Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Clinical Application of Mesenchymal Stromal Cells: Perspectives and Requirements for Orthopaedic Applications

Mesenchymal stromal cells (MSC) possess a wide spectrum of interacting properties that contribute to their broad therapeutic potential: In preand clinical settings MSC have been demonstrated to reduce tissue damage, to activate the endogenous regenerative potential of tissues and to participate in tissue regeneration (Noort, Feye et al. 2010). Initially, MSC have been described to differentiate into derivates of the mesoderm: bone, adipose and cartilage tissue and were therefore applied to restore damaged tissue (Frohlich, Grayson et al. 2008). Subsequent analyses, however, indicated that the repair process does not only lay in the differentiation potential and plasticity of MSC. As demonstrated in later studies even if only few cells were detectable after MSC transplantation, the therapeutic effect was obvious (Fuchs, Baffour et al. 2001; Shake, Gruber et al. 2002). This could be attributed to paracrine properties with consecutive modification of the tissue microenvironment to decrease inflammatory and immune reactions. MSC are therefore beyond doubt promising candidates for cell therapy in various settings (Horwitz, Prockop et al. 2001; Le Blanc, Rasmusson et al. 2004; Prockop 2009; Pontikoglou, Deschaseaux et al. 2011).
Serviços Microcontrolados em Ambientes Inteligentes

Avancos nas areas de eletronica e de sistemas de automacao predial/residencial tornam viaveis as construcoes dos chamados “Ambientes Inteligentes”, ambientes dotados de sensores, controladores, interfaces homem-maquina e atuadores capazes de interagir e se adaptar a presenca de usuarios, facilitando as operacoes destes sobre o ambiente e otimizando as condicoes operacionais. Neste trabalho e proposta uma arquitetura computacional, a qual permitira a utilizacao de servicos e dispositivos de automacao baseados em microcontroladores.
BigGIS: How big data can shape next-generation GIS

The emergence of the “Big Data” concept is changing the way data are managed and analyzed. Traditional GIS software is limited in dealing with big data challenges including versatile data forms, steaming processing, large scale parallel computing, and dynamic mapping and visualization. Significant improvement is needed, which can result in next generation GIS. This paper gives an overview of recent methods in supporting big data management and analysis in geospatial domain. First, it motivates the necessity to develop and use the bigdata-aware GIS software. By reviewing advanced information technologies and approaches, it can assess what operational system framework and approaches are available and applicable in developing bigdata-enabled next-generation GIS - BigGIS. Key considerations for development of BigGIS are highlighted. The results can help identify critical issues and direct future research agenda for next-generation GIS.
Towards Removing the Concurrency Control Bottleneck

In this paper we describe an implementation of a software transactional memory system that is capable of exploiting an increase in parallelism to improve performance. We demonstrate the improvements in performance via benchmarking our system against a standard STM. For the first time we show that parallelism can be exploited to offset an increase in contention. This provides a system where the concurrency control bottleneck is effectively removed given sufficient parallel resources. © 2012 Newcastle University. Printed and published by Newcastle University, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details SHARP, C. , MORGAN, G. Towards Removing the Concurrency Control Bottleneck [By] C. Sharp, G. Morgan Newcastle upon Tyne: Newcastle University: Computing Science, 2012. (Newcastle University, Computing Science, Technical Report Series, No. CS-TR-1358)
Choice of medication for radial artery vasodilation in patients awaiting coronary artery bypass grafting Koroner arter bypass greftleme ameliyatina alinacak hastalarda radial arter vazodilatasyonu için ilaç seçimi

Background:­ In this study, the vasodilatory effects of calcium antagonists and beta-blockers were investigated. Methods: After obtaining Faculty Ethics Committee approval and written informed consent from the patients, 72 adult patients (50 males, 27 females; mean age 62.9±9.3 years; range 37 to 75 years) who will undergo elective coronary artery bypass grafting (CABG) surgery were included in this study. We evaluated the vasodilatory effect of diltiazem alone (group 1), nifedipine alone (group 2), amlodipine alone (group 3), nebivolol alone (group 4) and nifedipine in combination with metoprolol (group 5). Seventy-seven patients who would undergo isolated CABG using radial artery were prospectively randomized to oral agents four or five days before operation. The dilatation of the lumen diameter and area of the radial artery were evaluated with high-resolution ultrasonography just before and 4-5 days after medical treatment. Results:­Diltiazem alone group (p=0.058) and niphedipine in combination with metoprolol group (p=0.067) did not show a significant increase in the lumen diameter and area after medical treatment compared to the pretreatment values. Statistically significant increases in lumen diameter and area were observed in nifedipine (p=0007), amlodipine (p=0.003) and nebivolol (p=0.047) groups. Conclusion:­ Our results demonstrate that calcium channel antagonists are not equally effective in radial artery vasodilatation. Nifedipine, amlodipine and nebivolol appear to be the most effective agents in reducing radial artery spasm. Nebivolol is a betablocker and also has a potent vasodilatory effect on radial artery vasospasm.
Sequential decomposition of 2D apparent motion fields based on low-rank and sparse approximation

Estimation of camera egomotion and detection/tracking of moving objects in video captured by moving camera are challenging tasks in many applications such as autonomous vehicle control, medical image analysis, video surveillance, compression, stabilization, etc. Camera egomotion and object motion in a scene both induce temporal variation of image intensity, which is represented as a vector field of the so-called optical flow. An egomotional optical-flow field at any moment is composed of a few common linear basis vector fields related to camera's translation and rotation, while the moving objects superpose spatio-temporally local and sparse optical flows. We present an online algorithm of stable principal component pursuit that decomposes frame by frame the apparent motion fields into linearly dependent optical-flow fields and sparse optical flows for a given sequence of images. We experimentally show that the former captures the egomotion, and the latter detects the moving objects.
A data mining experimentation framework to improve six sigma projects

The complexity and dynamics have been increased in the last years in the field of telecom operations. Due to this, many challenges were introduced in this area especially in handling the trouble tickets of the customer proactively before it occurs. Six Sigma is selected by one of the biggest telecom operators in the Middle East to achieve a breakthrough improvement in the quality of operations to achieve better customer experience and better operations efficiency through reducing the repeat rate of trouble tickets coming from the customer. In this paper, we propose an experimentation framework that integrates the Cross Industry Standard Process for Data Mining (CRISP-DM), as one of most widely adopted framework for developing data mining solutions with the phases of Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) project as one of the most used methodologies. The objective is to improve the six sigma projects by introducing data mining techniques into their phases to achieve a breakthrough quality improvement through bringing more insights from the massive data of telecom operations that represents the voice of the customer (VOC) and the voice of machine (VOM). A case study is used to show an implementation approach for the framework proposed in the paper by one of the biggest telecom operators in the Middle East.
Thermal Modeling and Validation of a Real-World Mobile AP

Editor’s note: Thermal issues are plaguing high-performance systems because of their high power consumption, and mobile systems because of their form factor and compactness. Researchers have developed tools and design methodologies to model, predict, and optimize thermal aspects, but it has remained a challenge to validate them against real systems. This paper is valuable by focusing on a commercial mobile application processor and validating the widely used models in the research community with experimental measurements using an IR camera. —Mircea R. Stan.
Effect of the Multiple Intelligences in multiclass predictive model of computer programming course achievement

This paper proposes the measurement of Multiple Intelligences (or MI) value as a co-determiner combine with the traditional academic achievements in predicting student performance in taking a computer programming course. The effectiveness of MI in such a predictive model is tested on three machine learning algorithms: Artificial Neural Network, Support Vector Machine, and the classic Naïve Bayes. Using three different validation schemes: 2, 5, and 10-folded cross validations, the results show that the Mi-inclusive model significantly helps to improve the accuracy of predicting students' performance. These are divided into three class: good, average, and poor achievement.
Traditional Birth Attendants and Maternal Mortality

The paper explored the consequences involved with the practice of traditional birth attendants and maternal mortality. In developing countries, most childbirth occurs at home and is not assisted by skilled attendants. This situation increases the risk of mortality for both mother and child with severe maternal and neonatal health complications. Maternal mortality is high in most African countries, particularly in rural areas where access to formal health care is limited. Physical distance and financial limitations are two major constraints that prevent community members from accessing, using trained attendants and institutional deliveries. Traditional Birth Attendants (TBAs) provide basic health care, support and advice during and after pregnancy/childbirth in rural communities. Traditional birth attendants are predominantly in rural, remote and other medically underserved areas. TBAs may not receive formal education and training in health care provision, and there are no specific professional requisites such as certification or licensing. Due to lack of education with some TBAs, the way they attend to delivery is risky for women and their babies, leading to poor health outcomes and even death. The paper suggests that since TBAs have made great impact in the rural communities, their roles cannot be abolished or overlooked therefore effective measures to train and improve their skills and define their roles is necessary. There should be integration and collaboration with the health sector to supervise and monitor their activities.
COMPUTER AIDED MELANOMA SKIN CANCER DETECTION USING ARTIFICIAL NEURAL NETWORK CLASSIFIER

Skin cancer is the uncontrolled growth of strange skin cells. It occurs when unrepaired DNA damages to skin cells triggers mutations, or genetic defects, that lead the skin cells to multiply readily and form malignant tumors. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. Artificial Neural Network (ANN) is one of the important branches of Artificial Intelligence, which has been accepted as a brand new technology in computer science for image processing. Neural Networks are currently the area of interest in medicine, particularly in the fields of radiology, urology, cardiology, oncology, etc. Neural Network plays a vital role in an exceedingly call network. In this paper, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems, to process the medical images. It has been used to analyse Melanoma parameters Like Asymmetry, Border, Colour, Diameter, (ABCD), etc. which are calculated using MATLAB from skin cancer images intending to developing diagnostic algorithms that might improve triage practices in the emergency department. Using the ABCD rules for the melanoma skin cancer, we use ANN in classification stage with Back Propagation Algorithm. Initially, we train the network with known target values. The network is well trained with 96.9% accuracy, and then the unknown values are tested for the cancer classification. This classification method proves to be more efficient for the skin cancer classification.
Scalability limitations of optical access and metro networks due to the polarization-dependent gain of semiconductor optical amplifiers

This article presents, for the first time, the derivation of approximate analytical formulae for the probability density function and the cumulative density function of the optical signal-to-noise ratio variation in optical local and metropolitan area networks due to the weakly polarization-dependent gain of cascaded semiconductor optical amplifiers. The cumulative density function is used to calculate the outage probability and derive specifications for the maximum allowable value of polarization-dependent gain per semiconductor optical amplifier in order to achieve a given network size.
Gravity-driven Lyα blobs from cold streams into galaxies

We use high-resolution cosmological hydrodynamical adaptive mesh refinement (AMR) simulations to predict the characteristics of Lyα emission from the cold gas streams that fed galaxies in massive haloes at high redshift. The Lyα luminosity in our simulations is powered by the release of gravitational energy as gas flows from the intergalactic medium into the halo potential wells. The ultraviolet UV background contributes only <20 per cent to the gas heating. The Lyα emissivity is due primarily to electron-impact excitation cooling radiation in gas at ∼2 x 10 4 K. We calculate the Lyα emissivities assuming collisional ionization equilibrium at all gas temperatures. The simulated streams are self-shielded against the UV background, so photoionization and recombination contribute negligibly to the Lyα line formation. We produce theoretical maps of the Lyα surface brightnesses, assuming that ∼85 per cent of the Lyα photons are directly observable. We do not consider transfer of the Lyα radiation, nor do we include the possible effects of internal sources of photoionization such as star-forming regions. Dust absorption is expected to obscure a small fraction of the luminosity in the streams. We find that typical haloes of mass M v ∼ 10 12 -10 13 M ⊙ at z ∼ 3 emit as Lyα blobs (LABs) with luminosities 10 43 -10 44 erg s -1 . Most of the Lyα comes from the extended (50-100kpc) narrow, partly clumpy, inflowing, cold streams of (1-5) x 10 4 K that feed the growing galaxies. The predicted LAB morphology is therefore irregular, with dense clumps and elongated extensions. The integrated area contained within surface brightness isophotes of 2 x 10 -18 erg s -1 cm -2 arcsec -2 is ∼2-100 arcsec 2 , consistent with observations. The linewidth is expected to range from 10 2 to more than 10 3 km s -1 with a large variance. The typical Lyα surface brightness profile is ∝ r -1.2 where r is the distance from the halo centre. Our simulated LABs are similar in luminosity, morphology and extent to the observed LABs, with distinct kinematic features. The predicted Lyα luminosity function is consistent with observations, and the predicted areas and linewidths roughly recover the observed scaling relations. This mechanism for producing LABs appears inevitable in many high-z galaxies, though it may work in parallel with other mechanisms. Some of the LABs may thus be regarded as direct detections of the cold streams that drove galaxy evolution at high z.
A counter architecture for reducing hot-carrier induced transistor aging effects

In many cases transistor aging may lead to the failure of a digital system before its designated lifetime requirement. The continuous stressing of transistors during operation affects their operational characteristics, degrading the performance of the circuits. The threshold voltage gradually increases and thus provoking the decrease of the driving ability of the transistors. This leads to a gradual degradation of the operating frequency making the system impossible to meet hard real time requirements after a certain time. New architectures have to be proposed for circuits which are mostly exposed to the aging phenomenon. In this paper, an alternative architecture for the counter unit is proposed which balances the transition activity through the circuit. The number of transitions at critical nodes of the circuit is reduced and thus the rate of performance degradation decreases.
Design of multi-channel electric energy metering and charge-monitoring system

In electric vehicle charging equipment, energy metering is a basic function, and the real-time monitoring for the safety of the charging process is essential. Electric vehicle AC charging pile is a convenient and efficient electric vehicle charging equipment, but the internal module of traditional AC charging pile scatters seriously. The volume of the AC charging pile is so large that is not conducive to equipment installation, commissioning and routine maintenance. STM32F107 is set as the master core, while ADE7816 is set as an important device to measure current, voltage and power and it can detect overcurrent, overvoltage and zero crossing. The system was designed from the three aspects of the principle scheme, the hardware circuit and the software programming respectively, and the prototype has been developed. After tested, the system can achieve desired functions, such as multi-charging port charging at the same time, real-time monitoring of charging process, which provided technical means for the intelligent and lightweight of charging equipment, especially multi-user charging equipment.
Accounting Information Inconsistencies and Their Effects on Insolvency Prediction Models

Many studies have shown that avoiding political costs is an incentive for firms to manipulate accounting information, e.g., McNichols and Wilson (1988); Jones (1991); Kato et al. (2001). The majority of them use discretionary accruals models as proxies to manipulation. This paper introduces a new variable (DIF) that measures data inconsistencies present in financial reports, replacing discretionary accruals in the detection of manipulation. Accounting information was collected from 2,033 Brazilian health maintenance organizations (HMOs). This information was then processed and financial ratios derived from insolvency prediction models and thresholds established by the regulatory agency were taken as attributes to differentiate solvent HMOs from insolvents. During this data processing, inconsistencies were identified and instead of being removed, were used to determine the value of the attribute DIF. Processed data was then analysed using data mining techniques and a series of classifiers were created. The classifiers found have high accuracy in terms of discriminating distressed companies, especially when the DIF variable is used. In addition, the attributes selected and the structure of the classifiers can be supported by traditional models of analysis based on financial ratios. Results are relevant for those who are interested in assessing firms’ insolvency risk, because data inconsistencies may signal firms’ performance, therefore shall not be removed from analysis.
TEACHING PRACTICES AND STUDENT ACHIEVEMENT

Despite a wide consensus among researchers and practitioners that teachers matter to student achievement in schools, the exact determinants of teacher quality remain elusive. This paper follows a recent line of research and examines the impact of different teaching practices on student test scores in the United States. It does so against the background of a series of proposed teaching reforms which call for an increase in the use of “modern” teaching practices at the cost of more “traditional” ones, thus implicitly assuming that the former are better at raising student achievement. Using student survey data from the 2007 wave of the Trends in International Mathematics and Science Study and an estimation strategy which allows me to control for the subjectinvariant part of unobserved student ability, I find evidence which points in the opposite direction. While my traditional-teaching measure has a substantial positive effect on student achievement, the estimated impact of my modernteaching measure is much smaller and statistically insignificant. This result is robust to a series of robustness checks.
Improved load-adaptive control strategy for PMSG based stand-alone wind energy generation system

This paper presents a novel load-adaptive control strategy for stand-alone wind energy generation system using a permanent magnet synchronous generator (PMSG). Since there is no grid supply in a stand-alone system, the output voltage has to be controlled in the constant amplitude and frequency by the load-side PWM converter. To improve the operation performance under the nonlinear load and the unbalanced load, a SVM based predictive current control technique is adopted in the inner-loop current controller to improve the performance of transient response, and a outer-loop voltage controller based on the proportional integral plus resonant (PI+R) technique is used to control the amplitude and frequency of the output voltage. Simulation results demonstrate that, by using the proposed new scheme, the stand-alone wind generation system is capable to provide the excellent output voltage performance under both of the steady-state and dynamic process with the load-adaptive ability.
A Statistical model for the warp matrix in super-resolution video reconstruction

This paper advances in the analysis of the least mean square (LMS) algorithm applied to super-resolution reconstruction (SRR) of an image sequence. SRR of image sequences is highly dependent on the quality of the registration stage. When motion between frames is unknown and has to be estimated, the best available statistical model still requires the numerical estimation of the moments of the registration (warp) matrix. In this work we derive an analytical model for these moments for Gaussian registration errors. The new model allows for different boundary conditions. Monte Carlo simulation results illustrate the accuracy of the new analytical model.
The Molecular Pathogenesis of Diabetic Retinopathy - A Spectrum of Pathology Caused by the Disruption of Inner Blood-Retinal Barrier

Among diverse microvascular complications of diabetes, diabetic retinopathy is a leading cause of adulthood blindness in the United States. According to the report of Eye Diseases Prevalence Research Group, the estimated incidence of diabetic retinopathy reaches up to 3.4% in US general population (Kempen et al., 2004). The incidence of retinopathy is increasing according to the duration of diabetes. In type I diabetes patients with duration of 20 years or more, the prevalence of any diabetic retinopathy reaches 100% (Klein et al., 1984b). Diverse classification criteria was introduced in diabetic retinopathy, but the presence of retinal new vessel (definition: any new vessels arising from retina or optic disc, extending to the inner surface of retina or into the vitreous cavity) is the most frequently used criterion because of its clinical significance. Panretinal photocoagulation is indicated in proliferative diabetic retinopathy (PDR) with high risk characteristics, where there is a dramatically increased risk of severe visual loss within 2 years (26.2%) compared to that of PDR without high risk characteristics (7%) (The Diabetic Retinopathy Study Research Group [ETDRS], 1987).