Miracle-dz/UniFAR-FTUs huggingface dataset:
Total Plasma Protein in Very Preterm Babies: Prognostic Value and Comparison with Illness Severity Scores

Objective We aimed to investigate the predictive value for severe adverse outcome of plasma protein measurements on day one of life in very preterm infants and to compare total plasma protein levels with the validated illness severity scores CRIB, CRIB-II, SNAP-II and SNAPPE-II, regarding their predictive ability for severe adverse outcome. Methods We analyzed a cohort of infants born at 24–31 weeks gestation, admitted to the tertiary intensive care unit of a university hospital over 10.5 years. The outcome measure was “severe adverse outcome” defined as death before discharge or severe neurological injury on cranial ultrasound. The adjusted odd ratio (aOR) and 95% confidence interval (95% CI) of severe adverse outcome for hypoproteinemia (total plasma protein level <40 g/L) was calculated by univariate and multivariate analyses. Calibration (Hosmer-Lemeshow goodness-of-fit) was performed and the predictive ability for severe adverse outcome was assessed for total plasma protein and compared with CRIB, CRIB-II, SNAP-II and SNAPPE-II, by calculating receiver operating characteristic (ROC) curves and their associated area under the curve (AUC). Results 761 infants were studied: 14.4% died and 4.1% survived with severe cerebral ultrasound findings. The aOR of severe adverse outcome for hypoproteinemia was 6.1 (95% CI 3.8–9.9). The rank order for variables, as assessed by AUCs and 95% CIs, in predicting outcome was: total plasma protein [0.849 (0.821–0.873)], SNAPPE-II [0.822 (0.792–0.848)], CRIB [0.821 (0.792–0.848)], SNAP-II [0.810 (0.780–0.837)] and CRIB-II [0.803 (0.772–0.830)]. Total plasma protein predicted severe adverse outcome significantly better than CRIB-II and SNAP-II (both p<0.05). Calibration for total plasma protein was very good. Conclusions Early hypoproteinemia has prognostic value for severe adverse outcome in very preterm, sick infants. Total plasma protein has a predictive performance comparable with CRIB and SNAPPE-II and greater than other validated severity scores.
Finger Placement and Hand Grasp during Smartphone Interaction

Smartphones are currently the most successful mobile devices. Through their touchscreens, they combine input and output in a single interface. A body of work investigated interaction beyond direct touch. In particular, previous work proposed using the device's rear as an interaction surface and the grip of the hands that hold the device as a means of input. While previous work provides a categorization of grip styles, a detailed understanding of the preferred fingers' position during different tasks is missing. This understanding is needed to develop ergonomic grasp-based and Back-of-Device interaction techniques. We report from a study to understand users' finger position during three representative tasks. We highlight the areas that are already covered by the users' hands while using the on-screen keyboard, reading a text, and watching a video. Furthermore, we present the position of each of the user's fingers during these tasks. From the results, we derive interaction possibilities from an ergonomic perspective.
Realization of wearable sensors-based human activity recognition with an augmented feature group

Feature extraction is a critical stage in human activity recognition. The information carried in features directly affects the classification performance. This paper explores a new group of features for activity recognition, which have not been broadly applied in previous works in this field. The newly introduced features are related to the attitude of the on-body devices, being extracted from both time-domain and frequency-domain. Based on the collected data, we implemented certain standard data mining techniques, e.g., the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm for feature selection, and Support Vector Machine (SVM) for classification, to evaluate the performance of the hypothesis. The comparison studies suggest the augmented features perform better than the commonly used features, giving a higher recognition accuracy of 93.46%. Exploring new features without adding more sensors, while improving the accuracy significantly, enables an efficient extraction of features from limited availability of sensors.
Standards and markets for university-originated organizational intelligence

The aim of this paper is to bring to discussion ways to diagnose university’s organizational intelligence and to put forward some ways of measuring it. The main steps pursued refer to defining and describing the organizational particularities of universities, which modulate in specific ways organizational intelligence strategies implementation, applying the organizational intelligence standards to universities, and examining the features of the intelligence markets. The manner in which the paradigm of the traditional university is being changed, and finally eliminated, by the social stimuli which claim for a different type of intelligence originating in universities and which are the beneficiaries of the new model of university, as an organization in-between – preserving its idiosyncratic position, but engaging in mutually profitable alliances, is an issue we address to.
The National Comorbidity Survey Replication (NCS‐R): background and aims

The National Comorbidity Survey Replication (NCS‐R) is a new nationally representative community household survey of the prevalence and correlates of mental disorders in the US. The NCS‐R was carried out a decade after the original NCS. The NCS‐R repeats many of the questions from the NCS and also expands the NCS questioning to include assessments based on the more recent Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) diagnostics system (American Psychiatric Association, 1994). The NCS‐R was designed to (1) investigate time trends and their correlates over the decade of the 1990s and (2) expand the assessment of the prevalence and correlates of mental disorders beyond the assessment in the baseline NCS in order to address a number of important substantive and methodological issues that were raised by the NCS. This paper presents a brief review of these aims. Copyright © 2004 Whurr Publishers Ltd.
Detecting Junctions in Photographs of Objects

Line drawings are inherently simple, yet retain most of the information of a full image. This suggest that line drawings, also called contour images, could be useful as an intermediate representation for computer understanding of images. However, extracting physically meaningful contours from photographs has proved to be difficult. Recently, there has been significant progress in detecting contours. As one example, the gPb algorithm (Arbeláez et al., 2010) uses local cues such as brightness, color, and texture, along with global cues that interpret the image as a whole in order to achieve high-performance contour detection. However, contour detectors perform poorly near junctions. Junctions are points where two or more contours meet, creating two or more regions near a single point. Contour detectors, including gPb, assume that contours occur at the meeting of only two regions. We find that this assumption breaks down near junctions, resulting in poor performance. This is unfortunate because the location of junctions and the orientation of the incident edges at a junction are useful in reasoning about the geometric relationships between contours, and thus reasoning about the shape of the object. These two observations, that junctions carry significant information and that con-
Detection of traffic light using structural information

Driver support systems using images are paid attention and various researches on recognizing and understanding the road environment have been done. If it is possible to detect and recognize traffic lights, it will give useful information to a driver to understand the road environment. In this paper, a method of detecting a traffic light in a scene image is proposed. Considering the structure of a traffic light, we propose a method for detecting a traffic light based on the Hough transform. Experimental results using images including a traffic light taken by a digital camera through a windshield verifies the effectiveness of the proposed method.
Transforming the energy system in Västra Götaland and Halland – linking short term actions to long term goals

This study analyzes pathways to meet EU, national and regional targets for CO2 emissions, energy efficiency and penetration of renewable energy in the Swedish part of the Kattegat-Skagerrak region (KASK-SE), i.e. more specifically in the counties of Vastra Gotaland (VGR) and Halland. Special focus is placed on four areas: The potential for energy savings in the building sector, energy savings and fuel shifting in the energy intensive industry, large-scale deployment of renewables in the electricity generation sector and greenhouse gas emission reductions in the transport sector. The energy savings are through the implementation of different energy efficiency measures.
Relational GIS and Remote Sensing Database System for Al-Salt Area, Jordan

Due to the importance of database systems, integration between two Geomatics sciences, GIS and Remote Sensing has been made in order to support and serve various sectors in Jordan. GIS has been used to create layers that can show decision makers in a simple, easy and flexible manner. Remote Sensing will be used in creating images of how Jordan is seen from space, which will give the users more information and an overview about the study area. Satellite images could be converted into digital image maps, using digitizing procedures. The features viewed in the original scene could be studied by using different remotely sensed techniques. Landsat-7 (ETM+) and SPOT images were implemented, in order to extract the information needed for the topography of the region, land cover areas, boundaries, drainage patterns, road networks, man-used areas, vegetated areas and many other features in Al-Salt areas. The validity and the appropriateness of GIS and Remote Sensing techniques, particularly data fusion of images were evaluated in relation to visualization.
Medical image illumination enhancement and sharpening by using stationary wavelet transform

Medical images captured by various devices have different illumination states based on chemicals used by patient prior to scanning. Consider a MRI image which has low contrast or is too bright, hence the experts cannot analysis that image due to poor representation of data in the image. In this paper we are proposing new medical image illumination enhancement and sharpening technique based on stationary wavelet transform which is addressing the aforementioned problem. The technique decomposes the input medical image into the four frequency subbands by using stationary wavelet transformation and enhances the illumination of the low-low subband image, and then it enhanced edges of image by adding the high frequency subbands to the image. The technique is compared with the conventional and state-of-art image illumination enhancement techniques such as histogram equalisation, local histogram equalisation, singular value equalisation, and discrete wavelet transform followed by singular value decomposition contrast enhancement techniques. The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques.
FNTF:First No-reference Then Full-reference image quality assessment using Dark Channel

It is an indispensable step to faithfully evaluate and control the perceptual quality of digital visual applications such as image compression, image restoration and multimedia streaming. In this paper, we propose an efficient and effective twostep framework named First No-reference Then Full-reference (FNTF), to evaluate different kinds of noise, and distinguish the quality of distorted image, using features Natural Scene Statistics of Dark Channel(NSSDC), Average Distance of Matched Keypoints(ADMK) and Dark Channel Similarity Deviation(DCSD) we proposed. Dark Channel is a kind of natural statistics based on the key observation - most local patches in images contain some pixels whose intensity are close to zero in at least one color channel. Features extracted from Dark Channel can greatly represent the pollution level of the image and the kind of noise it sufferd from. The average distance and dark channel similarity are sensitive to image distortions, while different local structures in a distorted image suffer different distance and degrees of similarity, respectively. This motivated us to explore global variation based local quality for overall image quality prediction. We find that our two-step framework can predict accurately perceptual image quality.
Division of Economics and Business Working Paper Series Energy efficiency and emissions intensity standards

In order to comply with the Environmental Protection Agency's (EPA) recently released regulations governing greenhouse gas emissions from power plants, states are given the option to implement rate-based emissions intensity standards for the power sector. One well-known consequence of rate-based emissions standards is that in addition to encouraging substitution towards less emissions-intensive sources, they also subsidize output, and thus are considered by economists to be inferior to a first-best solution. However, the exiting literature has not considered energy efficiency decisions within the framework of intensity standards. This omission is particularly problematic in the context of the power sector, where energy efficiency has been considered an important channel of cost-minimizing emissions reductions. To encourage end-use efficiency measures under an intensity standard EPA allows states to credit electricity savings as a means of complying with the rule by treating them as a form of zero-emissions output. In this paper we investigate the role of energy efficiency choices in rate-based emissions intensity standards. We show that when demand for energy services is perfectly inelastic, crediting efficiency measures can recover the first-best allocation. This approach extends the output subsidy in a traditional intensity standard to energy efficiency, thereby eliminating the distortion that favors electricity generation over energy efficiency. However, when demand for energy services exhibits some elasticity, crediting energy efficiency can no longer recover first-best. While crediting energy efficiency removes the relative distortion between energy generation and energy efficiency, it distorts the equilibrium level of both energy generation and energy efficiency via an energy services subsidy. Simulations calibrated to the electricity sector in Texas examine the above issues numerically, as well as explore the implications of alternative energy efficiency crediting schemes.
Green Energy and Delay Aware Downlink Power Control and User Association for Off-Grid Solar-Powered Base Stations

Cellular base stations (BSs) powered by renewable energy like solar power have emerged as a promising solution to address the issues of reducing the carbon footprint of the telecom industry as well as the operational cost associated with powering the BSs. This paper considers a network of off-grid solar-powered BSs and addresses two key challenges while operating them: first is avoiding energy outages and second is ensuring reliable quality of service (in terms of the network latency). In order to do so, the problem of minimizing the network latency given the constrained energy availability at the BSs is formulated. Unlike existing literature which have addressed this problem using user-association reconfiguration or BS on/off strategies, we address the problem by proposing an intelligent algorithm for allocating the harvested green energy over time, and green energy and delay aware downlink power control and user association. Using a real BS deployment scenario, we show the efficacy of our methodology and demonstrate its superior performance compared to existing benchmarks.
Hermitian codes in distributed storage systems with optimal error-correcting capacity

Maximum distance separable (MDS) erasure codes are widely used in distributed storage systems (DSS) for better storage efficiency and protection against Byzantine attacks. In this paper, we aim at enhancing the error-correction capacity of DSS in a hostile network. Firstly, we apply Hermitian code in DSS and presented a special placing mode for the encoded symbols. A reconstruction algorithm in error-free network is given. Next we show that the burst-error-correcting algorithm by Ren can correct more errors than Reed-Solomon code. We proposed an erasure rollback strategy in decoding. The new reconstructing algorithm improves both the lower and upper bound of error-correcting capacity. It has better computing complexity than Reed-Solomon code with the same storage efficiency.
Extracting Structural Image Contents

Images have been readily acquired through prevalent, available digital cameras. We could identify and differentiate each image from other ones because of our recognition and past experiences. A computer, on the other hand, has to be trained so that it could recognize things and infer from rules and facts. Ability in recognition of the computer at our level could be a few decades away if not longer. This paper, therefore, proposes an approach to extract structural contents of the image and represent them in efficient forms of B-splines and directional chain codes. Image is first segmented into bounded regions whose boundaries are then approximated by the B-splines. Control points of those B-splines are used to approximate elliptical representations of the regions. In addition, the Chaikin's algorithm is recursively applied to the control points a few times in order to get more accurate directional chain codes, which could later be used to represent and compared to their counterparts of other images. Consequently, these extracted features could be used for representation, comparison, and retrieval. Moreover, we could use these features to train the computer for recognizing and differentiating images in hope that it would have a cognitive capability comparatively close to us.
Directors: Myth and Reality

Under a title which I feel belongs to my friend and colleague Professor Myles Mace of the Harvard Business School,1 I am called upon to introduce three different views of boards of directors; the general public's, the business community's and the legal world's. Which of these represents myth and which reality is a question I prefer to postpone indefinitely. The questions raised in this introduction are basic ones that will be with us for some time; technical questions of directors' responsibilities should be approached in the light of these fundamental concerns. The practitioner should also keep his eye on proposed basic changes in the corporate management system which arise from them and are being put forth with increasing vigor.
Study on the Architecture of Platform for Internet of Things Service Based on EDSOA

This paper proposes architecture for Internet of Things service based on EDSOA. This architecture can fulfil the processing of perception flow information in a large scale, it can also support the mode of Internet of Things service which can make an interaction of business flow, control flow and perception flow. It has the advantages of loose coupling, business supporting and high ability of being integrated. This kind of architecture is helpful for industry developing of business, cooperative work, composition service and automation of business process. This architecture can be used for ease of management and monitoring.
Interannual variability in responses of belowground net primary productivity (NPP) and NPP partitioning to long‐term warming and clipping in a tallgrass prairie

The dynamics of belowground net primary productivity (BNPP) is of fundamental importance in understanding carbon (C) allocation and storage in grasslands. However, our knowledge of the interannual variability in response of BNPP to ongoing global warming is limited. In this study, we explored temporal responses of BNPP and net primary productivity (NPP) partitioning to warming and clipping in a tallgrass prairie in Oklahoma, USA. Infrared heaters were used to elevate soil temperature by approximately 2 °C since November 1999. Annual clipping was to mimic hay harvest. On average from 2005 to 2009, warming increased BNPP by 41.89% in the unclipped subplots and 66.93% in the clipped subplots, with significant increase observed in wet years. Clipping also had significant positive impact on BNPP, which was mostly found under warming. Overall, fBNPP, the fraction of BNPP to NPP, increased under both warming and clipping treatments, more in dry years. Water availability (either precipitation or soil moisture) was the most limiting factor for both BNPP and fBNPP. It strongly dominated the interannual variability in NPP, fBNPP, and their responses to warming and clipping. Our results suggest that water availability regulates tallgrass prairie's responses to warming and land use change, which may eventually influence the global C cycle. With increasing variability in future precipitation patterns, warming effects on the vegetation in this region may become less predictable.
Annexin A8 is a novel molecular marker for detecting lymph node metastasis in oral squamous cell carcinoma

Cervical lymph node metastasis is an important prognostic factor in oral squamous cell carcinoma (OSCC), but its accurate assessment after sentinel node biopsy or neck dissection is often limited to the histopathological examination of only one or two sections. Previous our study showed the usefulness of the reverse transcription loop-mediated isothermal amplification (RT-LAMP) targeting keratin 19 (KRT19) mRNA for the genetic detection of lymph node metastasis, but the sensitivity was insufficient. Here, we have attempted to identify novel molecular markers for OSCC cells in lymph nodes. We performed microarray analysis to identify genes overexpressed in 7 metastatic lymph nodes from OSCC patients, compared to 1 normal lymph node and 5 salivary glands from non-cancer patients. We then used real-time quantitative RT-PCR (qRT-PCR) and RT-LAMP to compare the expression of these genes in newly resected metastatic and normal lymph nodes. Of 4 genes identified by microarray analysis, annexin A8 (ANXA8) and desmoglein 3 mRNA were detected by qRT-PCR in metastatic lymph nodes but not in normal lymph nodes. Furthermore, ANXA8 mRNA expression was detected in all KRT19-negative metastatic lymph nodes. Both KRT19 and ANXA8 mRNA may be useful markers for detecting lymph node metastases in OSCC patients.