Miracle-dz/UniFAR-FTUs huggingface dataset:
Intensive Care Monitoring and Therapy Planning for Newborns

We developed a knowledge-based system, VIE-VENT, for monitoring and therapy planning of the artificial ventilation of newborn infants. Clinical and textbook knowledge were implemented in VIE-VENT’s knowledge base. Therapy planning was based on transcutaneously and invasively determined blood gas measurements and on clinical observations. After the selection of appropriate input parameters, measured data were validated and transformed into qualitative values. If these values differed from target values, therapeutic actions were proposed according to heuristic clinical rules of artificial ventilation. VIE-VENT was specifically designed for practical use under real-time constraints in Neonatal Intensive Care Units (NICUs). VIE-VENT was applied to the ICU data set provided by the organizers of the AAAI-AIM-94 symposium and a neonatal data set, which covered a neonatal case of similar severity. It included all transcutaneous measurements and allowed to explore the full potential of VIE-VENT.
Regulation of TMEM16A/ANO1 and TMEM16F/ANO6 ion currents and phospholipid scrambling by Ca2+ and plasma membrane lipid

TMEM16 proteins can operate as Ca2+‐activated Cl− channels or scramble membrane phospholipids, which are both highly relevant mechanisms during disease. Overexpression of TMEM16A and TMEM16F were found to be partially active at 37°C and at resting intracellular Ca2+ concentrations. We show that TMEM16 Cl− currents and phospholipid scrambling can be activated by modification of plasma membrane phospholipids, through reactive oxygen species and phospholipase A2. While phospholipids and Cl− ions are likely to use the same pore within TMEM16F, TMEM16A only conducts Cl− ions. Lipid regulation of TMEM16 proteins is highly relevant during inflammation and regulated cell death such as apoptosis and ferroptosis.
The Research of the Knowledge Management Technology in the Education

The knowledge has already played an important role in many areas in the period knowledge. As the core competitive ability, the knowledge has become the most important factor to evaluate the quality of education in the education field. The knowledge management takes the advantage of the collective wisdom from all the members in the organization to enhance the ability of adaptation and innovation. Naturally, the knowledge management receives more attention than any others in these years. The technology of the knowledge management should be developed to adapt to the environment which is changing everyday. The information technology's development would provide a better platform to the knowledge management. This article analyzes the characters of the knowledge management in education and the present technology of the education, and the difference between the knowledge management technology and the data and the information management technology. And then it proposed the key technology in the knowledge management of education.
The Synchrotron Shock Model Confronts a “Line of Death” in the BATSE Gamma-Ray Burst Data

The synchrotron shock model (SSM) for gamma-ray burst emission makes a testable prediction: that the observed low-energy power-law photon number spectral index cannot exceed -2/3 (where the photon model is defined with a positive index: dN/dE∝Eα). We have collected time-resolved spectral fit parameters for over 100 bright bursts observed by the Burst And Transient Source Experiment on board the Compton Gamma Ray Observatory. Using this database, we find 23 bursts in which the spectral index limit of the SSM is violated. We discuss elements of the analysis methodology that affect the robustness of this result, as well as some of the escape hatches left for the SSM by theory.
SPATIAL CONDITIONAL QUANTILE REGRESSION : WEAK CONSISTENCY OF A KERNEL ESTIMATE

We consider a conditional quantile regression model for spatial data. More precisely, given a strictly stationary random field Zi = (Xi, Yi)i∈NN , we investigate a kernel estimate of the conditional quantile regression function of the univariate response variable Yi given the functional variable Xi. The main purpose of the paper is to prove the convergence (with rate) in L norm and the asymptotic normality of the estimator.
Resilience and Its Association with Depression, Emotional and Behavioural Problems, and Mental Health Service Utilisation among Refugee Adolescents Living in South Australia

Background. Despite the frequency of traumatic or stressful events experienced by refugee children and adolescents prior to migration and following resettlement, the majority do not experience mental health problems emphasising the critical nature of resilience. While a host of factors deemed to be protective of mental health in young refugees have been identified, there has been little research exploring the role of resilience as a distinct psychological construct. This study aimed to explore the nature of psychological resilience in refugee adolescents and the relationship between resilience and depression, other emotional and behavioural problems, and mental health service uptake. Method. One hundred and seventy multiethnic refugee adolescents aged 13–17 from South Australia were administered a survey comprising the Connor-Davidson Resilience Scale (CD-RISC), Children’s Depression Inventory (CDI), and Strengths and Difficulties Questionnaire (SDQ). Results. Females tended to have higher resilience, as did those adolescents who had been living in Australia longer. Adolescents suffering from depressive symptoms or other emotional or behavioural problems had lower resilience. There was little evidence of an association between resilience scores and exposure to trauma or service utilisation. Discussion. Fostering resilience may be critical to efforts to prevent or reduce mental health problems in refugee adolescents.
A Prediction Based Long-Cycle Time Synchronization Algorithm for Sensor Networks

Existing time synchronization algorithms and protocols mostly focus on improving the synchronization accuracy. However, they usually require frequent resynchronization to keep designed precision in actual applications, which leads to high energy consumption and heavy traffic load. This paper presents a Prediction based Long-cycle Time Synchronization algorithm (PLTS), which puts emphasis on reducing the resynchronization frequency while guaranteeing a given accuracy. PLTS is a combination of periodic synchronization and prediction synchronization. It makes use of an existing time synchronization protocol to accomplish the periodic synchronization, while during the intervals of periodic synchronization, each node applies a prediction model to calibrate its own logic time according to the crystal oscillator's frequency characteristics. By this means, all nodes can keep synchronization till next periodic synchronization starts. Experiment results show that PLTS can reduce resynchronization frequency remarkably and possesses good merits in saving energy and reducing traffic load.
Quality of the Literacy Environment in Day Care and Children's Development

Abstract Although traditional assessments of day care environments have been linked to children's development, understanding of the specific characteristics of the environment that enhance language, literacy, and cognitive development is sketchy. The purpose of this study was to explore the lenvironment for iteracy in day care centers, its relationship with traditional measures of day care quality and its influence on children's cognitive and language development. Observation of the environments in the 30 community-based day care classrooms sampled revealed relatively impoverished environments. Correlation and multiple regression analyses indicated that settings of higher day care quality also had higher quality environments. In separate hierarchical regression analyses, controlling for variance due to family factors, both day care quality and the environment predicted a significant portion of the variance in children's language development but not in children's cognitive development.
QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques

We showcase QUEST (QUEry generator for STructured sources), a search engine for relational databases that combines semantic and machine learning techniques for transforming keyword queries into meaningful SQL queries. The search engine relies on two approaches: the forward, providing mappings of keywords into database terms (names of tables and attributes, and domains of attributes), and the backward, computing the paths joining the data structures identified in the forward step. The results provided by the two approaches are combined within a probabilistic framework based on the Dempster-Shafer Theory. We demonstrate QUEST capabilities, and we show how, thanks to the flexibility obtained by the probabilistic combination of different techniques, QUEST is able to compute high quality results even with few training data and/or with hidden data sources such as those found in the Deep Web.
On extracting session data from activity logs

Activity logs from large-scale systems facilitate the study of user behavior, which can be used to improve and tune the user experience. However, the available data often lacks important elements such as the identification of user sessions. Previous work typically compensated for this by setting a threshold of around 30 minutes, and assuming that breaks in activity longer than the threshold reflect breaks between sessions. We show that using such a global threshold introduces artifacts that may affect the analysis, because there is a high probability that long sessions are not identified correctly. As an alternative, we suggest that a suitable individual threshold be found for each user, based on that user's activity pattern. Applying this approach to a large dataset from the AOL search engine leads to a distribution of session durations that is free of artifacts like those that appear when using a global threshold.
Altered fingerprint detection – algorithm performance evaluation

The purpose of this paper is to present a comparative study on the performance of altered fingerprint detection algorithms. Different algorithms from different institutions have been evaluated on two different datasets. Both datasets feature real alterations on fingers and the ground truth regarding the alteration is known a priori, as, in some cases, corresponding pre-altered fingerprints were also available. The performance obtained on both datasets produced by either reference state-of-the-art or custom-built algorithms is higher than the reported 10% EER from previous studies [1].
Stage migration, selection bias, and survival associated with the adoption of positron emission tomography among medicare beneficiaries with non-small-cell lung cancer, 1998-2003.

PURPOSE Previous studies have linked the use of positron emission tomography (PET) with improved outcomes among patients with non-small-cell lung cancer (NSCLC). However, this association may be confounded by PET-induced stage migration and selection bias. We examined the association between PET use and overall survival among Medicare beneficiaries with NSCLC. PATIENTS AND METHODS Retrospective analysis of Surveillance, Epidemiology, and End Results (SEER) -Medicare data was used to characterize changes in overall survival, stage-specific survival, and stage distribution among Medicare beneficiaries with NSCLC between 1998 and 2003. RESULTS A total of 97,007 patients with NSCLC diagnosed between 1998 and 2003 met the study criteria. Two-year and 4-year survival remained unchanged, despite widespread adoption of PET. The proportion of patients staged with advanced disease increased from 44% to 50%. Upstaging of disease was accompanied by stage-specific improved survival, with 2-year survival of stage IV disease increasing from 8% to 11% between 1998 and 2003. PET was more likely to be administered to patients with less advanced disease (stages I through IIIA) and greater overall survival. CONCLUSION Overall survival among Medicare beneficiaries with NSCLC was unchanged between 1998 and 2003, despite widespread adoption of PET. The association between PET use and increased survival likely reflects an artifact of selection bias and consequent stage migration.
TÜKETİCİ TİPLERİ ENVANTERİNİN TÜRK KÜLTÜRÜNE UYARLANMASI

To understand how consumers make their purchasing decisions provides a significant contribution to the marketing process. Consumer Styles Inventory that was developed for this purpose and based on the assumption that consumers make purchasing decision according to certain styles has been accepted as a strong measurement instrument. But, having been developed the scale in western culture has necessitated investigation of validity in different culture. In this study, it is aimed to adapt Consumer Styles Inventory to Turkish culture. For this purpose, initially, confirmatory factor analysis has been applied to the Original scale. In the result of the analysis, because of not obtaining enough goodness of fit values, it has been examined which decision style dimensions are valid in Turkish culture using exploratory factor analysis. Then, the factor structure generated by EFA has been analyzed using confirmatory factor analysis in Lisrel 8,51. In the result of these analyses, the dimensions validated corresponding those of the original scale are ―Perfectionism/High Quality Consciousness, ―Brand Consciousness, ―NoveltyFashion Consciousness, ―Recreational Shopping Consciousness, ―Confusion from Overchoice. Lastly, the internal consistency, convergence and discriminant validity of the scale have been examined and compared with those of other similar studies.
TCP sur lien asymétrique : analyse des phénomènes et étude de solutions de faible empreinte mémoire ou de bout-en-bout. (Asymmetric link and TCP : analysis and end-to-end or low footprint solutions)

L'utilisation de TCP sur des liens asymetriques entraine frequemment des debits plus faibles qu'attendus au point de nuire sensiblement a la qualite de service ressentie par l'utilisateur. Ces baisses de performances peuvent prendre diverses formes parmi lesquelles une forte latence en debut de connexion, une sous utilisation de la capacite du lien ou encore des latences excessivement hautes pour l'ensemble de la connexion. Afin de contrer ces effets, plusieurs approches sont possibles, que ce soit de bout-en-bout par des modifications de la pile TCP/IP ou en cœur de reseau avec divers mecanismes d'ordonnancement. L'objectif de cette these est d'explorer comment un resultat similaire a celui obtenu par des methodes d'ordonnancement au goulot d'etranglement peut etre obtenu en travaillant de bout-en-bout, c'est a dire la ou les ressources de calcul et de memoire sont les plus abondantes. Ce questionnement est accompagne par une analyse en profondeur des phenomenes causant une degradation des performances, ainsi que l'evaluation des solutions existantes. Finalement, des solutions nouvelles, en cœur de reseau ainsi que de bout en bout, ont ete apportees et testees sur banc d'essai.
PRS: A Reusable Abstraction for Scaling Out Middle Tiers in the Datacenter

Scale-out datacenter applications are of enormous commercial interest, yet they are frustratingly hard to build. A common design pattern is to locate most of the application logic in a middle tier of soft-state servers and to provide storage using a separate backend system. The Partitioning and Recovery Service (PRS) is an infrastructure service that makes it easier to build these middle tier applications: it provides strong consistency on the soft state, supports arbitrary operations, and helps in recovery if state is ever lost. The PRS manages scaling out the middle tier across many machines, and hides the complexities of scale-out through a simple and powerful abstraction. We have built and evaluated the PRS, and other developers have built and deployed four scale-out soft-state datacenter applications using the PRS. Our experience with these applications confirms that the PRS significantly simplifies the construction of scale-out soft-state datacenter
Exploring Variations in Functional Connectivity of the Resting State Default Mode Network in Mild Traumatic Brain Injury

A definitive diagnosis of mild traumatic brain injury (mTBI) is difficult due to the absence of biomarkers in standard clinical imaging. The brain is a complex network of interconnected neurons and subtle changes can modulate key networks of cognitive function. The resting state default mode network (DMN) has been shown to be sensitive to changes induced by pathology. This study seeks to determine whether quantitative measures of the DMN are sensitive in distinguishing mTBI subjects. Resting state functional magnetic resonance imaging data were obtained for healthy (n=12) and mTBI subjects (n=15). DMN maps were computed using dual-regression Independent Component Analysis (ICA). A goodness-of-fit (GOF) index was calculated to assess the degree of spatial specificity and sensitivity between healthy controls and mTBI subjects. DMN regions and neuropsychological assessments were examined to identify potential relationships. The resting state DMN maps indicate an increase in spatial coactivity in mTBI subjects within key regions of the DMN. Significant coactivity within the cerebellum and supplementary motor areas of mTBI subjects were also observed. This has not been previously reported in seed-based resting state network analysis. The GOF suggested the presence of high variability within the mTBI subject group, with poor sensitivity and specificity. The neuropsychological data showed correlations between areas of coactivity within the resting state network in the brain with a number of measures of emotion and cognitive functioning. The poor performance of the GOF highlights the key challenge associated with mTBI injury: the high variability in injury mechanisms and subsequent recovery. However, the quantification of the DMN using dual-regression ICA has potential to distinguish mTBI from healthy subjects, and provide information on the relationship of aspects of cognitive and emotional functioning with their potential neural correlates.
SCAN: An Approach to Label and Relate Execution Trace Segments

Identifying concepts in execution traces is a task often necessary to support program comprehension or maintenance activities. Several approaches -- static, dynamic or hybrid -- have been proposed to identify cohesive, meaningful sequence of methods in execution traces. However, none of the proposed approaches is able to label such segments and to identify relations between segments of the same trace. This paper present SCAN (Segment Concept AssigNer) an approach to assign labels to sequences of methods in execution traces, and to identify relations between such segments. SCAN uses information retrieval methods and formal concept analysis to produce sets of words helping the developer to understand the concept implemented by a segment. Specifically, formal concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more specific to a given segment and high level relations between segments. The paper describes SCAN along with a preliminary manual validation -- upon execution traces collected from usage scenarios of JHotDraw and ArgoUML -- of SCAN accuracy in assigning labels representative of concepts implemented by trace segments.
Microstrip fed broadband mm-wave patch antenna for mobile applications

A novel design of a broadband mm-wave patch antenna for mobile devices is introduced. The proposed antenna element operates around 40 GHz. Through utilizing a multiple resonances patch structure, 9% bandwidth has been obtained. The substrate consists of low cost material and the antenna with its microstrip feed can easily be integrated with frontend circuits. Two parasitic patches are implemented to reduce the directivity of the antenna, which leads to a wide receiving angular range and makes the antenna suitable for specified mobile applications.
The Functional Equivalence of Ascending and Parallel Fiber Inputs in Cerebellar Computation

At the center of the computational cerebellar circuitry are Purkinje cells, which integrate synaptic inputs from >150,000 granule cell inputs. Traditional theories of cerebellar function assume that all granule cell inputs are comparable. However, it has recently been suggested that the two anatomically distinct granule cell inputs, ascending and parallel fiber, have different functional roles. By systematically examining the efficacy of patches of granule cells with photostimulation, we found no differences in the efficacy of the two inputs in driving the activity of, or in producing postsynaptic currents in, Purkinje cells in cerebellar slices in vitro. We also found that the activity of Purkinje cells was significantly increased upon stimulation of lateral granule cells in vivo. Moreover, when we estimated parallel fiber and ascending apparent unitary EPSC amplitudes using photostimulation in cerebellar slices in vitro, we found them to be indistinguishable. These results are inconsistent with differential functional roles for these two inputs. Instead, our data support theories of cerebellar computation that consider granule cell inputs to be functionally comparable.
RMLCC: Recovery-based multi-layer connected chain mechanism for multicast source authentication

Source authentication in multicast communication is essential and challenging requirement. We address the multicast stream authentication problem when the communication channel is under the control of an opponent who can drop, reorder or inject data. In such a network model, packet overhead and computing efficiency are important parameters to be taken into account when designing a multicast source authentication mechanism. The proposed scheme in this paper is highly robust to packet loss. It consists of a multi-layer connected chain structure in addition to an efficient recovery information model based on simple XOR operations. Our mechanism provides non-repudiation of the origin, low communication overhead, low delay and buffer capacity on the receiver side. NS-2 simulations show significant improvements over mechanisms in the same category.
Double-Pass System Assessing the Optical Quality of Pseudophakic Eyes

Purpose To compare the optical quality measurements obtained from the double-pass system and ocular aberrations, subjective visual acuity, and contrast sensitivity score in pseudophakic eyes. Methods Three months after cataract surgery, modulation transfer function (MTF) cutoff frequency, Strehl ratio, objective scatter index, and objective pseudoaccommodation obtained from the double-pass system were compared with total aberration, higher-order aberration, and spherical aberration obtained from ray-tracing aberrometer. In addition, parameters of the double-pass system were compared with subjective visual acuity and the contrast sensitivity score. Results Forty eyes of 40 patients were included. The MTF cutoff frequency and Strehl ratio were negatively correlated with total aberration (r = −0.503, p = 0.003; r = −0.509, p = 0.003, respectively) and subjective visual acuity (r = −0.453, p = 0.007; r = −0.354, p = 0.040, respectively). The objective scatter index was positively correlated with total aberration (r = 0.451, p = 0.024) and subjective visual acuity (r = 0.516, p = 0.008). The MTF cutoff frequency showed a correlation with contrast sensitivity score under photopic and mesopic conditions. Conclusions Optical quality parameters obtained from the double-pass system were correlated with ocular aberrations, subjective visual acuity, and contrast sensitivity score in pseudophakic eyes.