Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparisons of internal fixation treatments for femoral intertrochanteric fractures : a network meta-analysis

Objective: To identify the optimal treatment of femoral intertrochanteric fracture through multiple comparisons of gamma nail (GN), sliding hip screw (SHS), proximal femoral nail (PFN), proximal femoral nail antirotation (PFNA), percutaneous compression plate (PCCP), and Targon proximal femoral (Targon PF). Methods: We searched the Embase and PubMed databases in accordance with inclusion and exclusion criteria. Quality assessment was performed using the recommendation of the Cochrane Collaboration. All outcomes were assessed using odds ratio, standardized mean difference, and 95% confidence interval. The random-effects model was used to examine all the outcomes. Node-splitting analysis and the Brooks-Gelman-Rubin method were applied for consistency test and convergence assessment, respectively. The Aggregate Data Drug Information System was used for the statistical analysis. Results: In total, 31 eligible studies were included. The multiple comparisons indicated that PFNA for blood loss and non-union, PCCP for operative time and mortality, SHS for embolism, intraoperative fracture and later fracture, and Targon PF for reoperation and wound infection were the optimal treatments. Rank probability showed that SHS was the best option for blood loss, embolism, intraoperative fracture, and later fracture; and Targon PF, for operative time, mortality, non-union, reoperation, and wound infection. The subgroup analysis revealed that blood loss, cut-out incidence, and wound infection were lowest and the operative time was shortest with the PFN. Conclusion: No optimal internal fixation treatment was identified for femoral intertrochanteric fracture, but PFN may be a better treatment option for unstable femoral intertrochanteric fractures.
Poverty and School Readiness : Implications to Early Childhood Development in Zimbabwe

The concept of school readiness is multi-faceted, encompassing the holistic development of the learner, physical health, social-emotional, cognitive and linguistic status of children. Children from poverty stricken households in Zimbabwe often start school at a disadvantage. This paper examines the reasons why poor children are less ready for school and evaluates intervention measures for improving their school readiness. This study is informed by Urie Bronfenbrenner’s ecological theory. A qualitative phenomenological design was used with face to face interviews as data-collection instruments, to purposively selected eight (8) teachers in two ECD centres in Masvingo, Urban. This study established that poverty decreases a child’s readiness for school through aspects of, poor health and nutrition, financial constraints, neglect, parental level of education, stressful living conditions, low-quality preschools and low attachment to primary caregivers. School readiness gaps are further widened by unavailability of play materials at home and at school. On the way forward, parents, educational policymakers, Non-Governmental Organisation and the general public should recognise the importance of the first few years in the life of a child for promoting healthy physical, emotional, social, and intellectual development. High-quality early interventions should also be designed to combat the negative factors that threaten child development. Collaborative work is also needed among Zimbabweans and all stakeholders to revisit the root causes of poverty and poor academic performance among ECD B learners.
Bayesian Calibration for Transient Energy Simulation Model

Building simulation has become increasingly important because of its capability to assess potential energy efficiency savings in buildings. In the simulation modellingprocess, many assumptions and simplifications are required and many uncertain inputs are also involved. The aforementioned issues may cause a significant discrepancy between a reality and prediction. With this in mind, this paper investigatesapplicability of a Bayesian calibration technique to a transient simulation model. This will improve prediction accuracy and reduce uncertainty. The Bayesian calibration is a way to estimate the posterior distribution based on the quantified prior distribution.For sampling uncertain inputs, MCMC(Markov Chain Monte Carlo) method was applied in our study. One of the MCMC methods, DRAM(Delayed Rejection Adaptive Metropolis) was employed. This paper addresses Bayesian calibration, uncertainty analysis, and risk analysis for whole-building building energy simulation modelling(EnergyPlus). For this study, an office building was selected and 28 unknown inputs were identified. The Bayesian calibration was conducted in three steps: (1) determination of prior probability distributions for uncertain inputs, (2) formulation of likelihood functions,and (3)MCMC method for posterior distributions. Lastly, Coefficient of Variation of the Root Mean Squared Error (CVRMSE, ASHRAE Guide line14) was used for the validation of the approach. In the paper, the following is discussed: (1) posterior distributions of inputs against their prior distributions, (2)results of Bayesiancalibration for the model, and (3) advantages of Bayesian calibration for dynamic simulation model.
Peripheral Blood Smudge Cells Percentage in De Novo CLL : A Comparison with Other Established Laboratory Prognostic Markers

Chronic lymphocytic leukemia (CLL) is the most common form of leukemia in the Western world. The timing as to when a patient will require treatment is highly unpredictable. Thus, there has been great interest in identifying prognostic markers that can be used to distinguish patients who may have an aggressive form of CLL and might benefit from early intervention. Recently developed molecular markers are costly and often require a high level of technological expertise. Recent data give evidence for the prognostic relevance of peripheral blood smudge cells percentage in CLL. In our study, we investigated the prognostic potential of smudge cell percentage in 180 de novo CLL patients referring to the National Cancer Institute, Cairo University, Egypt and correlated the smudge cell percentage with established prognostic markers; including age, sex, ZAP 70 and CD 38 expression, pattern of marrow infiltration, Beta-2 microglobulin, Lactate dehydrogenase and lymphocyte doubling time. Our results showed that smudge cells percentage correlated inversely with markers of bad prognosis and correlated positively with hemoglobin and lymphocyte doubling time, which confer a better prognosis. We concluded that peripheral blood smudge cells percentage could be used as a simple, inexpensive and independent prognostic marker that can predict the outcome and survival in de novo CLL patients. [Zaher, Amr and Elgamal, Basma. Peripheral Blood Smudge Cells Percentage in De Novo CLL: A Comparison with Other Established Laboratory Prognostic Markers] Life Science Journal. 2011; 8(4):239-244] (ISSN: 1097-8135). http://www.lifesciencesite. com.
TOTAL-F GYROKINETIC NEOCLASSICAL SIMULATION OF GLOBAL IMPURITY TRANSPORT AND ITS EFFECT ON THE MAIN-PLASMA CONFINEMENT

The gyrokinetic neoclassical code XGCa is used to study the baseline transport physics of model wholevolume JET H-mode plasmas under tungsten contamination. Unlike other studies, separatrix and X-loss physics, non-Maxwellian pedestal, and electric field are calculated self-consistently. The Tungsten charge number Z is assumed to be fixed without the atomic interaction with the background plasma. The initial fraction of the tungsten charge-density profile is assumed to be 1% of the electron charge-density profile with the tungsten fraction relative to the electron density 2.5 × 10−4, which is a non-dominant but still non-negligible fraction. It is found that the low-Z tungsten particles at Z=6 can penetrate into the core of the plasma across the separatrix surface and through the pedestal top. On the other hand, the high Z tungsten particles at Z=40 suffers from a screening effect from the core plasma and moves radially outward toward the pedestal top. Across the separatrix surface and the steep pedestal region, the Z=40 tungsten particles do not suffer from a screening effect and are transported inward toward the pedestal top. As a result, Z=40 tungsten particles accumulates at the pedestal top, disagreeing with experimental observations and calling for the addition of turbulent transport. Another interesting finding is that the tungsten particles degrade the background energy confinement, even when the high-Z tungsten is transported outward hence the main ions move inward. The conductive outward heat transport of the main plasma is stronger than the convective inward energy transport.
- 1-Staggered spin-orbit coupling order : A new paradigm of broken symmetry phases of matter

We propose a novel spin-orbit coupling density wave order which can arise in variety of material classes. In systems where the non-interacting wavefunctions are defined by exotic quantum number such as total angular momentum, pseudospin or helical quantum number owing to spin-orbit coupling of various natures, interaction can induce emergent spin-orbit coupling density wave even when time-reversal symmetry is intact. This density wave order is different from standard time-reversal breaking spinor orbital density wave. We apply this idea to explain the enigmatic ‘hidden order’ phase in heavy fermion URu2Si2 as well as an unknown gapped quasiparticle state observed in two-dimensional electron gases, such as surface state of BiAg2. Introduction The formation of order parameter, due to either ‘spontaneous’ broken symmetry or symmetry invariant, leads to new phases of matter. In a broken symmetry state, the emerging order parameter relies incipiently on the good quantum number that defines the ‘parent Hamiltonian’ or the Hamiltonian of the non-ordered state. Among the mostly studied broken symmetry states, we have spin-density wave, charge density wave, orbital density wave, superconductivity in which the broken symmetries are spin, charge, orbital of the electrons, or gauge symmetry, respectively. [1] Recently, it is realized both theoretically and experimentally that the symmetry properties of the
Hardware Automated Data ow Deployment of CNNs Technical Report Haddoc / 2016-06 TR 03

Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classi cation and scene understating. However, such techniques are computationally intensive and involve highly regular parallel computation. CNNs can thus bene t from a signi cant acceleration in execution time when running on ne grain programmable logic devices. As a consequence, several studies have proposed FPGAbased accelerators for CNNs. However, because of the huge amount of the required hardware resources, none of these studies directly was based on a direct mapping of the CNN computing elements onto the FPGA physical resources. In this work, we demonstrate the feasibility of this so-called direct hardware mapping approach and discuss several associated implementation issues. As a proof of concept, we introduce the haddoc2 open source tool, that is able to automatically transform a CNN description into a platform independent hardware description for FPGA implementation.
CHEMICAL COMPOSITION AND QUALITY CHARACTERISTICS OF ENSILED MAIZE

Maize stover (MS), an abundant crop residue is a potential feed for ruminants. The nutritive value is reduced when left on the field without processing and preservation due to further lignification. The ensiling of MS without additives for preservation has not been adequately documented. Therefore, effect of non-additives on silage quality characteristics and chemical composition of ensiled maize stover was investigated. Maize stover were ensiled for 30 days using different additives (Molasses, honey and sugar) at the rate of 50gkg to obtain the following silages: Maize stover and Molasses (MSM), Maize stover and Honey (MSH), Maize stover and Sugar (MSS) and Maize stover only (Control) which served as treatments. The pH, moisture, colour, odour and texture silage characteristics were determined using standard procedures. Dry matter (DM), Crude protein (CP), ash, Crude fibre (CF), Ether extract (EE), Nitrogen free extract (NFE), Neutral detergent fibre, Acid detergent fibre, Acid detergent lignin, Hemicellulose and Cellulose of the ensiled MS were determined using standard techniques. The greenish-brown, greenish-yellow colour, alcoholic, fruity and pleasant odour, firm texture pH (3.5 – 3.7) and moisture (64.7 – 68.7%) were similar among the silages. The DM (31.6 – 35.3%), CP(7.9 – 8.9%), CF(30.0 – 31.9%), EE (1.4 – 1.8%), NDF (68.6 – 69.9%), ADF (56.1– 63.2%) and ADL (14.0 – 16.8%) were not significantly affected by the additives. However, ash composition, NFE, and cellulose were significantly (P < 0.05) affected by the additives. This investigationdemonstrated that addition of additives to MS stover had no significant effect on silage quality characteristics and chemical composition compared to control silage. Consequently, maize stover can be ensiled without additive and without significant reduction in nutrient composition and silage quality characteristics.
How well does your encounter-based application disseminate information?

In real-world, humans exhibit heterogeneous mobility patterns and are typically interested to receive context-based localized information originating from different data sources. Current works focus on sequential single pair-wise contacts among people under homogeneous mobility and single data source. This restricted evaluation for only single-scenarios do not collectively consider real-world mobility aspects of data dissemination. This approach based on narrow mobility aspects is mainly imposed due to intrinsic complexity of real-world. Deploying real experiments or simulating these real-world scenarios can be exhaustive and impractical. To include real-world mobility aspects and allow the understanding of scenarios bounds, we present a Markov model that collectively considers them to predict the performance of data dissemination. Our model allows multiple simultaneous pair-wise contacts among people under heterogeneous mobility and predicts the upper bound of data dissemination time. We achieve tighter upper bound of dissemination time than existing approaches by utilizing the long tail cut-off property of inter contact time distribution and data gathering process. We validate our model through different real-world traces from diverse environments and obtained the upper bound of data dissemination time with 5-10% error. We believe our model allows the pre-deployment performance analysis of encounter-based applications.
An Analysis of Lamb Wave Propagation Characteristics on Composite Materials with Multi-Stepped Laminates

Structural health monitoring(SHM) using lamb wave has been actively studied due to uniform group velocity and propagation over a wide area. The study on the structural health monitoring of composite materials using lamb waves has mainly been carried out on a flat plate having a constant thickness, but the results of a multi-layer structures such as a fan blade are less studied. This paper presents the characteristics of lamb wave propagation on composites with multi-stepped layer. The group velocity and dispersion curve with various layer thickness is predicted using a finite element analysis. Also, comparing the general composite plate, we analysed the influences of multi-stepped variation about the propagation characteristics of lamb wave.
Heuristic Interpretation of Predicate Logic Expressions in General Game Playing

Unlike traditional game playing, General Game Playing (GGP) is concerned with agents capable of playing classes of games. Given the rules of an unknown game, the agent is supposed to play well without human intervention. For this purpose, agent systems using game tree search need to automatically construct a state value function to guide search. This state value function is often either probabilistic as in Monte-Carlo Systems and thus most likely unable to compete with deterministic functions in games like chess; or it is expensive in construction due to feature generation and learning-based selection and weighting mechanisms. In this work we present an alternative method that derives features from the goal conditions stated in the game rules, avoiding thereby the disadvantages of other approaches. The paper structures and generalizes some known approaches, shows new ways of deriving features and demonstrates how to use these features as part of an evaluation function. Experiments demonstrate both a high effectiveness and generality.
Towards a physio-cognitive model of slow-breathing

How may controlled breathing be beneficial, or detrimental to behavior? Computational process models are useful to specify the potential mechanisms that lead to behavioral adaptation during different breathing exercises. We present a physio-cognitive model of slow breathing implemented within a hybrid cognitive architecture, ACT-R/Φ. Comparisons to data from an experiment indicate that the physiological mechanisms are operating in a manner that is consistent with actual human function. The presented computational model provides predictions of ways that controlled breathing interacts with mechanisms of arousal to mediate cognitive behavior. The increasing use of breathing techniques to counteract effects of stressors makes it more important to have a detailed mechanistic account of how these techniques may affect behavior, both in ways that are beneficial and detrimental. This multi-level understanding is useful for adapting to changes in our physical and social environment, not only for performance, but for physical and mental health.
Dynamics-Based Approach for Accurate User Identification and Authentication using Machine Learning Techniques

This paper presents a methodology for improving the security of identification and authentication processes using Keystroke Dynamics (KSD). KSD is considered a behavioral biometric operating as a second level of security along with the login process after inserting user name and password. KSD is mainly about observing the way in which the user types. Firstly, we propose four new time features; these features represent the user’s behavior. Secondly, due to the unavailability of standard dataset, a new behavioral dataset is built. Thirdly, we propose employing KSD on CAPTCHA Code for the identification process. In this research, we applied three different classification techniques namely: J48, Random Forest and Multi-layer Perceptron (MLP), to accurately identify the user behavior (legitimate or illegitimate) and its authority. Random Forest showed the best result for the identification with accuracy (93.13%), however for the authorization process the highest accuracy was obtained using MLP (94.90%).
BOOK TRACKING APPLICATION INANDROID FOR LIBRARY USING GPS

This book tracking application the concept of enhancing real physical world with an extra layer of information. The project is built around the innovative use of GPS (Global Positioning System) to navigate the person in the library. This will enable the library to live on your mobile at any time of day or season. Updated data (collated by librarian after arrival of new books) will provide the raw material for creating layers of graphical and multimedia content which can be accessed and viewed by compatible smart phone.
Organizational and managerial practices for circular economy business models : The case of an Italian SME in the office supply industry

Purpose This paper investigates the organizational and managerial antecedents of circular economy business models with the aim of creating a novel framework which builds on three main dimensions: (i) Value Network, (ii) Customer Value Proposition and Interface, and (iii) Managerial Commitment. Design/methodology/approach The study adopts a single case study methodology with the semi-structured interviews conducted in an Italian SME in office supply industry. Accordingly, the feasibility of our framework tested through the case. Findings The analysis reveals that proposed dimensions are interdependent that reinforces one another. The framework has been found to be useful especially for the companies that are seeking the ways to enhance the degree of circularity and for the companies willing to shift towards circular business models. Managerial commitment as a base of our framework is identified as essential for reaching the intended goals of circular business models. Research limitations/implications One of the limitations of the study which opens up new research opportunities can be summarized as testing the framework within a specific case, yet based on in-depth analysis the proposed framework might be feasible in different settings. Practical implications This study presents a roadmap that can be utilized by managers for governing the business model change or upgrade. Originality/value An interdisciplinary approach that integrates the literatures on circular economy, business models, organizational behavior and social psychology has been pursued to propose a novel theoretical framework.
Image Mosaicing of Neonatal Retinal Images

Image mosaicing is a data fusion technique used for increasing the field of view of an image. Deriving the mosaiced image entails integrating information from multiple images. Image mosaicing permits overcoming the limitations of a camera lens and help create a wide field of view image of a 3D scene and hence has a wide range of applications in various domains including medical imaging. This thesis concerns the task of mosaicing specific to neonatal retinal images for aiding the doctors in the diagnosis of Retinopathy of prematurity (ROP). ROP is a vascular disease that affects low birth-weight, premature, infants. The prognosis of ROP relies on information on the presence of abnormal vessel growth and fibrosis in periphery. Diagnosis is based on a series of images obtained from a camera (such as RetCam), to capture the complete retina. Typically, as many as 20 to 30 images are captured and examined for diagnosis. In this thesis, we present a solution for mosaicing the RetCam images so that a comprehensive and complete view of the entire retina can be obtained in a single image for ROP diagnosis. The task is challenging given that the quality of the images obtained is variable. Furthermore, the presence of large spatial shift across consecutive frames makes them virtually unordered. We propose a novel, hierarchical system for efficiently mosaicing an unordered set of RetCam images. It is a two-stage approach in which the input images are first partitioned into subsets and images in each sub-set are spatially aligned and combined to create intermediate results. Given n images, the number of registrations required to generate a mosaic by conventional approaches to mosaicing is O(n2) whereas it is O(n) for the proposed system. These images are then again spatially aligned and combined to create a final mosaic. An alignment technique for low quality retinal images and a blending method for combining images based on vessel quality is also designed as part of this framework. Individual components of the system are evaluated and compared with other approaches. The overall system was also evaluated on a locally-sourced dataset consisting of neonatal retinal images of 10 infants with ROP. Quantitative results show that there is a substantial increase in the field of view and the vessel extent is also improved in the generated mosaics. The generated mosaics have been validated by the experts to provide sufficient information for the diagnosis of ROP.