Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE ( Digital Academic Repository ) A systematic review of hand hygiene improvement strategies : a behavioural approach

Background: Many strategies have been designed and evaluated to address the problem of low hand hygiene (HH) compliance. Which of these strategies are most effective and how they work is still unclear. Here we describe frequently used improvement strategies and related determinants of behaviour change that prompt good HH behaviour to provide a better overview of the choice and content of such strategies. Methods: Systematic searches of experimental and quasi-experimental research on HH improvement strategies were conducted in Medline, Embase, CINAHL, and Cochrane databases from January 2000 to November 2009. First, we extracted the study characteristics using the EPOC Data Collection Checklist, including study objectives, setting, study design, target population, outcome measures, description of the intervention, analysis, and results. Second, we used the Taxonomy of Behavioural Change Techniques to identify targeted determinants. Results: We reviewed 41 studies. The most frequently addressed determinants were knowledge, awareness, action control, and facilitation of behaviour. Fewer studies addressed social influence, attitude, self-efficacy, and intention. Thirteen studies used a controlled design to measure the effects of HH improvement strategies on HH behaviour. The effectiveness of the strategies varied substantially, but most controlled studies showed positive results. The median effect size of these strategies increased from 17.6 (relative difference) addressing one determinant to 49.5 for the studies that addressed five determinants. Conclusions: By focussing on determinants of behaviour change, we found hidden and valuable components in HH improvement strategies. Addressing only determinants such as knowledge, awareness, action control, and facilitation is not enough to change HH behaviour. Addressing combinations of different determinants showed better results. This indicates that we should be more creative in the application of alternative improvement activities addressing determinants such as social influence, attitude, self-efficacy, or intention.
Global Information Assurance Certification Paper

Oracle Forms, a component of Oracle Fusion Middleware is a technology to efficiently build browser-based enterprise applications. In order to support multiple transport methods Forms has its own binary message format that is meant to provide serialization and additional security for the platform. Unfortunately this proprietary format renders conventional security testing tools unusable. Reverse engineering methods will be employed to reveal the format of the protocol messages and to analyze the cryptographic protections in use. It will be shown that the proprietary encryption and key exchange schemes can be attacked in multiple ways. New tools will be presented which can be used to exploit these weaknesses and allow existing security testing software to be used against Oracle Forms applications. Based on the observations deployment best practices will also be described to help mitigate the discussed problems. Automated Security Testing of Oracle Forms Applications! 2 ! Author! Name,! email@address!
Poolability and Transferability in CNN. A Thrifty Approach

The current trend in deep learning models for semantic segmentation are ever increasing model sizes. These large models need huge data-sets to be trained properly. However medical applications often oﬀer only small data-sets available and require smaller models. A large part of these models’ parameters is due to their multi-resolution approach for increasing the receptive ﬁeld, i
Repentance and Subjective Well-being of Muslim College Students who Access Pornography: Self-control as a Moderator

Based on a survey of 133 Muslim college students in Yogyakarta found 75,9% of students had accessed cyber pornography. Islam has explicitly prohibits people from approaching pornography, this causes psychological conflicts in students because of differences in beliefs and actions, then causes anxiety as the characteristics of low subjective well-being. Religion is one of the factors that improve subjective wellbeing. Islam commands people to repent. Self-control helps a person refrain from making the same mistakes. This recent study used a correlational method and multiple regression analysis. The subject of this study was 102 bachelor students at Islamic universities in Yogyakarta. Measurements used the SWLS (a=0,819), PANAS: Positive Affect (a=0,797) and Negative Affect (a=0,877), Brief Self-control Scale (a=0,692), and Repentance Scale (a=0,841). The results of the data analysis using Pearson Correlation Bivariate shows the correlation between Repentance and Self-control r=0,505 sig(p)<0,001; Repentance and Subjective well-being r=0,366 sig(p)<0,001; Self-control and Subjective well-being r=0,574 sig(p)<0,001. There is a correlation between Repentance, Self-control, and Subjective Well-being of students at Islamic universities in Yogyakarta. Multiple regression analyses self-control as a moderator. The result was self-control strengthens the relationship between repentance and subjective well-being. The research hypothesis was accepted.
Impact of an icy winter on the Pacific oyster (Crassostrea gigas Thunberg, 1793) populations in Scandinavia

The Pacific oyster (Crassostrea gigas) is an invasive species that has dispersed into Scandinavia during the last few decades. The objective of this study was to evaluate the effects of extreme winter conditions on the mortality of the Pacific oyster in Scandinavia. The study was done by compiling mortality data from independent surveys in Denmark, Sweden and Norway. Winter mortality of the oysters increased with latitude, which can be explained by the colder climate experienced at higher latitudes. Mortality was also found to be affected by site specific conditions such as water depth at the sampling sites of oyster populations. Despite the severe winter conditions of 2009/2010 causing high mortality, the Pacific oyster still exists in large numbers in Scandinavia. The present investigation indicates that extreme winter conditions may result in a temporary reduction of the density of the Pacific oyster, but that the species can be expected to continue its invasion of Scandinavian coastal areas.
Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.
Ultrastructure of Wing Scales in Adult Mammophilic Vector Culex(Culex) quinquefasciatus (Say) in a Semi-arid Zone

Wing scales are considered a significant taxonomic character in the family Culicidae. Scanning ElectronMicroscopy (SEM) of wing scales has been studied. The anatomy, morphology and distribution of wingscales of Culex (Culex) quinquefasciatus Say were studied using scanning electron microscopy (SEM). Thisnew role of scales has been described here to segregate it from other species of subgenera Culicidae. Also, wehave added some vital variatious in larval characters such as setae and its disposition during development oflarvae in the laboratory as well as been recorded in their actual niche. There appears to be a need to recordthese larval variations, which are regularly occurring, but has not been recorded the in semi-arid zone ofIndia. The fourth instar of Cx. quinquefasciatus Say has been examined in the compound microscope. Thechaetotaxy variations on segments VII, VIII, and X have been observed. Whereas, on the X segment seta 2-X and 3-X with 2-6 branches were recorded. These observations have been compared with the work ofSirivankaran and White (1978) descriptions, where differences have been distinctly tabled. The role of wingscales in morphology cannot be ignored in any species specific and site-specific study in a semi-arid zonewhich can improve upon the taxonomy of Culicidae itself, when studied under higher magnifications.Reported variations of species could play a role in management of new diseases in this area. This ultimatelycan be an establishment factor for a relationship with epidemics with environmental parasitology andsystematics of vectors.
Use of the SCIDDICA-SS3 model for predictive mapping of debris flow hazard: an example of application in the Peloritani Mountains area

Extreme rainfall events and subsequent triggering of shallow landslides hit many regions worldwide, and climatic change increases the frequency of these phenomena, so that there is an increased need of appropriate risk mitigation tools. SCIDDICA-SS3 is a semiempirical Cellular Automata (CA) model for simulating the run-out of debris/mud flows. In this study, the model was initially calibrated by comparison with real events occurred in the Giampilieri area, of the Peloritani Mountains (Sicily) which was devastated on October 1st, 2009, by more than 500 landslides, mostly debris flows, caused by extreme rainfall, causing more than 30 causalities and severe damage to roads, railway and buildings. Satisfying simulations of the six main debris flows in the Giampilieri area with appropriate values of SCIDDICA-SS3 parameters, enables to transfer results in order to assess hazard scenarios in areas with similar geo-physical features. In particular, the Messina Sud tollgate of A18 highway (Sicily, Italy) is an example case of CA simulation for hazard analysis, with detection of subareas of highest risk and their possible protection works.
Identification and Exclusion Multiple Outliers in GNC Microsystem

Monitoring the positioning reliability is of great importance for modern Global Navigation Satellite System (GNSS), especially for receivers which are on duty in multi-constellation Guidance Navigation and Control (GNC). Integrity and relevant techniques are gaining popularity among which many researches focus on receiver autonomous integrity monitoring (RAIM) referring to independent integrity without any external assistance. Therefore, the performances of RAIM have to be lucubrated in order to assure the positioning quality. An innovative method has been proposed for failure identification and exclusion in GNSS among GNC microsystem. This method is established on the evaluation of subsets precheck and residual vector adjustment to conduct RAIM. It is applicable to multi-constellation receivers carrying on FDE. Concerning on abundant visible satellites, we consider Geometric Dilution Precision (GDOP) for optimized satellite spatial geometry and manage to avoid matrix inverse operation by Newton efficient equivalence solution, which saves large quantity computational complexity in GNC microsystem. The main purpose is to improve the ability of GNSS receiver accuracy, availability, continuity and integrity. The multiple outlier hypothesis has also been evaluated to ensure the integrity by the statistical global and local tests of least squares residual. Simulation consequences imply that we are able to obtain fairly good behaviors when processing single and two outliers.
First record of the marbled ray, Dasyatis marmorata (Elasmobranchii: Dasyatidae), from Greece (central Aegean Sea)

Abstract Background Currently, seven dasyatid species have been described in the Mediterranean Sea: Bathytoshia lata, Dasyatis marmorata, Dasyatis pastinaca, Dasyatis tortonesei, Himantura uarnak, Pteroplatytrygon violacea and Taeniura grabata. Papaconstantinou (2014) listed four species of Dasyatidae occurring in Greece (P. violacea, D. pastinaca, D. tortonesei and D. centroura; the latter was a case of misidentification and it is currently identified as B. lata, according to genetic analysis). However, the marbled stingray (D. marmorata) was not amongst them. Here, the presence of D. marmorata was examined for the first time in Greece. New information The present study provides updated information on the geographical distribution of D. marmorata in the Eastern Mediterranean Sea. A juvenile male stingray was captured in February 2019, during an onshore survey in Maliakos Gulf, located in the central Aegean Sea, Greece. The ray was examined at the Fisheries laboratory of the Hellenic Centre for Marine Research (HCMR) in Athens and was identified as D. marmorata. Morphological characters were recorded and DNA barcoding was applied to confirm the species identification. The combination of the two methods verified the occurrence of the marbled ray in the Greek waters. This is the first record of D. marmorata from the Aegean Sea.
Predicting Hierarchical Relationship in Job Title Taxonomy

Job title taxonomy is a digital representation of the job market knowledge. It is used in job search engines to unite and organize the global workforce. However, building a well-structured taxonomy is non-trivial. In this project, we create a classiﬁcation model to predict the synonymy and hypernymy relationships between title entities to construct a complex hierarchical structure so that it helps taxonomy team to ﬁnd parent and child taxa for each taxon, and place a new taxon to the proper branch.
Automatic Conﬁguration of Benchmark Sets for Classical Planning

The benchmarks from previous International Planning Com-petitions are commonly used to evaluate new planning algorithms. Since this set has grown organically over the years, it has several ﬂaws: it contains duplicate tasks, trivially solvable domains, unsolvable tasks and tasks with modelling errors. Also, different domain sizes complicate the aggregation of re-sults. Most importantly, however, the range of task difﬁculty is very small in many domains. We propose an automated method for creating benchmarks that solves these issues. To ﬁnd a good scaling in difﬁculty, we automatically conﬁgure the parameters of benchmarks domains. We show that the resulting benchmark set improves empirical comparisons by allowing to differentiate between planners more easily.
Development of the ProPal-COPD tool to identify patients with COPD for proactive palliative care

Background Our objective was to develop a tool to identify patients with COPD for proactive palliative care. Since palliative care needs increase during the disease course of COPD, the prediction of mortality within 1 year, measured during hospitalizations for acute exacerbation COPD (AECOPD), was used as a proxy for the need of proactive palliative care. Patients and methods Patients were recruited from three general hospitals in the Netherlands in 2014. Data of 11 potential predictors, a priori selected based on literature, were collected during hospitalization for AECOPD. After 1 year, the medical files were explored for the date of death. An optimal prediction model was assessed by Lasso logistic regression, with 20-fold cross-validation for optimal shrinkage. Missing data were handled using complete case analysis. Results Of 174 patients, 155 patients were included; of those 30 (19.4%) died within 1 year. The optimal prediction model was internally validated and had good discriminating power (AUC =0.82, 95% CI 0.81–0.82). This model relied on the following seven predictors: the surprise question, Medical Research Council dyspnea questionnaire (MRC dyspnea), Clinical COPD Questionnaire (CCQ), FEV1% of predicted value, body mass index, previous hospitalizations for AECOPD and specific comorbidities. To ensure minimal miss out of patients in need of proactive palliative care, we proposed a cutoff in the model that prioritized sensitivity over specificity (0.90 over 0.73, respectively). Our model (ProPal-COPD tool) was a stronger predictor of mortality within 1 year than the CODEX (comorbidity, age, obstruction, dyspnea, and previous severe exacerbations) index. Conclusion The ProPal-COPD tool is a promising multivariable prediction tool to identify patients with COPD for proactive palliative care.
Understanding single‐pass transmembrane receptor signaling from a structural viewpoint—what are we missing?

Single‐pass transmembrane receptors are involved in essential processes of both physiological and pathological nature and represent more than 1300 proteins in the human genome. Despite the high biological relevance of these receptors, the mechanisms of the signal transductions they facilitate are incompletely understood. One major obstacle is the lack of structures of the transmembrane domains that connect the extracellular ligand‐binding domains to the intracellular signaling platforms. Over a period of almost 20 years since the first structure was reported, only 21 of these receptors have become represented by a transmembrane domain structure. This scarceness stands in strong contrast to the significance of these transmembrane α‐helices for receptor functionality. In this review, we explore the properties and qualities of the current set of structures, as well as the methodological difficulties associated with their characterization and the challenges left to be overcome. Without an increased and focused effort to bring this class of proteins on par with the remaining membrane protein field, a serious lag in their biological understanding looms. Design of pharmaceutical agents, prediction of mutational affects in relation to disease, and deciphering of functional mechanisms require high‐resolution structural information, especially when dealing with a domain carrying so much functionality in so few residues.
A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.
Fast Intra Mode Decision for HEVC

For the higher coding performance than the previous video coding standards, High-Eﬃciency Video Coding (HEVC) adopts an intra prediction method with 35 modes, which requires heavy computational complexity. Intending to reduce this complexity, we analyzed the role of modes and proposed a scheme that contains two rough mode decision (RMD) processes with a customized set of modes to be tested in the ﬁrst stage. The second stage of the RMD is calculated for a maximum of 4 modes. As compared to the default encoding scheme in HEVC test model HM-16.20, experimental results show that the proposed method reduces encoding time up to 22.74% with negligible loss of coding eﬃciency.
RNNs Based Planning with Discrete and Continuous Actions Anonymous

Dealing with discrete and continuous changes in real-world dynamic environments is of great importance for robots. Despite the success of previous approaches, they impose severe restrictions, such as convex quadratic constraints on state variables, which limits the expressivity of the problem, especially when the problem is non-convex. In this paper, we propose a novel algorithm framework based on recurrent neural networks. We cast the mixed planning with discrete and continuous actions in non-convex domains as a gradient descent search problem. In the experiment, we exhibit that our algorithm framework is both effective and efﬁcient, especially when solving non-convex planning problems.
Music and Instrument Classiﬁcation using Deep Learning Technics

This paper presents our implementation of a multi-class classiﬁer that identiﬁes instruments in music streams. Our model consists of a CNN which’s input is an audio stream that we pre-process to extract the mel-spectogram, and outputs the dominance or non-dominance of pre-selected instruments. We focus our study on 3 instruments
Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT

We implemented and evaluated a multiple resolution residual network (MRRN) for multiple normal organs-at-risk (OAR) segmentation from computed tomography (CT) images for thoracic radiotherapy treatment (RT) planning. Our approach simultaneously combines feature streams computed at multiple image resolutions and feature levels through residual connections. The feature streams at each level are updated as the images are passed through various feature levels. We trained our approach using 206 thoracic CT scans of lung cancer patients with 35 scans held out for validation to segment the left and right lungs, heart, esophagus, and spinal cord. This approach was tested on 60 CT scans from the open-source AAPM Thoracic Auto-Segmentation Challenge dataset. Performance was measured using the Dice Similarity Coefficient (DSC). Our approach outperformed the best-performing method in the grand challenge for hard-to-segment structures like the esophagus and achieved comparable results for all other structures. Median DSC using our method was 0.97 (interquartile range [IQR]: 0.97-0.98) for the left and right lungs, 0.93 (IQR: 0.93-0.95) for the heart, 0.78 (IQR: 0.76-0.80) for the esophagus, and 0.88 (IQR: 0.86-0.89) for the spinal cord.