Miracle-dz/UniFAR-FTUs huggingface dataset:
A Slice-based <inline-formula><tex-math notation="LaTeX">$^{13}$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq1-2849728.gif"/></alternatives></inline-formula>C-detected NMR Spin System Forming and Resonance Assignment Method

Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, <inline-formula><tex-math notation="LaTeX">$^1$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq2-2849728.gif"/></alternatives></inline-formula>H-detected experiments are the dominant NMR technique used due to the high sensitivity of <inline-formula><tex-math notation="LaTeX">$^1$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq3-2849728.gif"/></alternatives></inline-formula>H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of <inline-formula><tex-math notation="LaTeX">$^{13}$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq4-2849728.gif"/></alternatives></inline-formula>C nuclei. Consequently, <inline-formula><tex-math notation="LaTeX">$^{13}$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq5-2849728.gif"/></alternatives></inline-formula>C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for <inline-formula><tex-math notation="LaTeX">$^{13}$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq6-2849728.gif"/></alternatives></inline-formula>C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two <inline-formula><tex-math notation="LaTeX">$^{13}$</tex-math><alternatives><inline-graphic xlink:href="alazmi-ieq7-2849728.gif"/></alternatives></inline-formula>C-detected spectra, CBCACON, and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of ‘Ubiquitin’ and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92 percent correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86 percent and an average precision of 88 percent. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure.
FORTRAN 90

From the Publisher: Meissner, in his book Fortran 90, presents FORTRAN programming in the context of using the language as a tool for solving engineering or scientific problems. Students need to know which FORTRAN language features are more important/useful than others, and Meissner's explanations give students a good overview of key ideas. Meissner motivates his presentation of FORTRAN specifics through examples that show how the FORTRAN feature being discussed is used in the context of mathematical, scientific or engineering related problems (i.e. Fibonnaci sequence, factorials, Newton's Method, trigonometric functions, statistics, isotopes, polar form).
The Role of the Textural Microstructure Co-occurrence Matrices in the Automatic Detection of the Cirrhosis Severity Grades from Ultrasound Images

Cirrhosis is a lethal disease that can also precede liver cancer. The golden standard for diagnosing this affection, the biopsy, is invasive and dangerous. The noninvasive detection of the cirrhosis severity grades is a major challenge in these conditions. The objective of our research is to automatically discover the cirrhosis evolution stages from ultrasound images, through unsupervised classification techniques, based on textural features. In this work, the role of the textural microstructure co-occurrence matrices in the detection of the cirrhosis severity grades was studied. The textural features were provided at the input of unsupervised classification methods. Through specific techniques, the relevant textural features for class separation and their specific values for each severity grade were determined. The method was validated through cluster visualizations and supervised classification, the resulted accuracy being above 90%. The effect of the Principal Component Analysis (PCA) technique upon discovery process of the cirrhosis grades was also studied.
Hyperglycemia and neuronal damage in cerebral ischemia and beyond

The aggravation of cerebral ischemic neuronal damage by preischemic hyperglycemia has been lauded as the proof that lactic acidosis is a major detrimental factor in such damage. This phenomenon has steered clinicians to attenuate blood glucose levels as a means of brain protection against the risk of ischemia during cardiopulmonary bypass surgery and neurosurgical procedures. Researchers who use in vivo models of ischemia have repeatedly confirmed this paradoxical phenomenon where the only energy substrate capable of supporting ATP formation in the absence of oxygen must be avoided. Consequently, lactate, too, acquired a bad reputation among clinicians and researchers alike. The only investigators to defend glucose as a possible neuroprotectant have been those who use in vitro mod
ALGORITHMS FOR TRAFFIC MANAGEMENT IN THE INTELLIGENT TRANSPORT SYSTEMS

Traffic jams interfere with the drivers and cost billions of dollars per year and lead to a substantial increase in fuel consumption. In order to avoid such problems the paper describes the algorithms for traffic management in intelligent transportation system, which collects traffic information in real time and is able to detect and manage congestion on the basis of this information. The results show that the proposed algorithms reduce the average travel time, emissions and fuel consumption. In particular, travel time has decreased by about 23%, the average fuel consumption of 9%, and the average emission of 10%.
A relationship between Korean L2 learners’ stress placement of noun-verb homographs and English proficiency

Joo, Hyunjung. 2016. A relationship between Korean L2 learners’ stress placement of noun-verb homographs and English proficiency. SNU Working Papers in English Linguistics and Language 14, 82-102. For Korean L2 learners, stress plays a decisive role in learning English – a stress-timed language, compared to their L1, Korean – a syllable-timed language. The accurate stress placement is important, particularly, in noun-verb homographs, which are related to the grammatical functions. This paper aims to explore whether English proficiency has a significant effect on Korean L2 learners’ stress placement of noun-verb homographs. A total of ten female participants conducted the stress production experiment where they were required to read sentences including the noun-verb homographs in the sentence-middle and sentence-final. Two prosodic features of stress – pitch and duration were measured in Praat. The result shows that English proficiency is not a crucial factor to the stress placement in noun-verb pairs, but reveals that the location of a word in a sentence influences the stress patterns of English. (Seoul National University)
Enhancing Class IX Students Participants in Group Work in Chemistry

This action research has investigated ways to enhance students’ participation in group work in chemistry. It is observed that Bhutanese students participate minimally in group activities especially in science. Most Bhutanese students shy away from participating or interacting in the classroom. To enhance participation and interaction, the curriculum experts developed the curriculum considering the need of every students in Bhutan in science. Research in Bhutan found out that heterogeneous grouping is an effective way to maximize students’ success and collaborative learning is effective for meaningful learning and in solving problems. Some researchers found that in Bhutan science subjects are seen as difficult for both students and teachers. Through our experience of being science student, we saw that many students do not like to study Chemistry because they believe it is a difficult subject. Aim of this study is to identify effectiveness strategies that teacher can use to enhance students’ participation in group work in Chemistry. This study was carried out with class IX students of Taktse Central School. We collected our data through observation and questionnaire. A tally was used to record their participation in the group such as the frequency volunteering to do presentation and taking initiatives in the group work. The data collection also involved survey questionnaire which consist of open ended questions. Baseline data was collected and analyzed after which intervention strategies, such as allocation of group members and cooperative-learning methods was applied. After two months of the intervention process, post-intervention data was collected, analyzed and compared with the baseline data. The findings from this two sources reveal that students are more open for discussion and participation after the intervention process, and their initiative, contribution and understanding of contents increased. We were impressed by some student, who were usually quiet, shy and unsociable themselves to the teacher, became so close with us toward the end of our academic session.
VR Binoculars: an immersive visualization framework for IoT data streams

Every day we find ourselves moving through a blend of material spaces and immaterial networks. This invisible layer created from the millions of the data streams and network connections that take place around us tends to get denser with the recent development and deployment of the IoT devices in the urban space. In our work we aim to explore how the available technology of Mixed Reality spectrum can be applied to provide us an immerse view of the information that exist within the invisible layer of the “cyberspace” [1]. To this extend, we propose “VR Binoculars”, a digital visualization framework that operates in real time.The available Virtual (VR), Augmented (AR), and Mixed Reality (MR) technology is used as a medium to unveil the information that exist in our surrounding space. Specifically, we situate the user within an environment where the digital data visualizations and the physical space are matched together, providing to the user the ability to interact, orient themselves and navigate naturally.For us the proposed framework it is able to promote a better understanding of the IoT ecosystem, it can justify the use of sensors in the public space, and it can raise awareness about privacy and data sharing. Using different urban environments as test cases (indoor, outdoor) we present our methodology and our first results.
Spark in Action

Summary Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. Fully updated for Spark 2.0. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Big data systems distribute datasets across clusters of machines, making it a challenge to efficiently query, stream, and interpret them. Spark can help. It is a processing system designed specifically for distributed data. It provides easy-to-use interfaces, along with the performance you need for production-quality analytics and machine learning. Spark 2 also adds improved programming APIs, better performance, and countless other upgrades. About the Book Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. You'll get comfortable with the Spark CLI as you work through a few introductory examples. Then, you'll start programming Spark using its core APIs. Along the way, you'll work with structured data using Spark SQL, process near-real-time streaming data, apply machine learning algorithms, and munge graph data using Spark Graph X. For a zero-effort startup, you can download the preconfigured virtual machine ready for you to try the book's code. What's Inside Updated for Spark 2.0 Real-life case studies Spark DevOps with Docker Examples in Scala, and online in Java and Python About the Reader Written for experienced programmers with some background in big data or machine learning. About the Authors Petar Zeevi and Marko Bonai are seasoned developers heavily involved in the Spark community.
High Performance Reconfigurable Computing for Bioinformatics Applications

Compute and data intensive applications have been usually executed in high performance computing platforms. Nevertheless, with the exponential growth of the amount of data produced in some research domains, even these parallel systems are unable to produce results in reasonable time. Therefore, in order to further accelerate these applications, parts of them are migrated to hardware and implemented, for instance, in reconfigurable hardware such as FPGAs. Many bioinformatics applications are good candidates to explore the benefits of high performance reconfigurable computing platforms, since they are often very compute intensive, dealing with enormous amounts of data. This paper presents the research already conducted at UnB in the area of High Performance Reconfigurable Computing for Bioinformatics, discusses open problems and presents perspectives for future joint international research.
Theoretical Framework of Open Virtual Worlds for Professional Development

This paper discusses an attempt for formalization of the decision making process, evaluation and scaling of the components and parameters of Lifelong learning systems and services. Fuzzy logic techniques are used for the formal method representation. The parameterization and rules generation are based on preliminary research of the golden standards and surveys among the users and professionals in the field. The especially designed and implemented system is used for experiments.
MIKROKRMILNIŠKO MERJENJE TEMPERATURE IN PRENOS MERILNIH PODATKOV S PROTOKOLOM SNMP

Diplomsko delo opisuje zasnovo in realizacijo prototipnega sistema za merjenje temperature in njegovo povezavo v omrežje Ethernet. Jedro sistema je Microchipov mikrokrmilnik PIC 18F67J60, na katerem tece aplikacija za merjenje temperature s senzorji DS18x20 ter aplikacija za prenos podatkov na osebni racunalnik z uporabo protokola SNMP. Glavni namen enote je merjenje temperature, avtomatsko zaznavanje prikljucenih senzorjev ter prenos izmerjenih podatkov s pomocjo protokola SNMP na osebni racunalnik, na katerem se podatki shranjujejo in prikazujejo v graficni obliki. Cilj diplomske naloge je bil realizirati cim bolj univerzalen in cenovno ugoden sistem za merjenje temperature ter enostavno nadziranje in shranjevanje izmerjenih podatkov s pomocjo osebnega racunalnika.
Title Sensitivity Analysis of Stochastic Models of Bistable Biochemical Reactions

Sensitivity Analysis (SA) provides techniques which can be used to identify the parameters which have the greatest influence on the results obtained from a model. Classical SA methods apply to deterministic simulations of ODE models. We extend these to stochastic simulations and consider the analysis of models with bifurcation points and bistable behaviour. We consider local, global and screening SA methods applied to multiple runs of Gillespie’s Stochastic Simulation Algorithm (SSA). We present an example of stochastic sensitivity analysis of a real pathway, the MAPK signalling pathway.
Betreuung von Frühgeborenen und Neugeborenen mit Erkrankungen von Schwangerschaft bis Wochenbett

Auch in den Wochen nach einer Frühgeburt sind Eltern mit ihrem Kind vielfach noch unsicher. Deshalb ist es im frühen Wochenbett bedeutsam, die Eltern-Kind-Bindung zu fördern. In Teil 2 der Serie vermittelt die Autorin, wie Sie Familien nach der Frühgeburt und Klinikentlassung adäquat begleiten. Lesen Sie, wie es mit den in Teil 1 vorgestellten Familien weiterging, und finden Sie in den Kommentaren zu den Fallbeispielen Handlungsempfehlungen für die Praxis.
Estudio de las mejoras de aplicar Fog Computing en la distribución de servicios en Cloud Computing

Nelson Rodríguez, María Murazzo, Susana Chávez, Adriana Martín, Diego Medel, Jorge Mercado, Matías Montiveros 1-5,7Departamento e Instituto de Informática F.C. E.F. y N. U.N.S.J. 6Departamento de Matemática Facultad de Ingeniería UNSJ Complejo Islas Malvinas. Cereceto y Meglioli. 5400. Rivadavia. San Juan, 0264 4234129 nelson@iinfo.unsj. edu.ar marite@unsj-cuim.edu.ar schavez@iinfo.unsj. edu.ar arianamartinsj@gmail.com vdiego.unsj@hotmail.com jorgenmp@gmail.com, nicolasmontivero.nw273@gmail.com
DemoSoc Working Paper Paper Number 2006 -17 Government and the Distribution of Skills

Individuals’ life chances in the future will very much depend on how we invest in our children now. An optimal human capital model would combine a high mean with minimal variance of skills. It is wellestablished that early childhood learning is key to adult success. The impact of social origins on child outcomes remains strong, and the new role of women poses additional challenges to our conventional nurturing apporach to child development. This paper focuses on skill development in the early years, examining how we might best combine family inputs and public policy to invest optimally in our future human capital. I emphasize three issues: one, the uneven capacity of parents to invest in children; two, the impact of mothers’ employment on child outcomes; and, three, the potential benefits of early pre-school programmes. I conclude that mothers’ intra-family bargaining power is decisive for family investments and that universal child care is key if our goal is to arrive at a strong mean with minimal variance.
Multispectral and digital elevation model information fusion for classification and change detection in earth observation

A review of the state-of-the-art in land cover classification with satellite imagery of the Earth is provided. Information fusion of different typologies (in terms of processing, operation levels and information sources) for classification and change detection purposes is also surveyed. Three different approaches for automatic unsupervised classification of land cover have been implemented to exploit the fusion of multispectral and digital elevation model information for subsequent change detection processes. In broad terms, the first method is based on feature extraction and clustering, the second makes use of a hierarchical decision tree, and the third one constitutes a hybrid, incorporating further techniques. Different types of features have been extracted, at several levels of the process, and merged also at different fusion levels. The algorithms have been tested on two case scenarios, defined by three datasets. A comprehensive set of evaluation measures has been used for the assessment of the classifications, in the three cases scoring high, and especially with the third method, with completeness and correctness values of 97.1877% and 97.3453%. The potential of subsequently applying post-classification change detection techniques with the obtained results is demonstrated, allowing not only for the detection of changes, but for their identi�cation. Implementation of the algorithms and the software system and graphical interface generated has been undertaken in Matlab v.R2010a and IDL v.6. 2.
Computational Linguistic Distances and Big Data: Optimising the Speech Recognition Systems

Linguistic distance has always been an inter-language issue, but English being a interwoven cluster of rhyming words, homophones, tenses etc., has turned linguistic distance into an intra-language unit to measure the similarity of sounds. In many theoretical and applied areas of computational linguistics i.e., Big Data, researchers operate with a notion of linguistic distance or, conversely, linguistic similarity has become the means to optimise speech recognition systems. The present research paper focuses on the mentioned lines as an attempt to turn the existing systems from delivering good performance to perfect performance, especially in the area of Big Data.
ZAMANLARARASI DIŞ DENGE VE OPTİMİZASYON YAKLAŞIMLARI ÇERÇEVESİNDE TÜRKİYE’DE CARİ AÇIĞIN SÜRDÜRÜLEBİLİRLİĞİ1

Bu calismada, Turkiye'deki cari islemler aciginin surdurulebilirligi; 19922012 donemi verileriyle iki farkli model kullanilarak analiz edilmistir: Birinci modelde, dis âlem gelirleri (ihracat, hizmet gelirleri, transfer odemeleri ve dis borc faiz geliri) ve dis âlem giderleri (ithalat, hizmet giderleri ve dis borc faiz odemeleri) serileri kullanilarak, zamanlararasi dis denge kisiti yaklasimiyla; ikinci modelde toplam yurtici tasarruf ve yatirim serileri kullanilarak, zamanlararasi optimizasyon yaklasimiyla, Johansen es-butunlesme yontemiyle test edilmistir. Analizde “guclu ekonomiye gecis programi” nin etkilerini gormek amaciyla, 1992-2012 donemi, iki alt donem ve tam donem olarak arastirilmistir. Sonuc olarak; Turkiye’de ilgili donemde cari islemler aciginin zayif formda surdurulebilir oldugu tespit edilmistir
An Improvement of LEACH Algorithm Based on TinyOS for Wireless Sensor Network

LEACH protocol is one of the most popular clustering routing protocols in wireless sensor networks. To deal with the unbalanced clustering and energy consumption problems exist of LEACH algorithm, this paper proposes an energy efficient multi-hop routing algorithm. In the clustering stage, the new algorithm calculates the optimal cluster head distance value; adjust the node communication radius to control the size of the cluster, thus forming a reasonable network topology structure. In the stage of data stable transmission, a multi-hop communication mode is adopted between the cluster head and the base station to reduce the energy cost. Based on TinyOS system, LEACH-EEMH is designed and realized with the language nesC. Simulations in TOSSIM platform reveal that, in contrast with LEACH, the new algorithm can balance energy consumption of network effectively and prolong the network life.