Miracle-dz/UniFAR-FTUs huggingface dataset:
A Noval Approach For Optimization OF PHYSICAL LAYER FOR WIRELESS AND MOBILE COMMUNICATION

In modern era communication is very important. This communication can be divided in two parts (1) Voice and (2) Data. In voice communication real time data transmission is necessary while data can wait for while. But major area of concern is cost and that is the main reason why we cannot have separate lines for both. In any communication transmission of signal should be secure, error free and least affected by attenuation. These all can affect signal during transmission in physical layer where little can be done to prevent this. In recent developments in mobile communication has raised the expectation of users to a new height and as demand arises new advancement by mobile companies are also offering newer and faster technologies. In this series the latest development is advancement towards 5 G mobile network with expected data rate over 1 Gbps . This network is expected to roll out around 2020 but a lot of work is pending like infrastructure improvement, architecture development frequency band allotment etc
Selection of Input Features for Day-Ahead Electric Load Power Forecasting Based on Artificial Neural Network Technique with Novel Framework in Matlab Environment-A Composite Approach

In recent years, the electric load power forecasting (ELPF) has become an essential activity in power systems. As per ELPF literatures, Artificial Neural Network (ANN) technique based models are more accurate than any other. Hence this technique with new framework for day-ahead ELPF which includes features of calendar, weather and lagged load of previous day in time domain is chosen. This research paper is mainly focused to deal with the selection of best suitable weather feature from many weather variables with calendar and lagged load features as constants. The real time data used for this research work were collected from Tata Power Delhi Distribution Utility (TPDDL). The correlation analysis of ELP with respect to each selected feature is performed and presented. Then, suitable framework for development of models is created. The performance criteria for ELPF are chosen and for better understanding, the experimental results are presented with the help of histogram and bar charts. The proposed models produce an average forecast error of 2.6% to 3.3% per hourly samples throughout the year irrespective of seasonal and weather variations. The proposed models are proven for their simplicity, less measurement requirement and easiness in implementation with higher accuracy.
From fully automated observations to a neural network model inference: The Bridge "Fallersleben Gate" in Brunswick, Germany 1999–2006

Since the beginning of June 1999 the Institute of Geodesy and Photogrammetry is monitoring the displacements of a small, but unstable bridge within the city of Brunswick. As basic sensor for automatic and continuous monitoring an automated total station is installed below the bridge, which takes measurements to 180 reflecting targets on the structure three times per day. This paper begins with the outline of the basic concepts for the instrument setup, the longterm control of its stability and possible influencing factors. Then the processing of the time series of raw data is discussed, emphasing the filtering concepts to account for the detection of outliers, due to target corrosion or distortion. Finally the bridge is considered as a closed system and all the available information is used to generate a non-parametric model to describe its behaviour with tools from system analysis.
Efeitos do Uso Crônico do Amprenavir sobre a Prenhez da Rata Albina Effects of Chronic Amprenavir Treatment on Rat Pregnancy

OBJECTIVE: to evaluate the chronic effects of amprenavir on implantations, reabsorptions, fetuses, placentae, and maternal and fetal mortality in the albino rat. METHODS: five groups of EPM-1 Wistar pregnant rats were used: two controls: Contr1 (control of stress) and Contr2 (drug vehicle control), and 3 experimental groups that were treated once a day throughout gestation with 46mg/kg (Exper1), 138mg/kg (Exper2) and 414mg/kg (Exper3) of oral solution of amprenavir. The drug and the vehicle (propyleneglycol) were administered by gavage. The evaluations included maternal body weight gain, number of implantations, reabsorptions, fetuses, placentae and of intrauterine deaths as well as fetal and placental mean weight and major malformations. Fragments of lungs, kidneys, liver and intestines were collected and prepared for histopathological evaluation. RESULTS: Exper3 group tended to show lesser maternal body weight gain during gestation (P = 0.07), but amprenavir did not affect the intrauterine contents. The cytotoxic effect of the drug was observed with regard to the histopathological analyses of pregnant rat viscerae and to the maternal mortality rate: 50% in Exper1 and Exper2 groups, and 70% in Exper3 group. CONCLUSION: amprenavir exerted adverse side effects on maternal lung, gut, kidney and liver, and significantly increased maternal mortality rates in all administered doses and especially at 414 mg/kg.
Towards the Definition and Development of Expertise in the Use of Constraint-based CAD Tools: Examining Practicing Professionals

HARTMAN, NATHAN WILLIAM. Towards the Definition and Development of Expertise in the Use of Constraint-based CAD Tools: Examining Practicing Professionals. (Under the direction of Dr. Theodore J. Branoff) The purpose of this research will be to examine the development of expertise in the use of constraint-based CAD tools. There has been a significant shift towards the use of these tools in engineering and design environments in the last ten years, yet without a comparable shift within the academic environment. Engineering graphics within an academic setting should examine industry-based techniques to improve classroom instruction. Using methods adapted from phenomenological inquiry and cognitive psychology, practicing engineering design professionals were assessed in an effort to gauge their mental models and problem-solving processes to form an initial definition of the domain of constraint-based CAD tools, as well as the experiences that contributed to their development of expertise. Interviews and observations were used to gather data relevant to the experiences of the participants, while a knowledge-mapping task and think-aloud modeling task were used to examine the participants’ modeling strategies used in creating a 3D model and their organization of the concepts surrounding this knowledge domain. The results of this study yielded fifteen constituent themes of expertise based on the data collected from the observations. It also produced seven additional themes related to the personal experiences and conceptions of expertise of each participant obtained by the interview data. These two sets of thematic elements comprised the initial developmental factors of expertise. The think-aloud modeling tasks captured the modeling procedures employed by the participants in creating a 3D model, which looked specifically at their CAD tool usage and how they applied their background knowledge to solve a design modeling problem. Finally, the knowledge-mapping tasks captured each participant’s organization of the major concepts related to the use of constraint-based CAD tools. The conclusions drawn from this study provided an initial look at the development of expertise in the use of constraint-based CAD tools. Recommendations for further research were also provided. Towards the Definition and Development of Expertise in the Use of Constraint-based CAD Tools: Examining Practicing Professionals by Nathan W. Hartman A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Education Math, Science, and Technology Education Technology Education Program Raleigh, North Carolina
Cost Effective Optimal Placement and Sizing of Active Power Filter Using Grey Wolf Optimization in Radial Distribution System

Now-a-days the active power filter (APF) plays significant role for harmonics mitigation. The optimal placement and sizing of APF (OPSAPF) in a radial distribution system (RDS) is a constrained nonlinear problem. OPSAPF using grey wolf optimization (GWO) algorithm along with cost effectiveness is presented in this paper. The GWO is a nature emboldened algorithm. The optimization characteristics exploration and exploitation, are balanced in it. Three inequality constraints (a) total harmonic distortion in voltage (THDV), (b) individual harmonic distortion in voltage (IHDV), and (c) maximum size of APF are considered. The GWO is incorporated with harmonic load flow and implemented on the 33-bus system. The simulation is run for 30 independent runs. Simulation results confirm the efficacy and steadiness of GWO algorithm.
Research and citation impact of publication of the Nuclear Physics Division at Bhabha Atomic Research Centre

The paper analyses the citations to 257 publications published during 2003-2008 by the Nuclear Physics Division at Bhabha Atomic Research Centre, using Web of Science database as the source data. The extent of citations received, in terms of the number of citations per paper, year-wise break up of citations, citation time-lag, domain-wise citations, citing authors, subject analysis of citations, categories of citing documents, citing journals, keyword analysis of citations, citing institutions, and the distribution of citations are determined. During 2003-31st August 2010, Nuclear Physics Division publications received a total of 5627 citations. The average number of citations per year was 703.38 and the average number of citations per publication was 21.89. The highest number of citations received was 1155 in 2007. Citation time-lag was zero for 76 (29.57%) papers, one year for 92 (35.80%) papers and two years for 19 (7.39%) papers. High Energy Physics and Quark Gluon Plasma domain received the highest percentage (75.5%) of citations. The core citing authors were: A. Lebedev (449) followed by D. D'Enterria (396), S. Mioduszewski (392), M.J. Tannenbaum (378), H. Masui (378), R. Averbeck (377), Y. Akiba (377), R. Lacey (375), G. David (374), T.C. Awes (374). The core journals citing Nuclear Physics Division publications were: Physical Review - C (1148 citations), Journal of Physics - G (663 citations), Nuclear Physics - A (618 citations), Physical Review Letters (347 citations), European Physical Journal - C (332 citations), Physics Letters - B (263 citations), Physical Review - D (214 citations), International Journal of Modern Physics - E (169 citations), European Physical Journal - A (121 citations).
Evolutionary Design of Neural Networks

This thesis deals with methods for nding neural network architectures suitable for learning particular problems. We use an evolutionary algorithm with four di erent genetic encoding methods to search for the suitable architectures. We train the neural network weights with a separate neural learning algorithm. We use eight di erent learning problems for benchmarking the encoding methods. Four of the problems are arti cial (XOR, Encoder and two function approximation problems), three are real-world classi cation problems from the Proben1 benchmarking problem set, and one is a bankruptcy classi cation problem studied earlier in one of our projects. Our evaluation criteria are classi cation accuracy and e ciency for using only the relevant variables. The classi cation results are compared also to those for network architectures found by a systematic search.
Quantitative assay of RNA and DNA by the double fluorochrome method: Extraction optimization and pigment algal interference

Les concentrations en ADN et ARN du phytoplancton, en culture ou en milieu naturel marin, sont mesurees par la methode des deux fluorochromes, avec le Hoechst 33258 (HO) et le thiazole orange (TO). L'application de differents traitements aux echantillons a permis de selectionner une procedure optimale d'extraction qui a ete comparee a celle proposee dans trois autres methodes. Ces tests ont montre que l'utilisation de la sonication, suivie d'un traitement avec le Triton X-100, l'heparine et la proteinase K, donne un meilleur rendement d'extraction. Cette methode appliquee a des extraits de cyanobacteries et a quelques echantillons oceaniques donnait des valeurs negatives d'ARN. Ces homogenats sont caracterises par un blanc eleve, aux longueurs d'onde d'excitation et d'emission utilisees dans le dosage avec le TO (500, 540 nm), qui est du a la fluorescence de la phycoerythrine des cyanobacteries. La simple soustraction de ce blanc n'est pas suffisante car sa fluorescence a ete reduite (quenching) par l'absorption du TO. Nous montrons que la prise en compte du quenching du blanc par le TO donne des valeurs positives d'ARN, quel que soit le type d'echantillons. Dans nos conditions de mesure, l'interference duc aux pigments lipophiliques des algues peut etre consideree comme negligeable.
Analyzing Evolutionary Algorithms: The Computer Science Perspective

Evolutionary algorithms is a class of randomized heuristics inspired by natural evolution. They are applied in many different contexts, in particular in optimization, and analysis of such algorithms has seen tremendous advances in recent years. In this book the author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics. He starts with an algorithmic and modular perspective and gives guidelines for the design of evolutionary algorithms. He then places the approach in the broader research context with a chapter on theoretical perspectives. By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step. This main part is followed by a chapter covering practical applications of these methods. The notational and mathematical basics are covered in an appendix, the results presented are derived in detail, and each chapter ends with detailed comments and pointers to further reading. So the book is a useful reference for both graduate students and researchers engaged with the theoretical analysis of such algorithms.
Carotid Artery Stenting and Concomitant Coronary Artery Bypass Grafting or other Cardiac Surgical Procedures

Almost 8% to 14% of patients undergoing coronary artery bypass grafting (CABG) have significant internal carotid artery stenosis requiring treatment [1]. A carotid artery disease, especially when a high-risk plaque is present, represents an important risk factor for stroke after cardiac surgery, in particular after CABG.
Southeast New Mexico shines as state economy slowly mends

of rapid growth compared with the rest of the United States. The so-called Great Recession touched virtually every region, and New Mexico was no exception. But while the U.S. economy has been in recovery mode since the summer of 2009, New Mexico has struggled to keep up the pace. The nation has recovered 77 percent of the 8.7 million jobs lost to the recession. Although New Mexico’s job losses were similar to those of the U.S. in percentage terms, the state’s performance in restoring lost jobs has been very poor in comparison; the state has added back only 27 percent of the 51,700 jobs lost. Data indicate there is a significant difference in economic performance between northern and southern New Mexico, with the southeast leading the recovery. Strong economic growth in southeast New Mexico has been driven mostly by energy. The economic performance of this area was discussed at a recent Federal Reserve Bank of Dallas conference, “The Role of Southeast New Mexico in the Global Economy,” in Carlsbad and Hobbs, N.M. Southeast New Mexico Shines as State Economy Slowly Mends
A pilot study to assess whether high expansion CO2-enriched foam is acceptable for on-farm emergency killing of poultry

This pilot experiment was conducted to ascertain whether CO2-enriched high expansion foam could be an acceptable and efficient alternative in emergency killing of poultry. This method could have wide-ranging applications but with particular emphasis on small (backyard) flocks, free-range sheds or open (naturally-ventilated) housings. The objectives of the study were as follows: 1) to determine whether the injection of foam and being covered with foam leads to fear or panic reactions in birds; 2) to determine the time taken to render birds unconscious and dead and 3) to determine whether any pathological abnormalities are observed post mortem. Six laying hens were individually exposed to increasing levels of CO2 foam with an expansion rate of 300:1. The test box containing individual birds filled with foam within 30 s. During foaming, two out of six birds tried to escape from the test box (1–2 attempts per bird). Apart from displaying greater alertness, birds showed no aversive reactions to the CO2 foam. Twenty-to-thirty seconds after being covered with foam, five of the six birds demonstrated one or two forcable or convulsive movements. Movement patterns and muscle jerks immediately following this convulsive movement led us to believe that birds lost consciousness at this moment and, within approximately three minutes, all birds had ceased to have a heartbeat. Macroscopic post mortem examination of the birds revealed no abnormalities and microscopic examination showed moderate bronchiolar bleeding and a small amount of alveolar bleeding. After assessing behavioural parameters, measurements of heart rate and pathological data, it is our conclusion that CO2 foam has the potential to be an acceptable method of killing poultry. It is advisable for this method to be examined on a larger scale in order to assess the implications of physiological (EEG and ECG) measurements on welfare.
Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks

Computer networks and reinforcement learning algorithms have substantially advanced over the past decade. The Internet is a complex collection of inter-connected networks with a numerous of inter-operable technologies and protocols. Current trend to decouple the network intelligence from the network devices enabled by Software-Defined Networking (SDN) provides a centralized implementation of network intelligence. This offers great computational power and memory to network logic processing units where the network intelligence is implemented. Hence, reinforcement learning algorithms viable options for addressing a variety of computer networking challenges. In this dissertation, we propose two applications of reinforcement learning algorithms in computer networks. We first investigate the applications of reinforcement learning for deflection routing in bufferless networks. Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched (OBS) networks. We present a framework that introduces intelligence to deflection routing (iDef). The iDef framework decouples design of the signaling infrastructure from the underlying learning algorithm. It is implemented in the ns-3 network simulator and is made publicly available. We propose the predictive Q-learning deflection routing (PQDR) algorithm that enables path recovery and reselection, which improves the decision making ability of the node in high load conditions. We also introduce the Node Degree Dependent (NDD) signaling algorithm. The complexity of the algorithm only depends on the degree of the node that is NDD compliant while the complexity of the currently available reinforcement learning-based deflection routing algorithms depends on the size of the network. Therefore, NDD is better suited for larger networks. Simulation results show that NDD-based deflection routing algorithms scale well with the size of the network and outperform the existing algorithms. We also propose a feed-forward neural network (NN) and a feed-forward neural network with episodic updates (ENN). They employ a single hidden layer and update their weights using an associative learning algorithm. Current reinforcement learning-based deflection routing algorithms employ Q-learning, which does not efficiently utilize the received feedback signals. We introduce the NN and ENN decision-making algorithms to address the deficiency of Q-learning. The NN-based deflection routing algorithms achieve better results than Q-learning-based algorithms in networks with low to moderate loads.
An IP of an Advanced Encryption Standard for Altera/spl trade/ devices

This work presents an IP of the Rijndael encryption algorithm, the new Advanced Encryption Standard (AES) approved by the National Institute of Standards and Technology (NIST). The IP uses a VHDL description optimized to Altera devices. This Rijndael implementation runs its symmetric cipher algorithm using a key with 128 bits. This mode is called AES128. Two designs are proposed. The first one is a performance version, using full parallel operation and achieving an 820 Mbps throughput in an APEX device. The second and third designs present two costs/spl times/benefit approaches. The paper presents the Rijndael basic structures, the AES128 architecture and results of throughput and device utilization in Altera devices.
Sprachförderpraxis unter den Bedingungen der Diglossie – Zur «Sprachentrennung» bzw. «Sprachenmischung» als Normalität im Kindergartenalltag der deutschsprachigen Schweiz

Im vorliegenden Beitrag wird auf die pädagogische Qualität sprachlicher Förderpraxis im Feld des Kindergartens unter den curricularen Vorgaben und den vorgegebenen Diglossie-Bedingungen in der deutschsprachigen Schweiz deskriptiv-analytisch eingegangen. Durch unsere ethnographisch angelegte Feldstudie konnten wir aufzeigen, dass die sprachliche Förderung in ihrer praktischen Realisierung sowohl Sprachenmischung als auch Sprachentrennung voraussetzt und zugleich hervorbringt. Wir diskutieren unsere Ergebnisse auf der Basis von neueren soziolinguistisch fundierten pädagogisch-didaktischen Ansätzen, die auf das gesamte linguistische Repertoire mehrsprachiger Individuen (Lehrpersonen und Kinder) fokussieren und somit zur Qualitätsentwicklung des (vor-)schulischen Sprachunterrichts in der deutschsprachigen Schweiz beitragen könnten.
Classification of Phonocardiograms with Convolutional Neural Networks

The diagnosis of heart diseases from heart sounds is a matter of many years. This is the effect of having too many people with heart diseases in the world. Studies on heart sounds are usually based on classification for helping doctors. In other words, these studies are a substructure of clinical decision support systems. In this study, three different heart sound data in the PASCAL Btraining data set such as normal, murmur, and extrasystole are classified. Phonocardiograms which were obtained from heart sounds in the data set were used for classification. Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. In these studies, the obtained results show that the CNN classification gives the better result with 97.9% classification accuracy according to the results of ANN. Thus, CNN emerges as the ideal classification tool for the classification of heart sounds with variable characteristics.
Zu den Zerstörungen in Zincirli während des frühen 7. Jahrhunderts v. Chr.

Les destructions de Zincirli sont au 9eme s. av. J.C. d'ordre plus ou moins religieux et economique et entrainent une rapide reconstruction et un agrandissement des structures (murailles urbaines apres purification par le feu). Au 7eme s. par contre, il semblerait qu'il faille les attribuer a l'avancee des Cimmeriens en Syrie