fat mass index (difference=0.32, 95%CI: -0.0001-0, p=0.05), and android fat mass (difference=0.09 kg, 95%CI: 0.01-0.2, p=0.03). Rehabilitation (g/month) and post-hospitalization (g/kg/month) weight gain were associated with greater lean mass (difference = 0.7 kg, 95% CI: 0.1, 1.3, p = 0.02) (difference=1.3kg, 95% CI: 0.3-2.4, p=0.015) respectively. Conclusion: Rehabilitation weight gain exceeding 13g/kg/day was associated with adult adiposity in young, normal-weight adult SM survivors. This raises questions around existing malnutrition weight gain targets and warrants further studies exploring optimal post-malnutrition growth.
Deep Sequence Modeling for Pressure Controlled Mechanical Ventilation

This paper presents a deep neural network approach to simulate the pressure of a mechanical ventilator. The traditional mechanical ventilator has a control pressure monitored by a medical practitioner, which could behave inaccurately by missing the proper pressure. This paper exploits recent studies and provides a simulator based on a deep sequence model to predict the airway pressure in the respiratory circuit during the inspiratory phase of a breath given a time series of control parameters and lung attributes. This approach demonstrates the effectiveness of neural network-based controllers in tracking pressure waveforms significantly better than the current industry standard and provides insights to build effective and robust pressure-controlled mechanical ventilators.
I’m Not Sure: Designing for Ambiguity in Visual Analytics

Ambiguity, the state in which alternative interpretations are plausible or even desirable, is an inexorable part of complex sensemaking. Its challenges are compounded when analysis involves risk, is constrained, and needs to be shared with others. We report on several studies with avalanche forecasters that illuminated these challenges and identified how visualization designs can better support ambiguity. Like many complex analysis domains, avalanche forecasting relies on highly heterogeneous and incomplete data where the relevance and meaning of such data is context-sensitive, dependant on the knowledge and experiences of the observer, and mediated by the complexities of communication and collaboration. In this paper, we characterize challenges of ambiguous interpretation emerging from data , analytic processes , and collaboration and communication and describe several management strategies for ambiguity. Our findings suggest several visual analytics design approaches that explicitly address ambiguity in complex sensemaking around risk.