Miracle-dz/UniFAR-FTUs huggingface dataset:
OC 8535 AN OVERVIEW OF RESEARCH ETHICS COMMITTEES OPERATING IN LUSOPHONE AFRICAN COUNTRIES

Background In 2017, a North-South partnership was created, to strengthen Bioethics Committees in African Lusophone African countries (LAC), by joining the forces of National and Institutional Research Ethics Committees (REC) and Universities in Angola, Cape Verde, Mozambique and Portugal. This study is part of an EDCTP2-funded project and aims to describe key RECs operating in LAC, its establishment dates and further characteristics. Methods Document analysis and interviews with REC representatives of five LAC were conducted in April 2018. Legal documents were obtained through official national sources. Results We identified four National Ethics Committees, created between 2000 and 2008 by ministerial or governmental decree; only S. Tomé e Principe does not have an established REC. In Angola, the National REC was created in 2000, and since 2007, seven Institutional Committees were implemented at faculty level. National REC in Cape Verde and Guinee-Bissau (CNES) are unique and were founded in 2007 and 2009 respectively. In Mozambique, National REC (CNBS) dates to 2002, and since 2011, 8 Institutional Committees were formed; they functioned as a network under the umbrella of CNBS. Most National REC have representatives from health professional associations, lawyers, civil society and religious communities and have regular meetings (usually monthly). The number of members ranges between 6 (CNES) and 13 (CNBS). In 2007, around 200 protocols were reviewed by CNBS and 29 by CNES. Most of the National REC members attended training activities in bioethics but at different levels. Conclusion Few publications described REC operating in LAC; this study fills this gap by reporting historical and functional characteristics of RECs in five Lusophone African countries. Additional tools based on quantitative and qualitative approaches are being developed to assess more in-depth REC operational characteristics and to identify their needs in order to target training and capacity building initiatives underlying our project.
The Effects of Local Administration of Aminophylline on Transureteral Lithotripsy

Introduction. Urinary stone is a common cause of urinary tract disease. Stone excretion using ureteroscope is effective in inferior ureter. The aim of this study was to investigate the effects of aminophylline on ureteral spasm during ureteroscopy in acute phase of renal colic. Methods. In this double-blind randomized clinical trial, 120 patients with ureteral stones were enrolled and randomized into two groups. The bladder was drained and then received a 150 mL irrigation solution. Irrigation solution was saline and saline plus 10 mL aminophylline at 250 mg dose for control and case groups, respectively. Ureteroscopy and transureteral lithotripsy (TUL) were performed five minutes after irrigation. Results. The mean duration of TUL was 4.2 ± 2.61 min and 8.4 ± 2.9 min for control and case groups, respectively. The successful rate was 95% and 76.1% in case and control groups, respectively. Further extracorporeal shock wave lithotripsy (SWL) was performed in 5% and 30% for patients in case and control groups, respectively. Conclusion. Aminophylline facilitated ureteroscopy and increased the success rate in the treatment of renal colic using TUL. No significant complications from post-TUL were observed. Using aminophylline carries several advantages such as reducing procedure duration, decreasing the need for ureteral and double-J catheter, and reducing stone migration to the kidney and use of SWL.
Model of Providing Assistive Technologies in Special Education Schools

Most students diagnosed with disabilities in Thai special education schools received assistive technologies, but this did not guarantee the greatest benefits. The purpose of this study was to survey the provision, use and needs of assistive technologies, as well as the perspectives of key informants regarding a model of providing them in special education schools. The participants were selected by the purposive sampling method, and they comprised 120 students with visual, physical, hearing or intellectual disabilities from four special education schools in Chiang Mai, Thailand; and 24 key informants such as parents or caregivers, teachers, school principals and school therapists. The instruments consisted of an assistive technology checklist and a semi-structured interview. Results showed that a category of assistive technologies was provided for students with disabilities, with the highest being “services”, followed by “media” and then “facilities”. Furthermore, mostly students with physical disabilities were provided with assistive technologies, but those with visual disabilities needed it more. Finally, the model of providing assistive technologies was composed of 5 components: Collaboration; Holistic perspective; Independent management of schools; Learning systems and a production manual for users; and Development of an assistive technology center, driven by 3 major sources such as Government and Private organizations, and Schools.
Design and performance of the Shasta distributed shared memory protocol

Shasta supports a shared address space in software across a cluster of computers with physically distributed memory. A unique aspect of Shasta compared to most other software distributed shared memory systems is that shared data can be kept coherent at a fine granularity. Shasta achieves this by inserting inline code that checks the cache state of shared data before each load or store. In addition, Shasta allows the coherence granularity to be varied across different shared data structures in a single application. This approach alleviates potential inefficiences that arise from the fixed large (page-size) granularity of communication typical in most software shared memory systems. This paper focuses on the design and performance of the cache coherence protocol in Shasta. Since Shasta is implemented entirely in software, it provides tremendous flexibility in the design of the cache coherence protocol. We have implemented an efficient cache coherence protocol that incorporates a number of optimizations, some of which are common in hardware shared memory systems. The above protocol is fully functional and runs on a prototype cluster of Alpha systems connected through Digital’s Memory Channel network. To characterize the benefits of the various optimizations, we also present detailed performance results for nine SPLASH-2 applications running on this cluster.
Wavelet‐based regularity analysis reveals recurrent spatiotemporal behavior in resting‐state fMRI

One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large‐scale networks. In resting state fMRI (rs‐fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs‐fMRI signal. Here, we develop a wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs‐fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of “lagged” subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an “objective” estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs‐fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet‐based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs‐fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs‐fMRI as well as other biological signals. Hum Brain Mapp 36:3603–3620, 2015. © 2015 Wiley Periodicals, Inc.
ECG biometric recognition in different physiological conditions using robust normalized QRS complexes

This paper demonstrates subject recognition using electrocardiogram (ECG) signal in different physiological conditions. A total of 30 subjects used in this study were obtained from a non-invasive measurement called the Revitus ECG module. Each subject performed six physiological activities which are walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Unique features were extracted in these different physiological conditions from the same subject using normalized QRS complex technique. One physiological activity acts as the enrolment template while the remaining five activities represent the recognition data. Cross correlation was used to measure the similarity between activities. Later, Multilayer Perceptron classifier was applied to evaluate the distinctiveness between subjects. The results of the experiment show that QRS complexes in different activities from the same subject were strongly correlated to each other by obtaining correlation values of more than 0.9. A classification accuracy of 96.1% when using the proposed normalized method as compared to 93.4% without using the normalized QRS complex proves to distinguish between subjects.
Digital Watermarking Technology: A Review

Digital Watermarking is a technology of embedding watermark with intellectual property rights into images, videos, audios and other multimedia data by a certain algorithm. The basic characteristics of digital watermark is imperceptible, security, reliability, low complexity of watermarking algorithm and security of the hiding place. Digital Watermarking Algorithm is composed of three parts: watermark embedding algorithm, the watermark extraction algorithm and the watermark detection algorithm.Future research of digital watermarking technology will focus on the watermarking algorithm, the study of watermarking theories, the watermark attacks and the evaluation system of watermarking system.
Impossible Differential-Algebraic Cryptanalysis of Serpent

Serpent is an SP Network block cipher submitted to the AES competition and chosen as one of its five finalists. The security of Serpent is widely acknowledged, especially as the best known attack so far is a differential-linear attack on only 11 rounds out of the 32 rounds of the cipher. This paper proposed impossible differential-algebraic cryptanalysis of 11-round Serpent using the key arrangement scheme of Serpent as well as the attack principle of impossible differential analysis, according to the algebraic equation of S-box. The new method analyzed block 11-round Serpent with selected plaintexts and memory space.
A Fast Greedy Sparse Approximation for Image Based on Indexed Dictionary

Orthogonal Matching Pursuit (OMP) is an effective solution to sparse approximation based on redundant dictionary, but its greed nature requires that the algorithm traverse all the atoms in a redundant dictionary every time, which can consume much CPU time. The structure of a dictionary is of paramount importance for MP performance. Different from parametric or structured dictionary, the characteristics of the atoms in learned dictionary is unknown to us and cannot be exploited to speed up. This paper presents a novel method: we first cluster all the atoms in learned dictionary by exploiting the best similar greed nature of MP and set up the index of learned dictionary in advance, and then make matching among only a part of atoms via the index. As a result, it can avoid full search while trying to keep the matching effect. Thus it expedites the execution of the algorithm immensely. Some results show that our method is about twice as fast as ordinary OMP.
Visualization of physical library shelves to facilitate collection management and retrieval

Electronic cataloguing systems are used by libraries to provide search mechanisms for finding books in their collections. These systems provide limited, if any, tools for browsing content electronically in a manner similar to browsing books on physical library shelves. Furthermore, library patrons often struggle to physically locate and retrieve books, even after they have found what they are looking for using library catalogue systems. A number of prototype technologies have been developed in recent years to assist library users with the task of locating books. These systems are, however, rather limited in their functionality, and generally do not provide tools for remote browsing of library shelves. In this paper we introduce Metis, a system designed to allow virtual viewing of collections, and to assist with physical retrieval of books using a range of desktop and mobile computing devices.
Joint spectrum sensing method based on energy and covariance matrix theory

Spectrum sensing fast and accurately is the premise of cognitive radio communication systems. Energy detector (ED) is the most common way, but its performance will experience a significant loss under a small noise fluctuation. In order to improve the speed and performance, we use joint spectrum sensing detection (JSSD). With dual-threshold energy detection for the primary band, it will be zoned into three areas, and the covariance absolute value detection (CAVD) for the confused area. We simulate performance and computational complexity of these algorithms under noise certain or uncertainty. Comparing these results, it show that the JSSD as a blind detection algorithm is better than Energy detector, which has a high detection performance under a small increase in complexity.
Conversational Gambits used by the English Education Program Students of Graduate Program of State University of Padang in Classroom Discussion

This research is a descriptive research which aims at describing conversational gambits used by the English Education Program students of Graduate Program of Padang State University in organizing their utterances and interaction in classroom discussion. Purposeful sampling was used to choose 5 classroom discussions and 13 students who actively gave verbal contribution through the using of conversational gambits. The instruments used in this research were observation, field note, and also interview. The finding showed that the type of conversational gambits frequently used by the students in classroom discussion was linking gambits. The gambits used were frequently functioned as communication control. The research also revealed that the students tended to utter certain conversational gambits; “okay” and “I think” more frequently than others which was caused mainly by their habit in using those gambits in their daily communication.
An evolutionary multiobjective optimization algorithms framework with algorithm adaptive selection

It is well known that the performance of any evolutionary multiobjective optimization (EMO) algorithm over one class of problems is offset by the performance over another class by the “no free lunch” theorem. This means that there is no EMO algorithm can be regards as a panacea. Therefore, we propose an evolutionary multiobjective optimization algorithm with algorithm adaptive selection. It divides the population into several small subpopulations according to their distribution in the objective space. Each subpopulation owns a EMO algorithm, and make the worst agent on specific measures of performance learn from its neighbor best one according to the feedback from the search process. We test the proposed algorithm on nine widely used test instances. Experimental results have shown that the proposed algorithm is very competitive.
Fast sparse matrix-vector multiplication on graphics processing unit for finite element analysis

Finite element analysis involves the solution of linear systems described by large size sparse matrices. Iterative Krylov methods are well suited for such type of problems. These methods require linear algebra operations, including sparse matrix-vector multiplication which can be computationally expensive for large size matrices. In this paper, we present the best way to perform these operations, in double precision, on Graphics Processing Unit (GPU). Several linear algebra libraries are considered and compared to our proper implementation. These libraries and our proper implementation are then integrated within an iterative Krylov method on the GPU. Numerical experiments done on a set of finite element matrices are presented and illustrate the performance, robustness and accuracy of our proper implementation compared to the existing libraries and its suitability for finite element analysis. Dynamic tuning of the gridification, upon the GPU architecture and the finite element matrix characteristics, is finally applied to faster the sparse matrix-vector multiplication operation.
Transcriptional Effects of Estrogen on Neuronal Neurotensin Gene Expression Involve cAMP/Protein Kinase A-Dependent Signaling Mechanisms

Steroid hormones exert dramatic effects on neuronal expression of genes that encode neuropeptides. Expression of the neurotensin/neuromedin (NT/N) gene in preoptic area neurons is dramatically enhanced by estrogen in vivo, even though its promoter lacks palindromic estrogen response elements. We report here that estrogen promotes transcription of this gene by interactions with the cAMP cascade in a neuronal cell line, SK-N-SH, and in a mouse model. In neuroblastoma cells, estrogen increases cAMP and the phosphorylation of the cAMP response element-binding protein in a time frame that precedes induction of NT/N gene transcription. Interference with the cAMP/protein kinase A signal transduction cascade blocks the ability of estrogen to elicit increases in transcription of this gene. Furthermore, in studies performed in vivo using mice deficient in protein kinase A, estrogen fails to induce increases in NT/N mRNA but retains its ability to promote estrogen response element-dependent progesterone receptor gene transcription. These data represent the first report of a nonclassical effect of estrogen on the expression of an endogenous estrogen-regulated neuropeptide gene through cAMP-mediated mechanisms both in a neuroblastoma cell line and in hypothalamic neurons. More importantly, this “cross-talk” may represent a more generalized mechanism by which steroid hormones act through other signal transduction cascades to regulate the expression of other genes in the brain.
Thermal Conductivity of Nanoparticle -Fluid Mixture

Effective thermal conductivity of mixtures of e uids and nanometer-size particles is measured by a steady-state parallel-plate method. The tested e uids contain two types of nanoparticles, Al 2O3 and CuO, dispersed in water, vacuum pump e uid, engine oil, and ethylene glycol. Experimental results show that the thermal conductivities of nanoparticle ‐e uid mixtures are higher than those of the base e uids. Using theoretical models of effective thermal conductivity of a mixture, we have demonstrated that the predicted thermal conductivities of nanoparticle ‐e uid mixtures are much lower than our measured data, indicating the dee ciency in the existing models when used for nanoparticle ‐e uid mixtures. Possible mechanisms contributing to enhancement of the thermal conductivity of the mixtures are discussed. A more comprehensive theory is needed to fully explain the behavior of nanoparticle ‐e uid mixtures. Nomenclature cp = specie c heat k = thermal conductivity L = thickness Pe = Peclet number P q = input power to heater 1 r = radius of particle S = cross-sectional area T = temperature U = velocity of particles relative to that of base e uids ® = ratio of thermal conductivity of particle to that of base liquid ¯ = . ® i 1/=. ® i 2/ ° = shear rate of e ow Ω = density A = volume fraction of particles in e uids Subscripts
Ingested double-stranded RNA and their mobile RNA derivatives have different requirements for gene silencing in C. elegans.

Double-stranded RNA (dsRNA) can cause specific gene silencing upon ingestion in many invertebrates and is being developed as a pesticide to target essential genes in animal pests. Silencing by ingested dsRNA is best understood in the worm C. elegans, where ingested dsRNA is recruited into the RNA interference (RNAi) pathway by the dsRNA-binding protein RDE-4 for eventual gene silencing by Argonaute proteins. Although silencing is thought to rely on both the ingested dsRNA and on additional dsRNA-derived RNAs called mobile RNAs that are transported between cells, the specific forms of RNA that enter different tissues and the proteins they engage to cause silencing are unclear. We found that when RDE-4 was expressed at high levels within a somatic tissue, silencing by ingested dsRNA could occur in rde-4(-) somatic tissues but not in the rde-4(-) germline. Consistent with the spread of dsRNA-derived mobile RNAs between cells, silencing was more efficient in rde-4(-) cells located near rde-4(+) cells. Surprisingly, silencing by mobile RNAs derived from ingested dsRNA could bypass the requirement for a nuclear Argonaute that both ingested dsRNA and mobile RNAs derived from neuronal dsRNA require for silencing the same target gene. Furthermore, silencing by mobile RNAs could bypass inhibition of silencing within a tissue that can be caused by expression from repetitive DNA in that tissue. These results identify possible mechanisms that animals can use to evade RNAi and suggest that mobile RNA derivatives of ingested dsRNA can bypass these resistance mechanisms and cause gene silencing.
Parallel Realization of Cognitive Cells on Film Mammography

High speed detection of breast masses from mammography images at an affordable cost is a problem of practical significance in large volume real-time processing and diagnostic assessments. In this paper, we present a new approach to real-time detection of breast masses by introducing the concept of cognitive cells that has a fully parallel high speed computing architecture realised in a low cost hardware. The prototype system was tested using the Compute Unified Device Architecture (CUDA) that achieved an average speed of 6 ms for processing a single 1024x1024 pixels mammography image. Initial results shows feasibility of using cognitive cells for suspicious breast cancer mass detection in mammograms with superior performances in speed in comparison to other standard methods. We report specificity of 95.25% and the cancer false positives per image as 2.275 for MISC, ASYM, CIRC and SPIC cases, while a relatively lower specificity of 70% and the false positives per image as 2.25 is reported for CALC and ARCH cases of abnormalities.
Computational Study of Graphene Nanoribbon Resonant Tunneling Diodes

The structural effects of different graphene nanoribbon resonant tunneling diodes (GNR-RTDs) are investigated under different temperatures. Although the W-shape structure outperforms the H-shape structure in term of the peak current, the peak-to-valley ratio of the H-shape is higher than the W-shape due to the smaller peak currents of the former. Furthermore, the effects of the channel length and the contact's bandgap of the W-shape GNR RTDs on device performance are studied and their detailed device physics is also provided in this work.