Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel economic perspectives on prevention and treatment : case studies for paediatric, adolescent and adult infectious and chronic diseases

Conventionele economische gezondheidsevaluaties richten zich voornamelijk op het rendement van de gezondheidszorg-service-begroting en richten zich zelden op de bredere gevolgen. Het laatste betreft een variatie aan externe, de impact van ziektes op kennis en prestaties op school en ook gedragseff ecten die economische beslissingen kunnen veranderen. Het benaderen van economische evaluaties in de gezondheidszorg in een breed perspectief, welke rekening houdt met een aanzienlijk deel van de bredere voordelen voor welzijn en externe, vereist een nieuwe beeldvorming en nieuwe methoden voor de meting van de voordelen. In Part I van dit proefschrift wordt een kwantitatief analytische methodologisch kader voor het beoordelen van het perspectief van de overheid van de fi scale invloed van gezondheidsproblemen en de fi scale voordelen van preventieve en therapeutische ingrepen (interventies) en immunisaties gepresenteerd. Part II van dit proefschrift betrekt de methode op pediatrische, jongeren en volwassenen immunisering. Part III richt zich op het meten van de bredere voordelen van het behandelen van ADHD in Duitsland. Aanvullend is voorgesteld dat er een overgangsdomein is, welke de micro- en macro-economische verbetering toegeschrijven aan welzijnsveranderingen verbindt. De tegenwoordig beschikbare economische evaluatie methoden laten doorgaans deze gevolgen achterwege, echter; zij kunnen worden aangepast om deze overgangsgevolgen te integreren. Ondanks de kanttekeningen en de praktische uitdagingen voor het uitvoeren van een bredere economische analyse, wordt gesuggereerd dat het verruiming van de voordelen, waar rekening mee wordt gehouden in een economische evaluatie, het waarschijnlijker maakt dat welzijnsmaatregelen met bredere voordelen en/of externe eff ecten, de voorkeur hebben.
SURFACE at Syracuse University SURFACE at Syracuse University

Recently there has been increasing interest in constructing general-purpose political opinion classifiers for applications in e-Rulemaking. This problem is generally modeled as a sentiment classification task in a new domain. However, the classification accuracy is not as good as that in other domains such as customer reviews. In this paper, we report the results of a series of experiments designed to explore the characteristics of political opinion expression which might affect the sentiment classification performance. We found that the average sentiment level of Congressional debate is higher than that of neutral news articles, but lower than that of movie reviews. Also unlike the adjectivecentered sentiment expression in movie reviews, the choice of topics, as reflected in nouns, serves as an important mode of political opinion expression. Manual annotation results demonstrate that a significant number of political opinions are expressed in neutral tones. These characteristics suggest that recognizing the sentiment is not enough for political opinion classification. Instead, what seems to be needed is a more finegrained model of individuals' ideological positions and the different ways in which those positions manifest themselves in political discourse.
Simplification and Event Identification for AIS Trajectories: the Equivalent Passage Plan Method

Two pre-processes for Automatic Identification System (AIS) trajectories commonly reported in the maritime knowledge discovery literature are trajectory simplification and event identification. Both pre-processes reduce storage and computational expenses by reducing the number of data points to be used in an analysis. This paper presents an event identification and trajectory simplification method based on behaviour identification and translation. Trajectory segments deemed to correspond to coastal or ocean navigation are translated into equivalent passage plan segments; a succinct description of the movements and behaviour of the ship. As a trajectory simplification method, it provides two main advantages over commonly used trajectory simplification methods: more meaningful simplified trajectories with better encoding of basic behaviours and the possibility to retain interesting behaviours in full resolution. As an event identification method, it is capable of differentiating between normal ocean or coastal navigating behaviour and complex or interesting behaviour, such as pilotage, reaction to a traffic conflict, or an involuntary deviation from the passage plan.
Mechanism of human-hornet conflicts in an urban ecosystem

i Acknowledgement . iv Chapter 1 . 1 General Introduction . 1 1.1 Green spaces as solution for urbanization problem . 1 1.2 Human wildlife interaction . 2 1.3 Emergence of urban wildlife conflict . 3 1.4 Wildlife conflict in Japan: High pest consultations demand . 4 1.5 Aim of research . 6 1.6 Outline of chapters . 7 Chapter 2 . 8 Materials & Methodologies . 8 2.1 Study site. 8 2.2 Hornet characteristics . 9 2.2.1 Life cycle of hornet . 9 2.2.2 Species-specific ecology . 10 2.2.3 Benefits of hornet in urban ecosystem . 10 2.3 Hornet sampling . 11 2.4 Hornet Removal data . 11 2.5 Vegetation indices. 12 2.5. 1 NDVI validity . 13 Chapter 3 . 20 Response of hornet towards greenness level . 20 3.
IoT-based Mobility Tracking for Smart City Applications

The proliferation of IoT devices and the growing deployment of 5G networks combine to provide the perfect ecosystem for advanced smart city use cases. In this paper, we address the possibility of detecting and quantifying flows of people on city streets thanks to deployment of commercial sensors, connected to the 5G network, that capture WiFi probes transmitted by people’s smartphones. We first outline the motivation and challenges of such a scenario. Then, we illustrate our approach and present results derived from live measurements in a testbed deployed in the city of Turin within the 5G-EVE project. We show that we can quite accurately estimate transit flows by simply collecting anonymized MAC addresses and timestamps from smartphones of passers-by.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Recent advances in binary instrumentation have been focused on performance. By statically transforming the code to avoid additional runtime operations, systems such as Egalito and RetroWrite achieve near zero overheads. The safety of these static transformations relies on several assumptions: (a) error-free and complete disassembly, (b) exclusive use of position-independentcode,and(c)codepointeridentification thatisfree of both false positives and false negatives. Violations of these assumptions can cause an instrumented program to crash, or worse, experience delayed failures that corrupt data or compromise security. Many earlier binary instrumentation techniques (e.g., DynamoRio, Pin, and BinCFI) minimized such assumptions, but the price to be paid is a much higher overhead, especially for indirect-call-intensive (e.g., C++) applications. Thus, an open research question is whether the safety benefits of the earlier works can be combined with the performance benefits of recent works. We answer this question in the affirmative by presenting a new instrumentation technique that (a) tolerates the use of position-dependent code and common disassembly and static analysis errors, and (b) detects assumption violations at runtime before they can lead to undefined behavior. Our approach provides a fail-crash primitive for graceful shutdown or recovery. We achieve safe instrumentation without sacrificing performance, introducing a low overhead of about ∼ 2%.
Using Machine Learning to Predict Geomorphic Disturbance: The Effects of Sample Size, Sample Prevalence, and Sampling Strategy

Advances in data acquisition and statistical methodology have led to growing use of machine‐learning methods to predict geomorphic disturbance events. However, capturing the data required to parameterize these models is challenging because of expense or, more fundamentally, because the phenomenon of interest occurs infrequently. Thus, it is important to understand how the nature of the data used to train predictive models influences their performance. Using a database of cliff failure prediction and associated covariates from Auckland, New Zealand, we assess the performance of seven machine‐learning algorithms under different sampling strategies. Three sampling components are investigated: (i) the number of data points used in model training (sample size), (ii) the prevalence of occurrences (presences) in the data, and (iii) random versus spatial sampling strategy. Across the seven algorithms, small sample sizes can produce models that perform relatively well, especially if the prime concern is identifying key predictors rather than quantifying risk or predicting categorical outcome. Our analyses show that for the same effort (i.e., number of samples), sampling around multiple locations provides better predictions than sampling at just one or a few locations. Predictive performance may be further improved by considering issues such as the nature of what absences actually represent and paying careful attention to decisions about hyperparameter tuning, training‐testing data splits, and threshold optimization. It is well known that big data can inform complex data‐driven modeling, but here we show that careful sampling can facilitate informative event prediction even from small data.
Transformative potential of Large Language Models (LLMs) in data mining on Electronic Health Records.

Objectives: Explore the potential of Large Scale Language Models (LLMs) to improve the organization of data in clinical reports, with a particular focus on the identification and classification of history and comorbidities in the electronic medical records of oncology patients in the Hospital. Virgin Macarena of Seville. We specifically evaluate the gpt-3.5-turbo-1106 and gpt-4-1106-preview models against the capabilities of specialized human evaluators. Methods: We implemented a script using the OpenAI API to extract, in JSON format, structured information on comorbidities reported in 250 personal history reports. These reports were manually reviewed in groups of 50 by five radiation oncology specialists. A detailed analysis of the discrepancies between GPT and medical models allowed the ground truth to be established. We compare the results using metrics such as Sensitivity, Specificity, Precision, Accuracy, F-value, Kappa index, and McNemar's test, as well as examining common causes of errors in humans and GPT models. Results: GPT-3.5 showed slightly lower performance than physicians in all metrics, although without statistically significant differences. GPT-4 showed clear superiority in several key metrics. Particularly, it reached 96.8% in sensitivity, compared to 88.2% for GPT-3.5 and 88.8% for doctors. These, however, marginally outperformed GPT-4 in accuracy (97.7% vs. 96.8%). GPT-4 demonstrated greater consistency, replicating exact results in 76% of reports after 10 analyses, in contrast to 59% for GPT-3.5. Clinicians failed more to detect explicit comorbidities, possibly due to fatigue or distraction, while GPT models inferred non-explicit comorbidities more frequently, sometimes correctly, although also resulting in more false positives. Conclusion: The LLMs studied, with carefully designed prompts, show competence comparable to that of medical specialists in the interpretation of clinical reports, even in complex and confusingly worded texts. Also considering their superior time and cost efficiency, these models represent a preferred option over human analysis for data mining and structuring information in extensive sets of clinical reports.
How to mitigate selection bias in COVID-19 surveys: evidence from five national cohorts

Background: Non-response is a common problem, and even more so during the COVID-19 pandemic where social distancing measures challenged data collections. As non-response is often systematic, meaning that respondents are usually healthier and from a better socioeconomic background, this potentially introduces serious bias in research findings based on COVID-19 survey data. The goal of the current study was to see if we can reduce bias and restore sample representativeness despite systematic non-response in the COVID-19 surveys embedded within five UK cohort studies using the rich data available from previous time points. Methods: A series of three surveys was conducted during the pandemic across five UK cohorts: National Survey of Health and Development (NSHD, born 1946), 1958 National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), Next Steps (born 1989-90) and Millennium Cohort Study (MCS, born 2000-02). We applied non-response weights and utilised multiple imputation, making use of covariates from previous waves which have been commonly identified as predictors of non-response, to attempt to reduce bias and restore sample representativeness. Results: Response rates in the COVID-19 surveys were lower compared to previous cohort waves, especially in the younger cohorts. We identified bias due to systematic non-response in the distributions of variables including parental social class and childhood cognitive ability. In each cohort, respondents of the COVID-19 survey had a higher percentage of parents in the most advantaged social class, and a higher mean of childhood cognitive ability, compared to the original (full) cohort sample. The application of non-response weights and multiple imputation was successful in reducing bias in parental social class and childhood cognitive ability, nearly eliminating it for the former. Conclusions: The current paper demonstrates that it is possible to reduce bias from non-response and to a large degree restore sample representativeness in multiple waves of a COVID-19 survey embedded within long running longitudinal cohort studies through application of non-response weights or multiple imputation. Such embedded COVID-19 surveys therefore have an advantage over cross-sectional COVID-19 surveys, where non-response bias cannot be handled by leveraging previously observed information on non-respondents. Our findings suggest that, if non-response is appropriately handled, analyses based on the COVID-19 surveys within these five cohorts can contribute significantly to COVID-19 research, including studying the medium and long-term effects of the pandemic.
Clinical nurse managers’ leadership styles and staff nurses’ work engagement in Saudi Arabia: A cross-sectional study

Background Effective nurse leadership enhances nurse welfare, improves patient care, and increases organisational success. A lack of adequate, supportive leadership significantly contributes to many nurses leaving the profession. Nurse managers need to prioritise engagement and retention as significant focus areas to address the nursing shortage in Saudi Arabia and accomplish the national program’s objectives. Aim To examine the correlation between the leadership styles of clinical nurse managers and staff engagement. Method This study used a descriptive, cross-sectional, correlational design. The leadership styles of clinical nurse managers were evaluated using the Multifactor Leadership Questionnaire (MLQ-5X). Work engagement was assessed using the Utrecht Work Engagement Scale (UWES). Questionnaires were distributed to 450 nurses in four public hospitals in western Saudi Arabia. Non-probability convenience sampling was used to collect the data. Results A total of 278 nurses from a range of clinical areas participated in the survey, which revealed that the leadership styles of clinical nurse managers positively or negatively impact nurse work engagement. Most clinical nurse managers exhibit transformational leadership, followed by transactional, then passive-avoidant styles. Respondents displayed a high level of work engagement, emphasising the positive impact of transformational and transactional leadership on work engagement outcomes. The findings showed significant differences in leadership styles and work engagement levels between Saudi and non-Saudi nurses across various dimensions. Conclusion Understanding the effect of leadership styles employed by nurse managers on work engagement can positively impact staff retention rates and the quality of patient care. Nurse managers should participate in training programs to enhance their practical leadership skills to enhance the work engagement levels of nurses. Implication Nurse work engagement can be improved by establishing training programs that promote effective leadership and highlight the significance of various leadership styles and their subsequent impact on nurse work engagement. Nursing students should receive education on leadership styles. Nursing leaders should be given access to mentoring programs and opportunities for career advancement to support the introduction of effective leadership styles.
WATER REQUIREMENT OF CHARDONNAY VINEYARDS IN ‘SERRA GAÚCHA, SOUTHERN BRAZIL

The decision to invest in irrigation systems does not have any technical subsidy on vine water relations at the edaphoclimatic conditions of Pinto Bandeira-RS that can assist the producers in defining the need of water replacement by irrigation. Based on the literature from other wine regions of the world, the poor water management in vineyards can induce vigorous and unbalanced growths and reduce the quality of the grapes. This work aims to characterization the variability of the micrometeorological conditions and their relations in the water requirement of Chardonnay vineyard destined for sparkling wines elaboration. Three sites were selected, one in Bento Gonçalves-RS and another two in Pinto Bandeira-RS, which have micrometeorological stations installed with sufficient variables to obtain the reference evapotranspiration (ETo) estimated by Penman-Monteith method parameterized by FAO-56. The periods corresponding to the phenological stages of the Chardonnay variety were defined based on a 10-years phenology study in this region and applied FAO56 Kc values to ET estimation. It was observed that even in the same mesoregion, the vineyard water requirement of the same variety and with similar cultural management has a slight variation.
Advancing Understanding on Greenspace and Mental Health in Young People

Abstract Mental distress among young people has increased in recent years. Research suggests that greenspace may benefit mental health. The objective of this exploratory study is to further understanding of place‐based differences (i.e., urbanity) in the greenspace‐mental health association. We leverage publicly available greenspace data sets to operationalize greenspace quantity, quality, and accessibility metrics at the community‐level. Emergency department visits for young people (ages 24 and under) were coded for: anxiety, depression, mood disorders, mental and behavioral disorders, and substance use disorders. Generalized linear models investigated the association between greenspace metrics and community‐level mental health burden; results are reported as prevalence rate ratios (PRR). Urban and suburban communities with the lowest quantities of greenspace had the highest prevalence of poor mental health outcomes, particularly for mood disorders in urban areas (PRR: 1.19, 95% CI: 1.16–1.21), and substance use disorders in suburban areas (PRR: 1.35, 95% CI: 1.28–1.43). In urban, micropolitan, and rural/isolated areas further distance to greenspace was associated with a higher prevalence of poor mental health outcomes; this association was most pronounced for substance use disorders (PRRUrban: 1.31, 95% CI: 1.29–1.32; PRRMicropolitan: 1.47, 95% CI: 1.43–1.51; PRRRural 2.38: 95% CI: 2.19–2.58). In small towns and rural/isolated communities, poor mental health outcomes were more prevalent in communities with the worst greenspace quality; this association was most pronounced for mental and behavioral disorders in small towns (PRR: 1.29, 95% CI: 1.24–1.35), and for anxiety disorders in rural/isolated communities (PRR: 1.61, 95% CI: 1.43–1.82). The association between greenspace metrics and mental health outcomes among young people is place‐based with variations across the rural‐urban continuum.
Isolation and characterization of multiple-stress tolerant bacteria from radon springs

Radon springs, characterized by their high concentrations of radon gas (Rn222), are extreme environments with unique physicochemical conditions distinct from conventional aquatic ecosystems. Our research aimed to investigate microbial life in radon springs, focusing on isolating extremophilic bacteria and assessing their resistance to adverse conditions. Our study revealed the prevalence of Actinomycetia species in the radon spring environment. We conducted various tests to evaluate the resistance of these isolates to oxidative stress, irradiation, desiccation, and metal ion content. These extremophilic bacteria showed overall higher resistance to these stresses compared to control strains. Lipidomic analysis was also employed to provide insights into the adaptive mechanisms of these bacteria which were found mainly in the correlations among individual clusters and changes in content of fatty acids (FA) as well as differences between content and type of FAs of environmental isolates and type strains.
Relationship between Geography-Tourism and Tourism's Effects According to High School Students.

This research was designed in the screening model to determine the opinions of high school students on tourism effects and geography-tourism relations. The data were gathered from 760 students who were educated in high schools in the central district of Kutahya , identified by cluster sampling method. The data were collected with the help of a scale developed by the researchers. This process benefited from the studies carried out in the related literature. According to findings, high school students' opinions on the social and cultural effects of tourism are positive. It was observed that the students do not have a clear view of the environmental effects of tourism, and remain at a level of indecision. According to the students, tourism is an important tool in promoting the country and Turkey is a country rich in natural and cultural tourism values. However, students were not sure whether Turkey can use its full potential and whether it introduces enough tourism opportunities at home and abroad. It was seen that the students can relate geography and tourism. The students emphasized the importance of geography in preserving the natural and cultural environment and in tourism education. In this context, they stated that it is necessary to arrange visits to tourist areas in geography lessons. When the significant differences in the view of students on tourism effects and their views on geography-tourism relation were examined, it was observed that female students have more positive views than male students, that students in 12th grade have more positive views than students in 9th, 10th, and 11th grades, that the students in the academic high schools have more positive views than the students in the skill set related to high schools and in the vocational high schools.