Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Patients’ Life Events, Emotional Health And Post-Illness Awareness: A Qualitative Study

ABSTRACT Purpose This study aimed to examine the life events, emotional health and post-illness awareness of COVID-19 patients. We interviewed 25 people who experienced COVID-19. Design The study was executed in qualitative descriptive design. Methods The study was conducted with a phenomenological design approach. Data were collected using an in-depth, semi-structured interview method. We interviewed 25 people who experienced COVID-19. Findings Three themes were identified in the content analysis: “Experiences before COVID-19,” “The active experience of COVID-19,” “Remnants of COVID-19. ” We found that when participants first heard they tested positive for COVID-19, the anxiety they experienced turned into panic and fear of death as the duration and severity of the symptoms of the disease increased. That uncertainty, as well as the influence of the media, intensified their fears. However, despite the unfavorable conditions, experiencing the disease led to positive awareness in all the participants. Conclusions We observed that the intensity of the symptoms of COVID-19, the uncertainty of the treatment process, and the attitude of the media were important factors in producing fear and anxiety. Experiencing the illness created an awareness that resulted in the participants’ questioning the meaning of life. Clinical Evidence Within the scope of preventive health services, there is a need for informative programs of education on COVID-19 prevention, coping, and self-improvement that will be useful in reducing the psychological effects of the disease.
Allergic contact dermatitis to Compositae: An Australian case series

Allergic contact dermatitis (ACD) to Compositae is caused by sensitisation to sesquiterpene lactones (SQLs) and subsequent exposure can occur from direct handling or from airborne transmission. Plants from the Compositae family are ubiquitous globally and their plant extracts are also used in various products.
Predictors of neuropathic pain and related functional disability in sciatica patients: a cross-sectional study

Abstract Purpose The aim of the present paper was to assess neuropathic pain and its predictors among patients with sciatica. In addition, we sought to investigate the predictors of functional disability in sciatica patients with neuropathic pain. Methods A cross-sectional study was carried out on a sample of 100 sciatica patients aged 18 years and older with a radiating pain from the lower back down to the leg below the knee. Information about socio-demographic characteristics, duration of symptoms, being treated or not, type of treatment, side of sciatica, presence of back pain, leg pain occurrence, leg pain intensity, functional disability and pain catastrophizing were collected. Results Of the total patients, 57% had neuropathic pain. Multivariable analysis showed that leg pain intensity was higher in the neuropathic pain group as compared to the non-neuropathic pain group. Also, patients with left sciatica were less likely to have neuropathic pain than patients with right sciatica. Patients with neuropathic pain having high levels of pain catastrophizing and being on bed rest for at least one week were at a higher risk of developing functional disability as compared to their counterparts. Conclusion Low levels of pain catastrophizing and less bed rest should be considered in interventions aimed at improving the functional ability for sciatica patients with neuropathic pain.
Virtual Road Signs

Studies have shown that experts are more sensitive to changes in the road scene than novice drivers, and use the driving patterns of other cars to infer important information. A tool that can help bridge the gap between experts and novices may be augmented reality (AR), which can be used to graphically overlay virtual information onto the real world that may not otherwise be easily inferred. In this paper, we propose an AR interface that aims to improve the sensation, attention, situation awareness and decision making of international drivers who are new to the United States (US). We present results of a preliminary study that identifies the needs of novice international drivers as well as an AR interface design created to support these needs. Contextual inquiry and analysis techniques were used to extract the needs of novice international drivers. Based on observations, iterative designs and a prototype were developed that merge AR and audio feedback. Lastly, the prototype was evaluated by two usability experts, who performed a heuristic walkthrough based on the principles of human information processing. The experts conclude that the interface has the potential of increasing the sensation, attention, situation awareness and decision making while reducing the mental workload of novice international drivers. Future work will include an empirical study to support the observations of the analytical evaluation presented herein.
Potential Global Sequestration of Atmospheric Carbon Dioxide by Semi-Arid Forestation

: The global carbon sequestration potential of semi-arid forests is described. Organic and inorganic carbon sequestration was studied in Israel’s planted Yatir forest, a 28 km 2 Aleppo pine forest growing at the semi-arid timberline (with no irrigation or fertilization). The organic carbon sequestration rate (above and below ground) was measured as 550 g CO 2 m −2 yr −1 , by Eddy Covariance flux and Carbon Stock counting methods. Assuming that the soil composition at Yatir is representative, we estimate a global organic sequestration rate of roughly 3.0 billion tons CO 2 yr −1 , after future global forestation, by extrapolating to 20 % of the global semi-arid area. Consider now also the inorganic carbon sequestration rate. A tree’s roots exhale CO 2 into the soil after some of the tree’s glucose (produced by photosynthesis) has been oxidized to supply energy for the tree’s cellular processes. We quantify the annual inorganic sequestration rate in Yatir soil cores by measuring the decreased density (due to calcite precipitation) versus depth, of bicarbonates in the liquid phase of the soil’s unsaturated zone (USZ). We found that the bicarbonate concentration decreases with depth, as the bicarbonates precipitate and are incorporated within the USZ. The depth profiles were converted to time profiles, taking into account that the annual rate of downward water percolation at Yatir has been measured to be ~11 cm yr −1 . At Yatir, in 1 Liter of sediment, the calcite precipitation rate was measured as 22 mg CO 2 yr −1 L −1 . The precipitated calcite remains in place long term, not dissolving in low rainfall semi-arid regions. Taking 6 m as the global average depth of root respiration in semi-arid regions, extrapolating as above, roughly 0.8 billion tons of CO 2 could potentially be precipitated globally each year in the USZ as calcite. The total organic plus inorganic sequestration rate of ~4 billion tons CO 2 yr −1 then represents roughly 20 % of the present annual increase of 20 billion tons of CO 2 being added to the present global atmospheric CO 2 reservoir of ~3200 billion tons. Although the uncertainties are high, this estimate already demonstrates the global potential, the need for further measurements, and the need to begin implementing a global land management policy of widespread tree planting in semi-arid regions.
Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

As the rate of percutaneous coronary intervention increases, in-stent restenosis (ISR) has become a burden. Random forest (RF) could be superior to logistic regression (LR) for predicting ISR due to its robustness. We developed an RF model and compared its performance with the LR one for predicting ISR. We retrospectively included 1501 patients (age: 64.0 ± 10.3; male: 76.7%; ISR events: 279) who underwent coronary angiography at 9 to 18 months after implantation of 2nd generation drug-eluting stents. The data were randomly split into a pair of train and test datasets for model development and validation with 50 repeats. The predictive performance was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). The RF models predicted ISR with larger AUC-ROCs of 0.829 ± 0.025 compared to 0.784 ± 0.027 of the LR models. The difference was statistically significant in 29 of the 50 repeats. The RF and LR models had similar sensitivity using the same cutoff threshold, but the specificity was significantly higher in the RF models, reducing 25% of the false positives. By removing the high leverage outliers, the LR models had comparable AUC-ROC to the RF models. Compared to the LR, the RF was more robust and significantly improved the performance for predicting ISR. It could cost-effectively identify patients with high ISR risk and help the clinical decision of coronary stenting.
Aspects of Personal Privacy in Communications: Problems, Technology and Solutions

The modern society is rapidly becoming a fully digital society. This has many benefits, but unfortunately it also means that personal privacy is threatened. The threat does not so much come from a 1984 style Big Brother, but rather from a set of smaller big brothers. The small big brothers are companies that we interact with; they are public services and institutions. Many of these little big brothers are indeed also being invited to our private data by ourselves. Privacy as a subject can be problematic. At the extreme it is personal freedom against safety and security. We shall not take a political stand on personal privacy and what level of personal freedom and privacy is the correct one. Aspects of Personal Privacy in Communications is mostly about understanding what privacy is and some of the technologies may help us to regain a bit of privacy. We discuss what privacy is about, what the different aspects of privacy may be and why privacy needs to be there by default. There are boundaries between personal privacy and societal requirements, and inevitably society will set limits to our privacy (Lawful Interception, etc.). There are technologies that are specifically designed to help us regain some digital privacy. These are commonly known as Privacy Enhancing Technologies (PETs). We investigate some these PETs including MIX networks, Onion Routing and various privacy-preserving methods. Other aspects include identity and location privacy in cellular systems, privacy in RFID, Internet-of-Things (IoT) and sensor networks amongst others. Some aspects of cloud systems are also covered. Content: Getting a Grip on Privacy The Legal Context of Privacy Anonymous Communications Secure Multi-party Computations and Privacy Privacy and Data Mining in Telecommunications Requirements for Cellular System Subscriber Privacy The 3GPP Systems and Subscriber Privacy Future Cellular Systems and Enhanced Subscriber Privacy Sensor Networks Radio Frequency Identification Privacy and Trust for the Internet-of-Things Privacy in the Cloud Summary and Concluding Remarks
Managing performance and energy in large scale data centers

Data centers are the computing facilities to process, store, and access information. Specifically, data centers serve as the key infrastructure for Cloud computing service providers. However, service providers have observed the trend of the under-utilization of production servers, which unnecessarily increases the total cost of ownership. The demand on managing the total cost of ownership is driving researchers to study both performance and energy issues in data centers, which are addressed in this thesis. For considering performance and energy, this thesis consists three contributions – design of a comprehensive multi-tier data center simulation platform; energy management of multi-tier data centers; performance model for predicting running times of applications in data centers. Design and analysis of large and complex distributed systems like data centers often suffer from the lack of an available physical infrastructure due to the cost constraints especially in the academic community. With this motivation, this thesis proposes the design of a comprehensive, flexible, and scalable simulation platform for in-depth analysis of multi-tier data centers. The potentials of such a simulation platform is demonstrated by its ability to simulate and measure the performance and power consumption of data centers with high accuracy. The second contribution is towards increasing the energy efficiency of multi-tier data centers using a multifacet approach, namely Hybrid, consisting of dynamic provisioning, frequency scaling and dynamic power management (DPM) schemes to reduce the energy consumption of multi-tier data centers, while meeting the Service Level Agreements (SLAs). The energy management scheme consists of two heuristics that utilize the Mean Value Analysis by modeling data centers as closed queueing networks. This scheme manages the energy consumption at the global and local levels. The global level management determines the sufficient number of servers for a service. At the local level, the proposed scheme dynamically exploits energy management techniques in individual servers. The third contribution is estimating the performance of data centers. This involves the development of performance models of shared service platforms with multiple resources contention given the fact that a typical data center is shared by multiple services that contend for multiple system resources. The proposed model can estimate the total completion time of jobs, which is the objective function of the scheduling problem. This work illustrates that an existing job scheduler can be enhanced by modifying its original objective function to the proposed model. To summarize, this thesis discusses the performance and energy implications in data centers, along with suggesting optimization techniques for improving performance and energy conservation in typical data centers.
Particle entity in the Doi–Peliti and response field formalisms

We introduce a procedure to test a theory for point particle entity, that is, whether said theory takes into account the discrete nature of the constituents of the system. We then identify the mechanism whereby particle entity is enforced in the context of two field-theoretic frameworks designed to incorporate the particle nature of the degrees of freedom, namely the Doi–Peliti field theory and the response field theory that derives from Dean’s equation. While the Doi–Peliti field theory encodes the particle nature at a very fundamental level that is easily revealed, demonstrating the same for Dean’s equation is more involved and results in a number of surprising diagrammatic identities. We derive those and discuss their implications. These results are particularly pertinent in the context of active matter, whose surprising and often counterintuitive phenomenology rests wholly on the particle nature of the agents and their degrees of freedom as particles.
QASem Parsing: Text-to-text Modeling of QA-based Semantics

Various works suggest the appeals of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios. Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides a shallow and partial representation. More recently, an appealing trend introduces semi-structured natural-language structures as an intermediate meaning-capturing representation, often in the form of questions and answers.In this work, we further promote this line of research by considering three prior QA-based semantic representations. These cover verbal, nominalized and discourse-based predications, regarded as jointly providing a comprehensive representation of textual information — termed QASem. To facilitate this perspective, we investigate how to best utilize pre-trained sequence-to-sequence language models, which seem particularly promising for generating representations that consist of natural language expressions (questions and answers). In particular, we examine and analyze input and output linearization strategies, as well as data augmentation and multitask learning for a scarce training data setup. Consequently, we release the first unified QASem parsing tool, easily applicable for downstream tasks that can benefit from an explicit semi-structured account of information units in text.
Automated machine learning: AI-driven decision making in business analytics

The realization that AI-driven decision-making is indispensable in todays fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning. This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries. The H2O AutoML framework was benchmarked against a manually tuned stacked ML model on three real-world datasets to test its performance, robustness, and reliability. The manually tuned ML model could reach a performance advantage in all three case studies used in the experiment. Nevertheless, the H2O AutoML package proved to be quite potent. It is fast, easy to use, and delivers reliable results, which come close to a professionally tuned ML model. The H2O AutoML framework in its current capacity is a valuable tool to support fast prototyping with the potential to shorten development and deployment cycles. It can also bridge the existing gap between supply and demand for ML experts and is a big step towards fully automated decisions in business analytics.
A comparison of modelled serum cTnT and cTnI kinetics after 60 min swimming

Abstract Post-exercise elevations of cardiac troponin T (cTnT) and I (cTnI) are often used in isolation but interpreted interchangeably. Research suggests, however, that post-exercise cTn kinetic might differ with each isoform. In this cross-sectional observational study, we collected blood samples before, immediately after (5 minutes), and at 1-, 3-, 6-, 12-, and 24-hour post-exercise in a mixed cohort of 56 participants after a distance-trial of 60 min continuous swimming (age range from 14 to 22, 57.1% female). Cardiac troponin kinetics were modelled using Bayesian mixed-effects models to estimate time to peak (TTP) and peak concentration (PC) for each isoform, while controlling for participants sex, tanner stage and average relative heart rate during the test. Exercise induced an elevation of cTnT and cTnI in 93% and 75% of the participants, respectively. Cardiac troponin T peaked earlier, at 2.9 h (CI: 2.6 – 3.2 h) post-exercise, whereas cTnI peaked later, at 4.5 h (CI: 4.2 – 4.9 h). Peak concentrations for cTnT and cTnI were 2.5 ng/L, CI: 0 – 11.2 ng/L and 2.16 ng/L, CI: 0 – 22.7 ng/L, respectively. Additionally, we did not observe a systematic effect of sex and maturational status mediating cTn responses.
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders

Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the privacy issues of public face datasets have attracted increasing attention in recent years. Previous works simply mask most areas of faces or synthesize samples using generative models to construct privacy-preserving face datasets, which overlooks the trade-off between privacy protection and data utility. In this paper, we propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE. We tailor the instance relation matching (IRM) module to minimize the distribution gap between real faces and FaceMAE reconstructed ones. During the deployment phase, we use trained FaceMAE to reconstruct images from masked faces of unseen identities without extra training. The risk of privacy leakage is measured based on face retrieval between reconstructed and original datasets. Experiments prove that the identities of reconstructed images are difficult to be retrieved. We also perform sufficient privacy-preserving face recognition on several public face datasets (i.e. CASIA-WebFace and WebFace260M). Compared to previous state of the arts, FaceMAE consistently \textbf{reduces at least 50\% error rate} on LFW, CFP-FP and AgeDB.
Epidemiological analysis of coronary heart disease and its main risk factors: are their associations multiplicative, additive, or interactive?

Abstract Objective The purpose of this study was to discover how considering multiplicative, additive, and interactive effects modifies results of a prospective cohort study on coronary heart disease (CHD) incidence and its main risk factors. Material and methods The Kuopio Ischaemic Heart Disease Risk Factor (KIHD) Study provided the study material, 2682 Eastern Finnish middle-aged men, followed since the 1980s. We applied multiplicative and additive survival models together with different statistical metrics and confidence intervals for risk ratios and risk differences to estimate the nature of associations. Results The mean (SD) follow-up time among men who were free of CHD at baseline (n = 1958) was 21.4 (10.4) years, and 717 (37%) of them had the disease and 301 (15%) died for CHD before the end of follow-up. All tested non-modifiable and modifiable risk factors statistically significantly predicted CHD incidence. We detected three interactions: circulating low-density lipoprotein cholesterol (LDL-C) × age, obesity × age, and obesity × smoking of which LDL-C × age was the most evident one. High LDL-C increased the risk of CHD more among men younger than 50 [risk ratio (RR) 2.10] than those older than 50 (RR 1.22). LDL-C status was the only additive covariate. The additive effect of high LDL-C increased almost linearly up to 18 years and then reached a plateau. The simple multiplicative survival model stressed glycemic status as the strongest modifiable risk factor for developing CHD [hazard ratio (HR) for diabetes vs. normoglycemia was 2.69], whereas the model considering interactions and time dependence emphasised the role of LDL-C status (HR for high LDL-C vs. lower than borderline was 4.43). Age was the strongest non-modifiable predictor. Conclusions Including covariate interactions and time dependence in survival models potentially refine results of epidemiological analyses and ease to define the order of importance across CHD risk factors. KEY MESSAGES Including covariate interactions and time dependence in survival models potentially refine results of epidemiological analyses on coronary heart disease. Including covariate interactions and time dependence in survival models potentially ease to define the order of importance across coronary heart disease risk factors.