Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Fabrication of Soft, Multilayered Electronics for Wearable Biomonitoring

Soft integrated electronics are key components for emerging applications in wearable biomonitoring, soft co‐robotics, and physical human–machine interaction. They are composed of soft and elastically deformable circuits and sensors that are combined with packaged microelectronics for signal processing, power regulation, and communication. While promising, widespread use of soft wearable electronics is currently limited by the lack of robust fabrication techniques to rapidly, efficiently, and precisely assemble soft and rigid components into multilayered systems. Here, an efficient digital fabrication approach is presented to create highly customizable wearable electronics through rapid laser machining and adhesion controlled soft materials assembly. Well aligned, multilayered materials are created from 2D and 3D elements that stretch and bend while seamlessly integrating with rigid components such as microchip integrated circuits, discrete electrical components, and interconnects. These techniques are applied using commercially available materials and components and the fabrication of thin, lightweight, customized sensor skins is demonstrated in under an hour. These fully integrated wireless devices conformably bond to the hand and are successfully used for monitoring hand gesture, pulse rate, and blood oxygenation. These materials and methods enable custom wearable electronics while offering versatility in design and functionality for a variety of applications through material selection and construction.
Automated multiple lesion identification on vertebral spine using modified average intensity

X-ray images of vertebral spine are important to analyze vertebral diseases such as osteoporosis, osteopenia and scoliosis. Vertebral Spine lesion affects the diagnosing of bone disease such that the lesion might affect bone mineral density calculation that used to predict osteoporosis. In this respect, the accuracy of identifying lesion from these images is essential. However, some lesion detecting may have low accuracy because of the quality of the acquired images. In this research, we propose a new approach to identify lesion and its position. In our proposed method, there are five main steps. Firstly, Auto Crop, we segment vertebral spine area using Vertical Projection and Normal Distribution. Secondly, Boundary Segmentation, we use Gamma Correction and Distance Regularized Level Set Evolution to detect vertebral spine boundary. Thirdly, Vertebral Pose Estimation, we identify vertebral pose by applying the technique on locating the middle point of a couple of graph peaks as the border line to divide among each pose. Fourthly, Locating Thoracic Bone 12 and Lumbar Bone 5, we use linear equation to localize position of colliding between left or right rib and the vertebral spine to identify Thoracic Bone 12. Likewise, we detect vertebral bone Lumbar Bone 5 by applying the technique to find the minimum distance between position of top hip and vertebral poses. Finally, Identifying Bone Lesion, we use average intensity. From the experimental results, we found that the accuracy performance of our approach is 82.50% for Thoracic Bone 12 and 76.25% for Lumbar Bone 5 compared to ground truth. Moreover, the accuracy performance of bone lesion 61.25%.
Alcohol Consumption, Folate Intake, Hepatocellular Carcinoma, and Liver Disease Mortality

Background: Excessive alcohol consumption is a well-established risk factor for liver disease and hepatocellular carcinoma (HCC). Previous studies have found that increased alcohol consumption can lead to lower absorption of folate. Conversely, higher folate intake has been inversely associated with liver damage and HCC. In the current study, we investigate the effect of alcohol consumption and folate intake on HCC incidence and liver disease mortality in the NIH–American Association of Retired Persons Diet and Health Study. Methods: The study population included 494,743 participants who reported at baseline their dietary intake for the previous year. Alcohol and folate were analyzed with hazards ratios (HR) and 95% confidence intervals (CI) using multivariate Cox proportional hazards regression models adjusted for age, sex, race, education, smoking, body mass index, and diabetes. HCC incidence (n = 435) was determined through 2006 via linkage with cancer registries, and liver disease mortality (n = 789) was determined through 2008 via linkage to the U.S. Social Security Administration Death Master File and the National Death Index Plus by the National Center for Health Statistics. Results: Consumption of more than three drinks per day was positively associated with both HCC incidence (HR: 1.92; 95%CI: 1.42–2.60) and liver disease mortality (HR: 5.84; 95%CI: 4.81–7.10), whereas folate intake was associated with neither outcome. Folate, however, modified the relationship between alcohol and HCC incidence (Pinteraction = 0.03), but had no effect on the relationship between alcohol and liver disease mortality (Pinteraction = 0.54). Conclusions: These results suggest that higher folate intake may ameliorate the effect of alcohol consumption on the development of HCC. Impact: Folate intake may be beneficial in the prevention of alcohol-associated HCC. Cancer Epidemiol Biomarkers Prev; 22(3); 415–21. ©2013 AACR.
Reframing Narrative Cases for Ill-Structured Contexts: The Design with Learners in Mind.

This study reviews theories supporting the construction of student knowledge and found they rely upon theoretical operations with few pragmatic applications. A new paradigm of learning needs to emerge which focuses on how students actually construct their own knowledge, based on reconstructing current/past knowledge or experience, and their interpretation of the world. Such a paradigm implies ways that would enable educators to facilitate students’ problem solving, and gears to help students construct hypotheses, select and transform information, and finally make decisions. Implications for designing effective narrative cases for ill-structured problem solving are discussed.
Voting May Be Habit-Forming: Evidence from a Randomized Field Experiment

Habit is a frequently mentioned but understudied cause of political action. This article provides the first direct test of the hypothesis that casting a ballot in one election increases one's propensity to go to the polls in the future. A field experiment involving 25,200 registered voters was conducted prior to the November general election of 1998. Subjects were randomly assigned to treatment conditions in which they were urged to vote through direct mail or face-to-face canvassing. Compared to a control group that received no contact, the treatment groups were significantly more likely to vote in 1998. The treatment groups were also significantly more likely to vote in local elections held in November of 1999. After deriving a statistical estimator to isolate the effect of habit, we find that, ceteris paribus, voting in one election substantially increases the likelihood of voting in the future. Indeed, the influence of past voting exceeds the effects of age and education reported in previous studies.
optimizations for FPGA-based Sparse

— Performance of FPGA-based token dataﬂow architectures is often limited by the long tail distribution of parallelism in the compute paths of dataﬂow graphs. This is known to limit speedup of dataﬂow processing of Sparse LU factorization to only 3– 10 × over CPUs. In this paper, we show how to overcome these limitations by exploiting criticality information along compute paths; both statically during graph pre-processing and dynamically at runtime. We statically restructure the high-fanin dataﬂow chains using a technique inspired by Huffman encoding where we provide faster routes for late arriving inputs as predicted through our timing models. We also perform a fanout decomposition and selective node replication in order to distribute serialization costs across multiple PEs. This static restructuring overhead is small; roughly the cost of a single iteration, and is amortized across 1000s of LU iterations at runtime. Additionally, we modify the dataﬂow ﬁring rule in hardware to prefer critical nodes when multiple nodes are ready for dataﬂow evaluation. We compute this criticality ofﬂine through a one-time slack analysis and implement this in hardware at virtually no cost through a trivial address encoding ordered by criticality. For dataﬂow graphs extracted for sparse LU factorization, we demonstrate up to 2.5 × (mean 1.21 × ) improvement when using the static pre-processing alone, a 2.4 × (mean 1.17 × ) improvement when using only runtime optimizations alone while an overall 2.9 × (mean 1.39 × ) improvement when both static and runtime optimizations are enabled across a range of benchmark problems.
Just Blowing Smoke? Residents’ Social Construction of Communication about Wildfire

This study uses social constructionism as a basis for understanding the effectiveness of communication about wildfire risk between agency officials and wildland–urban interface (WUI) residents. Risk communication literature demonstrates a well-documented difference in the way land managers and stakeholders conceptualize risk. This is especially true of fire because management of these hazards have changed so drastically in past decades; fire managers have typically struggled to clearly articulate the current management policy to the public or integrate their specific knowledge in the risk management process. This study contributes to an understanding of how WUI residents construct communication about wildland fire and agency effectiveness in communicating the new era of fire inclusion. Specifically, we explore the personal and professional sources of information residents’ use to understand their fire risk and the subjects they would like more information about. We also explore the continued viability of Smokey Bear, the most enduring symbol of fire management.
Context-sensitive ranking

We are witnessing a growing number of applications that involve both structured data and unstructured data. A simple example is academic citations : while the citation's content is unstructured text, the citation is associated with structured data such as author list, categories and publication time. To query such hybrid data, a natural approach is to combine structured queries with keyword search. Two fundamental problems arise for this unique marriage : (1) How to evaluate hybrid queries efficiently? (2) How to model relevance ranking? The second problem is especially difficult, because all the foundations of relevance ranking in information retrieval are built on unstructured text and no structures are considered. We present context-sensitive ranking, a ranking framework that integrates structured queries and relevance ranking. The key insight is that structured queries provide expressive search contexts. The ranking model collects keyword statistics in the contexts and feeds them into conventional ranking formulas to compute ranking scores. The query evaluation challenge is the computation of keyword statistics at runtime, which involves expensive online aggregations. At the core of our solution to overcome the efficiency issue is an innovative reduction from computing keyword statistics to answering aggregation queries. Many statistics, such as document frequency, require aggregations over the data space returned by the structured query. This is analogous to analytical queries in OLAP applications, which involve a large number of aggregations. We leverage and extend the materialized view research in OLAP to deliver algorithms and data structures that evaluate context-sensitive ranking efficiently
Growing Up with Dyslexia

Interviews with 75 teenagers and young adults were performed to investigate how young people with dyslexia experienced school in terms of well-being, educational achievement, self-esteem, peer relations and belief in their future. Results from earlier studies suggest that secondary emotional problems are common. The first six grades in school were experienced by the interviewees as full of distress and failure for a majority. Though peer relations were often good, many had experienced bullying. As they grew older, problems were more limited to reading and writing activities. This was thought to be an effect of acknowledgement and compartmentalization of the disability along with choices of school curricula and occupations in line with subjects' talents and capacities. Academic self-esteem seemed low, and most subjects had chosen vocational programmes in secondary school and had decided not to go to college. The most optimistic subjects were those who had finished school and were permanently employed. Early diagnosis along with careful explanation of the disability was recommended as well as the encouragement of dyslexic children in areas where they can do well and which makes them view themselves positively.
LoTrust: A social Trust Level model based on time-aware social interactions and interests similarity

With the immense growth of online social applications, trust plays a more and more important role in connecting users to each other, sharing their personal information and attracting him to receive recommendations. Therefore, how to obtain trust relationships through mining online social networks became a critical issue. To calculate the level of trust between two users, many computational trust models are proposed which mainly rely on the social network structure, the explicit trust from user to another, the users' behaviors, or the users' similarity, etc. However, the majority of these models ignored the temporal factor. In this paper, we propose a trust relationship detection mechanism from an egocentric social network in order to compute the trust level between an active user and his directed friends. We propose a Level of social Trust model, that we called LoTrust, which is suitable for personalized recommendation purpose. This computational model founded on novel trust metric which is based not only on the users' interests similarity according to their semantic social profiles (RDF/FOAF), but also takes into account the time factor of the users' active interactions (e.g comments, share photo, wall posts, messages). We perform experiments on real life dataset extracted from Facebook. The experimental results demonstrated how our LoTrust model produces satisfactory results than other computational models.
Activation by SLAM Family Receptors Contributes to NK Cell Mediated “Missing-Self” Recognition

Natural Killer (NK) cells attack normal hematopoietic cells that do not express inhibitory MHC class I (MHC-I) molecules, but the ligands that activate NK cells remain incompletely defined. Here we show that the expression of the Signaling Lymphocyte Activation Molecule (SLAM) family members CD48 and Ly9 (CD229) by MHC-I-deficient tumor cells significantly contributes to NK cell activation. When NK cells develop in the presence of T cells or B cells that lack inhibitory MHC-I but express activating CD48 and Ly9 ligands, the NK cells’ ability to respond to MHC-I-deficient tumor cells is severely compromised. In this situation, NK cells express normal levels of the corresponding activation receptors 2B4 (CD244) and Ly9 but these receptors are non-functional. This provides a partial explanation for the tolerance of NK cells to MHC-I-deficient cells in vivo. Activating signaling via 2B4 is restored when MHC-I-deficient T cells are removed, indicating that interactions with MHC-I-deficient T cells dominantly, but not permanently, impair the function of the 2B4 NK cell activation receptor. These data identify an important role of SLAM family receptors for NK cell mediated “missing-self” reactivity and suggest that NK cell tolerance in MHC-I mosaic mice is in part explained by an acquired dysfunction of SLAM family receptors.
A universal method for designing low-power carbon nanotube FET-based multiple-valued logic circuits

This study presents new low-power multiple-valued logic (MVL) circuits for nanoelectronics. These carbon nanotube field effect transistor (FET) (CNTFET)-based MVL circuits are designed based on the unique characteristics of the CNTFET device such as the capability of setting the desired threshold voltages by adopting correct diameters for the nanotubes as well as the same carrier mobility for the P- and N-type devices. These characteristics make CNTFETs very suitable for designing high-performance multiple- V th circuits. The proposed MVL circuits are designed based on the conventional CMOS architecture and by utilising inherently binary gates. Moreover, each of the proposed CNTFET-based ternary circuits includes all the possible types of ternary logic, that is, negative, positive and standard, in one structure. The method proposed in this study is a universal technique for designing MVL logic circuits with any arbitrary number of logic levels, without static power dissipation. The results of the simulations, conducted using Synopsys HSPICE with 32 nm-CNTFET technology, demonstrate improvements in terms of power consumption, energy efficiency, robustness and specifically static power dissipation with respect to the other state-of-the-art ternary and quaternary circuits.
Fetal magnetic resonance imaging of normal spinal cord: Evaluating cord visualization and conus medullaris position by T2-weighted sequences

Background: Prenatal magnetic resonance (MR) imaging demonstration of the normal spinal cord and the conus medullaris location has not been well studied. We compared balanced fast field echo (bFFE) with single-shot turbo spin-echo (SSh-TSE) MR sequences for visualizing the normal spinal cord and position of conus medullaris in fetuses. Methods: This retrospective study was approved by the Institutional Review Board of Chang Gung Medical Foundation. We reviewed the MR images of 141 fetuses aged between 16 and 39 gestational weeks, to determine the position of the conus and visualize the spinal cord by using a signal intensity ratio of cerebral spinal fluid (CSF) to the spinal cord. Results: Of the 75 subjects having normal spinal cord and being examined by both bFFE and SSh-TSE studies, the signal intensity ratio of CSF/cord was greater on bFFE images (2.18 ± 0.53) than that on SSh-TSE images (1.21 ± 0.13) (p < 0.05). The conus level identified in the 50 subjects, in whom the lumbosacral spine was appropriately imaged, was located from L1 to L5 levels. The ascendance of the conus correlated moderately with gestational age. Conclusions: With greater signal contrast ratio of CSF to spinal cord, bFFE sequence, when compared with SSh-TSE sequence, provides better visualization of normal spinal cord. The fetal conus medullaris ascends from L5 to L1 levels as the gestational age increases.
Brain-Computer Interfacing for Intelligent Systems

Advances in cognitive neurosci- ence and brain-imaging technologies give us the unprecedented ability to interface directly with brain activity. These technologies let us monitor the physical processes in the brain that correspond with certain forms of thought. Driven by society's growing recognition of the needs of people with physical disabilities, researchers have begun using these technologies to build brain-computer interfaces (BCIs)communication systems that don't depend on the brain's normal output pathways of peripheral nerves and muscles. In BCIs, users explicitly manipulate their brain activity instead of motor movements to produce signals that control computers or communication devices. This research has extremely high impact, especially for disabled individuals who can't otherwise physically communicate.
Serum prohepcidin reflects the degree of liver function impairment in liver cirrhosis

Abstract In the past few years the role of hepcidin metabolism disturbances, a recently described key regulator of iron metabolism, has been raised in patients with chronic liver diseases. The aim of this study was to assess the serum concentrations of prohepcidin in liver cirrhosis of various aetiologies and their possible relationship with the disease activity. Prohepcidin was measured in the sera of 70 patients with liver cirrhosis of various aetiologies by an immunoassay technique. The serum concentrations of prohepcidin were compared with the degree of liver insufficiency and biochemical markers of iron metabolism. A significant decrease in serum prohepcidin was observed in patients with liver cirrhosis compared with healthy individuals (52.6±1.9 vs 79.5±9.7 ng ml−1, p<0.01); this was most prominent in patients with hepatitis C virus and alcohol-related liver cirrhosis. The association between serum prohepcidin and the degree of liver dysfunction was observed in alcoholic liver cirrhosis, as illustrated through the inverse correlation with the Child–Pugh score (r= − 0.41, p<0.01). In conclusion, serum prohepcidin concentration is lowered in liver cirrhosis, which at least to some extent, may be a result of impaired liver function.
Risk Management Analysis in Software Projects which Use the Scrum Framework

Studies show one of the reasons for the failure of the software projects is the absence of Risk Management or its improper application. The adoption of Scrum Framework on software projects is increasing. However, the Scrum does not have specific Risk Management activities. In this scenario, this paper presents the results of a survey applied using qualitative approach, in order to analyze how Risk Management is carried out in software projects, which use Scrum. The research method adopted was the case study and the research instrument for data collection was developed based on scientific articles and the application of structured interviews. As a result, this paper presents Risk Management practices which achieved greater and lower agreement among respondents and literature. It was found that Risk Management must be applied continuously in a feedback loop. Furthermore, Scrum projects must not have a high formal planning level, even for the high risk ones. This result does not converge to the literature. The research verified that the Risk Management in Scrum projects is performed differently from its application on traditional methodologies. The framework has native resources, but the use of the classic Risk Management processes must be incorporated and adapted to Scrum.
The predictive validity of the team diagnostic survey

Purpose – The aims of the present study were to test the predictive validity of the Swedish version of the Team Diagnostic Survey (TDS). Design/methodology/approach – A model with both performance and satisfaction was tested with structural equation model (SEM) analyses. Participants completing the survey were employees (N = 214) across three large workplaces. Analyses were done at the group level and data from 33 teams were included in the final data material. Findings – Results from validation data indicate that the TDS has satisfactory high Cronbach’s alpha values on most factors. Results from the SEM analyses show a moderate model fit for the main model. Team-level factors predict both performance and satisfaction, while organization and coaching factors do not. Research limitations/implications – The present study was limited to a cross-sectional design, but earlier studies have shown that the accuracy of the TDS remains consistent over time. The main purpose of this study was to test the predictive .