Miracle-dz/UniFAR-FTUs huggingface dataset:
Short Term Outcome of Neonates Born With Abnormal Umbilical Cord Arterial Blood Gases

Background: Umbilical arterial blood gas (UABG) analysis is more objective than other methods for predicting neonatal outcome. Acidemic neonates may be at risk for unfavorable outcome after birth, but all neonates with abnormal arterial blood gas (ABG) analysis do not always have poor outcome. Objectives: This study was carried out to determine the short term outcome of the neonates born with an abnormal ABG. Patients and Methods: In a cohort prospective study 120 high risk mother-neonate pairs were enrolled and UABG was taken immediately after birth. All neonates with an umbilical cord pH less than 7.2 were considered as case group and more than 7.2 as controls. Outcomes like need to resuscitation, admission to newborn services and/or NICU), seizure occurrence, hypoxic ischemic encephalopathy (HIE), delayed initiation of oral feeding and length of hospital stay were recorded and compared between the two groups. P value less than 0.05 was considered as being significant. Results: Comparison of short term outcomes between normal and abnormal ABG groups were as the fallowing: need for advanced resuscitation 4 vs. 0 (P = 0.001), NICU admission 16 vs. 4 (P = 0.001), convulsion 2 vs. 0 (P = 0.496), HIE 17 vs. 4 (P = 0.002), delay to start oral feeding 16 vs. 4 (P = 0.001), mean hospital stay 4 vs. 3 days (P = 0.001). None of the neonates died in study groups. Conclusions: An umbilical cord PH less than 7.2 immediately after birth can be used as a prognostic factor for unfavorable short term outcome in newborns.
Increasing On-line Classification Performance Using Incremental Classifier Fusion

To process the large amounts of data industrial systems are producing nowadays, machine learning techniques have shown their usefulness in many applications. As the amounts of data being generated are getting huge, the need for machine learning methods which can deal with them in an appropriate way -- i.e.\ methods which can be adapted incrementally -- becomes very important. Ensembles of classifiers have been shown to be able to improve the predictive accuracy as well as the robustness of single classification methods. In this work novel incremental variants of several well-known classifier fusion methods (Fuzzy Integral, Decision Templates, Dempster-Shafer Combination and Discounted Dempster-Shafer Combination) are presented. Furthermore, a novel incremental classifier fusion method called Incremental Direct Cluster-based fusion will be introduced, which exploits an evolving clustering approach. A flexible and interactive framework for on-line learning will be introduced, in which the ensemble (classifier fusion) methods are adapted incrementally in a sample-wise manner together with their base classifiers. The performance of this framework and the proposed incremental classifiers fusion methods therein are evaluated on five real-world visual quality inspection tasks, captured on-line from an industrial CD imprint production process.
Comparative Performance to Changeability of Measuring Software Components

Changeability is defined as a measure of impact of changes made to a module on the rest of the system. Changeability is one of the characteristics of maintainability. Factors include coupling between the modules, lack of code comments, naming of functions and variables. In this research work we are solving this issue by building a framework which helps to measure degree of changeability by using clustering methods (machine learning) , which would be best suited for our problem definition and better from previous research works.
A Framework for Parallel Disk-Based Computation for Very Large Groups

For the first time in computational group theory, parallel disk-based search algorithms are used. These algorithms emphasize streaming access while avoiding the high latency of disk. Search and enumeration is a recurring theme throughout computational group theory. Examples include orbit computation, permutation group membership (permutation group order, random element of a permutation group,etc.), condensation of matrix representations, generation of permutation representations from matrix representations, group intersection, centralizer, and normalizer. Without the use of disk, larger computations of this type would be infeasible. An analysis of these algorithms is presented. Formulas with application-specific parameters are derived that successfully predict each of their run times. Armed with these algorithms, we compute a permutation representation of the Baby Monster sporadic simple group using 8 terabytes of distributed disk. In addition, we compute a matrix condensation of Fischer’s group Fi23 using 2 terabytes of distributed disk. Finally, we present and implement a library with a well-developed API that allows computational group theory programmers to easily use the search algorithms presented in this paper.
MAXILLOFACIAL PATHOLOGY DETECTION USING AN EXTENDED A CONTRARIO APPROACH COMBINED WITH FUZZY LOGIC

In this work, we develop a method for pathology detection of the maxillary-facial in 3D CT medical images. Our method is based on “a contrario” approach that is a statistical region based method. To do this, we detect significant changes in regions previously delineated by a physician anatomist. We apply a decision algorithm based on fuzzy logic on the changes detected and we get a decision with a degree of uncertainty. Tests conducted on a basis of real images have shown the performance and robustness of the proposed approach.
A 1.5-to-5.0GHz input-matched +2dBm P1dB all-passive switched-capacitor beamforming receiver front-end in 65nm CMOS

Phased arrays in CMOS for consumer communication bands aim to enhance receiver performance by exploiting beamforming with antenna arrays. Sensitivity increases with the number of antenna elements through array gain and interferers can be cancelled through the spatial filtering of the beam pattern [1]. For the latter, the linearity of the receiver before the beamforming summing point becomes a bottleneck as interferers are not cancelled yet. Phase shifting in the LO domain reduces the complexity in the signal path and enables the use of linear signal blocks, but has high requirements on the multiphase LO generation [2]. On the other hand, a switched-capacitor phase shifter can be very linear, but is limited by the linearity of the necessary input matching and element summing gm-stages [3]. This paper proposes a fully passive phased-array receiver front-end which implements impedance matching, phase shifting and element summing with only switched-capacitor stages for a high linearity.
BrainBrowser: distributed, web-based neurological data visualization

Recent years have seen massive, distributed datasets become the norm in neuroimaging research, and the methodologies used to analyze them have, in response, become more collaborative and exploratory. Tools and infrastructure are continuously being developed and deployed to facilitate research in this context: grid computation platforms to process the data, distributed data stores to house and share them, high-speed networks to move them around and collaborative, often web-based, platforms to provide access to and sometimes manage the entire system. BrainBrowser is a lightweight, high-performance JavaScript visualization library built to provide easy-to-use, powerful, on-demand visualization of remote datasets in this new research environment. BrainBrowser leverages modern web technologies, such as WebGL, HTML5 and Web Workers, to visualize 3D surface and volumetric neuroimaging data in any modern web browser without requiring any browser plugins. It is thus trivial to integrate BrainBrowser into any web-based platform. BrainBrowser is simple enough to produce a basic web-based visualization in a few lines of code, while at the same time being robust enough to create full-featured visualization applications. BrainBrowser can dynamically load the data required for a given visualization, so no network bandwidth needs to be waisted on data that will not be used. BrainBrowser's integration into the standardized web platform also allows users to consider using 3D data visualization in novel ways, such as for data distribution, data sharing and dynamic online publications. BrainBrowser is already being used in two major online platforms, CBRAIN and LORIS, and has been used to make the 1TB MACACC dataset openly accessible.
An experimental study on the effectiveness of a mutual support group for family caregivers of a relative with dementia in mainland China

Abstract When caring for an older relative with dementia, family members experience considerable distress and burden. Literature reviews show that supportive group interventions for these caregivers have significant positive effects on improving their distress and quality of life, but not consistent and conclusive. Limited research is found in Asian populations. This study tested the effectiveness of a 12-session bi-weekly mutual support group program for Chinese family caregivers of a relative with dementia in Hong Kong, when compared with standard family support service. An experimental study with pre- and post-test, parallel groups design was conducted. A randomized sample of 78 family caregivers, 39 in each of the experimental and control groups, from one regional dementia care center participated in the study. A protocol was specifically designed by an advanced practice nurse to guide the mutual support group process and the facilitator and peer leader training, based on evidence from the literature on family support group intervention in Western countries. The results of ANOVA tests indicated that the mutual support group participants had significantly greater improvements in distress levels and quality of life than the control group. There were only mild changes in the demands for mental health services in both groups at post-test. These findings support the effectiveness of mutual support groups to offer psychosocial support to Chinese family caregivers in dementia care beyond routine community mental health care.
Epigenetic Basis of Regeneration: Analysis of Genomic DNA Methylation Profiles in the MRL/MpJ Mouse

Epigenetic regulation plays essential role in cell differentiation and dedifferentiation, which are the intrinsic processes involved in regeneration. To investigate the epigenetic basis of regeneration capacity, we choose DNA methylation as one of the most important epigenetic mechanisms and the MRL/MpJ mouse as a model of mammalian regeneration known to exhibit enhanced regeneration response in different organs. We report the comparative analysis of genomic DNA methylation profiles of the MRL/MpJ and the control C57BL/6J mouse. Methylated DNA immunoprecipitation followed by microarray analysis using the Nimblegen ‘3 × 720 K CpG Island Plus RefSeq Promoter’ platform was applied in order to carry out genome-wide DNA methylation profiling covering 20 404 promoter regions. We identified hundreds of hypo- and hypermethylated genes and CpG islands in the heart, liver, and spleen, and 37 of them in the three tissues. Decreased inter-tissue diversification and the shift of DNA methylation balance upstream the genes distinguish the genomic methylation patterns of the MRL/MpJ mouse from the C57BL/6J. Homeobox genes and a number of other genes involved in embryonic morphogenesis are significantly overrepresented among the genes hypomethylated in the MRL/MpJ mouse. These findings indicate that epigenetic patterning might be a likely molecular basis of regeneration capability in the MRL/MpJ mouse.
Social workers’ policy practice in Israel: internal, indirect, informal and role contingent

ABSTRACT Social work is strongly committed to furthering social justice, among others, by incorporating efforts to influence policies into social work practice. However, there is little empirical data on if, and how, social workers seek to impact social policy as an integral part of their job. This article explores the policy-related activities which Israeli social workers engage in as part of their job. The sample consisted of 621 social workers: 300 direct practitioners and 106 community social workers employed by local government, 109 direct practitioners in hospitals, and 106 social workers employed by nonprofits. The findings show that practitioners in all four groups engaged in policy-oriented activities as part of their job, albeit to an often-limited extent. They were most involved in activities related to policies within their own organisation and much less in policy activities intended to influence local or national policies. The main conclusion is that social workers deal with their commitment to policy advocacy and with the various constraints on this type of activity by integrating primarily indirect and informal forms of policy engagement into their practice. The level of this activity is contingent on the social workers’ professional roles and the organisational context in which they work.
Identifying citing sentences in research papers using supervised learning

Researchers have largely focused on analyzing citation links from one scholarly work to another. Such citing sentences are an important part of the narrative in a research article. If we can automatically identify such sentences, we can devise an editor that helps suggest when a particular piece of text needs to be backed up with a citation or not. In this paper, we propose a method for identifying citing sentences by constructing a classifier using supervised learning. Our experiments show that simple language features such as proper nouns and the labels of previous and next sentences are effective features to identifying citing sentences.
Some examples of F and D-term SUSY breaking models

In this paper we investigate the possibility of finding models that, in the global minimum, break SUSY by a combined F and D-term effect. We find that if the superpotential is a cubic polynomial in the fields and the Kahler potential is canonical this does not happen. We then give two examples where the previous conditions do not apply and where the vacuum of the model breaks SUSY by combined F and D-term vevs: SQCD+singlet with N_C=N_F+1, and a baryon deformed ISS model. We briefly discuss the phenomenological viability of the models.
Exploring the Effect of Expulsion on Student’s Psycho-Social Development

Expulsion from school is a life changing event and can leave a big scar in students’ lives making them feel lost or miserable. This incident might change the direction of a student’s life and whether the outcome is good or bad, will largely depend on the student. However, without keen intercession especially from parents, this interference in the students' lives might have an unsalvageable effect. This narrative case study aims to find out about an expelled girl’s life once she left her old school where she was expelled from. The intention of this study is to understand how the respondent recognizes and overcomes her negative perception and emotions as an expelled student in the new school in regards to her psychosocial development. Results from the findings using qualitative interviewing, journal writing, and document analysis show four notable areas. The expelled student (1) expresses deep feeling of contrition after expulsion, (2) managed her emotions by surrounding herself in the company of people who gave her moral support like her parents, teachers and friends (3) parents, particularly mother, was the most pivotal in helping through her emotions, (4) feels the expulsion was unfair, but supports zero tolerance policies for serious offenses. This research provides valuable information regarding the expulsion experience of a student and addresses the effect on the student’s psychosocial development. The analysis concludes that expulsion can be coped by the student with proper guidance and leads the expulsion experience in a positive way.
International Journal of Hospitality Management Developing Green Management Standards for Restaurants: an Application of Green Supply Chain Management

Keywords: Green restaurant Food and beverage industry Sustainable development Green supply chain management Delphi Technique a b s t r a c t Hospitality businesses influence the sustainability of their natural environment by consuming significant amounts of natural resources. However, research on applying green supply chain management (GrSCM) with process of food production to construct green management indicators for restaurants is lacking. This study thus establishes a green management standard with GrSCM that green restaurants can implement. This study combines GrSCM and food and beverage management to develop green restaurant standards, and primarily adopts the Delphi Technique to develop green standards of restaurant management. A total of 23 experts from government, industry, and academia were invited to participate. The results show that green restaurant management standards comprise three facets (green foods; green environment and equipment; and green management and social responsibility), nine sub-facets, and 81 indicators: besides five indicators for the new established restaurant, the remaining 76 were standards for existing restaurants in Taiwan. In addition to its practical implications, an important contribution of this study is its application of GrSCM to establish a green management standard for restaurants.
Brain MR image segmentation based on Gaussian mixture model with spatial information

As magnetic resonance imaging (MRI) is an important technology of radiological evaluation and computer-aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. In general, the Gaussian mixture model (GMM) is highly effective for MR image segmentation. But for the conventional GMM appling in image segmentation, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and the spatial distribution of pixels in an image is not taken into consideration. In this paper, we present a novel GMM scheme by utilizing local contextual information and the high inter-pixel correlation inherent for the segmentation of brain MR image. Firstly, a local spatial function is established, and the class probabilities of very pixels according to bayesian rules are determined adaptively based on local spatial function. Secondly, Expectation Maximization algorithm as an optimization method is used to obtain iterative formula of E-step and M-step for the proposed model Finally, the segmentation experiments by synthetic image and real image demonstrate that the proposed method can get a better classification result.
Local Wisdom as the Representation of Social Integration between Religions in Lombok Indonesia

Social Integration in Indonesia is often disturbed by religious conflict occurring in many areas. This research aimed to analyze local wisdom as the representation of social integration between religions in Lombok Indonesia. Bali ethnic Hindu and Sasak Ethnic adherents in Lombok were the unit of analysis in this qualitative research with case study approach. Data was collected through observation, in-depth interview, and documentation, and then was processed and analyzed using Parsons’ social integration theory. The result of research showed that Perang Topat was defined as the symbol of peace and Nampah Kaoq, fraternity and unity. They maintained social integration through such tradition as sign and cultural pattern representing social integration. This local wisdom realized harmony between religious communities. Keywords— local wisdom; representation; social integration; hindu; islamsasak
The Egorov property in perturbed cat maps

We study the time evolution of the quantum-classical correspondence (QCC) for the well-known model of quantized perturbed cat maps on the torus in the very specific regime of semi-classically small perturbations. The quality of the QCC is measured by the overlap of the classical phase-space density and corresponding Wigner function of the quantum system called quantum-classical fidelity (QCF). In the analysed regime the QCF strongly deviates from the known general behaviour discussed in Horvat et al (2006 Nonlinearity 19 1–23 (Preprint quant-ph/0601139)), in particular it decays faster than exponential. Here we study and explain the observed behaviour of the QCF and the apparent violation of the QCC principle.
On the evaluation of services selection algorithms in multi-service P2P grids

It has been shown that the use of a reciprocation mechanism in peer-to-peer grid systems which provide multiple services to their users is an efficient way to prevent free-riding and, at the same time, to promote the clustering of peers that have mutually profitable interactions. However, when peers are subject to resource limitations, they may be unable to offer all possible services and shall select a subset of services to offer. Previous work showed that the overall profitability of a peer is strongly dependent on the set of services it offers. Thus, the use of an appropriate services selection algorithm is crucial to yield better profitability to peers. Clearly, evaluating the efficiency of services selection algorithms is an important aspect in the search for suitable solutions for this problem. Unfortunately, due to the complexity and inherent non-determinism of the system, it is normally intractable to compute optimal solutions even for small systems. This renders the task of evaluating the performance of practical heuristic-based algorithms difficult. This work aims to fill in this gap by providing a cheaper evaluation method. The methodology we propose maps the services selection problem into the well-known knapsack problem, making brute force techniques affordable for reasonably large systems. Then, by immersing the algorithms under evaluation on a similar setting, it is possible to assess their efficiency compared to an optimal solution. We show how the methodology can be used by evaluating two services selection heuristics.
EMule Simulator: A Practical Way to Study the eMule System

Simulation is an effective way to study P2P systems. But for the popular file sharing software eMule, there is no eMule simulator to date. In this paper, we release the first eMule simulator, which simulates the internal mechanisms of the eMule software, such as requesting source list from server, publishing shared file list to server, connecting to other clients and requesting for uploading, credit and queue system, part selection algorithm, and data transfer between peers. Design and implementation of the simulator are presented in detail. Performance and memory efficiency of the simulator are discussed. Experiment results demonstrate that the simulator can model the internal behavior of the eMule network properly.