Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular motor-based models of random intermittent search in dendrites

A key component in the cellular mechanisms underlying learning and memory involves the distribution and delivery of mRNA to synaptic sites in dendrites. A minimal three-state random intermittent search model of motor-driven mRNA transport is developed to explore the question of why motor-driven mRNA are observed moving bidirectionally. The model is analyzed by computing the probability an mRNA is delivered to a synaptic target and the average delivery time (MFPT). It is found that if the branched geometry of the dendrite is ignored, a purely unidirectional transport strategy will result in the smallest MFPT at any given delivery probability. The branched geometry of the dendrite is then incorporated into the model, and it is shown that a phase transition exists for a critical delivery probability where bidirectional strategies improve the corresponding MFPT. To further explore the impact of motor-driven transport behavior on mRNA delivery, the three-state model is extended to include a detailed, biophysical model of a multimotor complex coordinated through a tug-of-war. The model is analyzed to explore how various measurable, physical quantities, such as adenosine triphosphate, can be tuned to optimize cargo delivery.
Ripartire dalle competenze «invisibili» per investire sulla risorsa uomo. Laboratori di rilettura autobiografica per ri-orientarsi tra i saperi dell’esperienza

Starting from «Invisible» Competences to Invest in Human Resources. Workshops on Autobiographical Reading Enabling the Re-orientation of Experiential Knowledge The increasing attention given to non-formal and informal learning by the European Union in training contexts, and the consequent need to recognize and certify this learning, confirms the value of the individual and of his/her personal potential within economic and production mechanisms. In an increasingly more flexible and changing labour market, professional identity cannot vanish at every change, but needs to be able to use previous experience gained in any field to reinvent itself each time in an intelligent, inclusive and sustainable way. The autobiographical reading and writing workshop is proposed as a useful tool to accompany adults in the processes of self-guidance and co-construction of existing competences. If methodologically properly deconstructed and reconstructed, a person’s experiential and emotional past allows qualitative and functional personal and professional self-planning for personal and political-economic implementation. The narrative approach linking words, times and places acts as an intermediary between visible and invisible experiences textualising these in a curriculum vitae grid, and can thereby make a difference in terms of employability and competitiveness. Using biographical-designing methodologies means giving meaning and awareness to every act and every knowledge from the different worlds of an individual’s life.
Energy Efficient Perturb and Observe MPPT Algorithm for PV Application

-Photovoltaic (PV) boards are gadgets that change over daylight into electrical vitality and are thought to be one of the significant methods for delivering spotless and endless sustainable power source. Notwithstanding, these gadgets don't generally normally work at most extreme proficiency because of the nonlinearity of their yield currentvoltage trademark which is influenced by the board temperature and irradiance. Subsequently, an elite greatest power-point following MPPT is the way to keep the PV framework working at the ideal power point which at that point gives most extreme productivity. Numerous MPPT controllers and distinctive sorts of control methods have been considered before. In any case, they don't precisely track the most extreme power. This paper basically considers the MPPT control strategy or calculation i.e, irritate and watch (P&O) system which is broadly utilized because of its minimal effort and simplicity of usage. The proposed strategy has been assessed utilizing MATLAB programming. Keywords--Buck Converter, MPPT, PV Panel, Perturb &Observe Algorithm.
Networked virtual reality

A Virtual Reality or Virtual Environment is a highly interactive simulation of a real or imaginary world. It is one form of a Highly Interactive Three Dimensional User Interface. Now that many single-user applications have been successfully implemented, a hotter topic is Networked Virtual Reality, where multiple virtual environments (multiple users) at di erent geographical locations, share information over a network. This thesis focuses on building an Environment Manager (EM) on top of the Object Modeling Language and the Minimal Reality Toolkit. The purpose of EM is to facilitate the creation of multi-user virtual environments that operate on workstations connected over a network; and to allow one or several users to interact with 3D objects in a virtual world, share information and cooperate with each other. As a new component of the virtual reality software package developed at the University of Alberta's Graphics Laboratory, EM simpli es the task of building a networked virtual world by providing the standard facilities required by a wide range of networked virtual environments.
Prototype SOLAP appliqué sur des champs continus en mode raster. Analyse de hot spots de criminalité.

SOLAP (Spatial OnLine Analytical Processing) is a server which allows decision makers to quickly analyze archived data from a spatial datawarehouse. Until now, most of SOLAP tools only manage spatial data through the vector mode. However, some visualization techniques use interpolation to transform them into continuous fields in raster mode. Crime hotspot mapping is one of them: data are modeled in raster with a KDE (Kernel Density Estimation) algorithm to offer a spatially continuous visualization of crimes. This research aims at combining hotspot mapping and SOLAP. First, we adapt SOLAP to continuous fields with a raster multidimensional data model. Then the raster model is adapted to KDE. The data model is validated with a prototype including London crime data. MOTS-CLES : Estimation de la densite par la methode du noyau, entrepot de donnees, SIG, cartographie criminelle, mode maille.
Improving the Eleventh Grade Students'ability in Writing Simple Cause Effect Sentences Through Flow Chart

This research aimed to improve the ability of the eleventh grade students of SMA Negeri 5 Palu in writing simple cause and effect sentences through flow chart. The research usedtrue experimental research design. The population was the eleventh grade science major students and the sample was two parallel classes consisting 52 students. The data were collected through observation and tests: pre test and post test. The observation was conducted to get a picture of teaching and learning process in the classroom while the pre test and post test were used to find out the improvement of the students’ writing ability before and after the treatment. Based on the result of pre test and post test, the research found that the t-counted was 5.967. By applying 0.05 level of significance and the 27+25-2=50 degree of freedom through interpolation formula, it was found that t-table was 0.01. It showed that t-counted was higher than the t-table. In the other words, flow chart is effective to improve the ability of the eleventh grade students in writing simple cause and effect sentences. Keywords: Improving; Writing Ability; Sentence; Cause effect; Flow Chart.
Coarse-grained distributed optimal power flow

We present an approach to parallelizing optimal power flow (OPF) that is suitable for coarse-grained distributed implementation and is applicable to very large interconnected power systems. We demonstrate the approach on several medium size systems, including IEEE Test Systems and parts of the ERCOT system. Our simulations demonstrate the feasibility of distributed implementation of OPF. Rough estimates are made of parallel efficiencies and speed-ups.
Contextual Handling in Neural Machine Translation: Look behind, ahead and on both sides

A salient feature of Neural Machine Translation (NMT) is the end-to-end nature of training employed, eschewing the need of separate components to model different linguistic phenomena. Rather, an NMT model learns to translate individual sentences from the labeled data itself. However, traditional NMT methods trained on large parallel corpora with a one-to-one sentence mapping make an implicit assumption of sentence independence. This makes it challenging for current NMT systems to model inter-sentential discourse phenomena. While recent research in this direction mainly leverages a single previous source sentence to model discourse, this paper proposes the incorporation of a context window spanning previous as well as next sentences as source-side context and previously generated output as target-side context, using an effective non-recurrent architecture based on self-attention. Experiments show improvement over non-contextual models as well as contextual methods using only previous context.
Aware surfaces : large-scale, surface-based sensing for new modes of data collection, analysis, and human interaction

This thesis describes the design and construction of pressure sensing matrices for capturing human location and activity data from large surfaces in a space such as the floors, walls, tabletops, countertops, and furniture. With the ability to operate either alone or connected to others in an assembly, each sensor module is 0.3m x 2m, contains 512 force sensitive resistors, and has a refresh rate of about 8Hz. Each module was made with conductive inkjet printing and PCB fabrication, creating a low-profile sensing surface with robust signal-collecting circuitry. Several experiments were conducted on an assemblage of three modules to assess parameters such as response time, sensitivity, measurement repeatability, spatial and pressure resolution, and accuracy in analyzing walking data as compared to a camera. Applications that could employ such a system are explored and two visualizations were prototyped that could ambiently provide data and trends to a user. Thesis Supervisor: Kent Larson Title: Principal Research Scientist, Changing Places Group, MIT Media Lab
Old English Character Recognition Using Neural Networks

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning model, Artificial Neural Networks, to perform character recognition based on individual character images cropped directly from the images of the manuscript pages. We propose model dimensionality reduction methods that improve accuracy and computational effectiveness. Our experimental results show that the model we propose outperforms current automatic text recognition techniques. INDEX WORDS: Machine learning, Neural network, Image recognition, Old English 2009 Mathematics Subject Classification: 68U10, 68T10, 62H35 OLD ENGLISH CHARACTER RECOGNITION USING NEURAL NETWORKS by SATTAJIT SUTRADHAR M.S., Jagannath University, Bangladesh, 2011 B.S., Jagannath University, Bangladesh, 2010 A Thesis Submitted to the Graduate Faculty of Georgia Southern University in Partial Fulfillment of the Requirements for the Degree
HER2 Protein Overexpression in Infiltrating Ductal Carcinoma of the Breast Associated with Lymph Node Involvement

The current study compares the rates of overexpression of the HER2 protein in stage II ductal carcinoma of breast in patients with lymph-node involvement. The research material was tissue sections obtained from the primary tumor of 28 patients with lymph-node involvement. The technique used to detect the protein was immunohistochemistry. The overexpression of HER2 protein occurred in 33.3% of lymph node-positive patients. Subsequently, the lymph-node metastases population was divided into two subgroups: one subgroup having involvement of up to three lymph nodes and the second subgroup with the involvement of four or more lymph-nodes. In the first group the level of HER2 overexpression was 7.7% and in the second subgroup the HER2 overexpression was 63.6%. The difference was statistically significant.
Nonlinear Filtering Methods in INS/GPS Alignment

In this paper ， we propose a new method for land-vehicle In-Motion Alignment by integrating Inertial Navigation System (INS) and the transfer Dif-ferential GPS (DGPS). Our aim in this work is to integrate the advantages of these systems and to develop the navigation system that does not re-quire initial attitudes information of the vehicle by applying recursive nonlinear filterling techniques.
Design of a slave robot for laparoscopic, thoracoscopic surgery

Conventional Minimally Invasive Surgery (MIS) generally provides the surgeon with an uncomfortable body posture, limited force feedback and unnatural eye-handcoordination when manipulating camera and instruments through small incisions. A masterslave system with force-feedback is being developed, since such a system can overcome the inconveniences of MIS. This paper is about the design of the slave robot for MIS in the abdomen and chest. By applying design principles on kinematically and statically determined design: hysteresis of position is minimized; and stresses, deformations and dynamical behaviour are highly predictable. This improves the reliability of the robot.
Evolucion de las Comunicaciones Multicast: del nivel de red al nivel de aplicacion

La tecnoloǵıa IP Multicast surgió a finales de la década de los 80 como una solución para las transmisiones de datos desde un servidor hacia un grupo determinado de usuarios. Sin embargo, debido a una serie de razones de ı́ndole diversa, su desarrollo no ha sido el esperado en Internet. Como alternativa a esta tecnoloǵıa ha surgido en los últimos años la tecnoloǵıa ALM (Application Layer Multicast) que traslada la funcionalidad multicast desde el nivel de red al nivel de aplicación, utilizando para ello algoritmos propios de las redes Overlay. En el grupo de Ingenieŕıa Telemática de la UPCT se ha trabajado en los últimos años tanto con la tecnoloǵıa IP Multicast como con ALM. Estos trabajos han llevado al diseño de un protocolo de transporte para IP Multicast y al diseño de una aplicación de compartición de ficheros P2P que utiliza una solución ALM para solucionar algunos de los problemas presentes en este tipo de aplicaciones.
Combining induction control and wake steering for wind farm energy and fatigue loads optimisation

Turbine power and yaw set points can be adjusted across a wind farm to minimise the overall power losses and the additional fatigue loads caused by wake interactions. Detailed modelling is required to understand the complex flows in sufficient detail to allow a realistic practical control design. High-fidelity computational fluid dynamics requires enormous computational resources, so simpler engineering models are needed which capture the most important effects while running fast enough to allow sufficient testing. This paper describes a steady-state optimisation tool which has been extended to optimise all the power reduction set-points and yaw offsets simultaneously for different wind conditions. It also describes a fast time-domain simulation model which captures turbine and wake dynamic effects, so that wind farm controllers of all kinds can be tested in realistic and time-varying conditions. To demonstrate its application for controller testing, the performance of the combined power and yaw controller is tested during changing conditions of wind speed, direction and turbulence derived from measured site data. Finally, the need for validation is discussed, as many uncertainties still need to be resolved in order to obtain sufficient confidence that the potential benefits of such wind farm control schemes can be realised in practice.
A bimodal approach for GPS and IMU integration for land vehicle applications

In this paper, we present a novel idea to integrate low cost inertial measurement unit (IMU) and differential global positioning system (DGPS) for land vehicle applications. Since the usage of high performance inertial navigation systems (INS) is restricted by their high price and the government regulation, low cost IMU is generally used for most land vehicle applications. However, low cost IMU produces large positioning and attitude errors in very short time due to the poor quality of inertial sensor assembly. More specifically, when GPS information is available, a good navigation solution can be obtained by incorporating DGPS data. However, a reliable result cannot be provided for land vehicle navigation when GPS signal is not available. To conquer the limitation, we present a bimodal approach for integrating IMU and DGPS, taking advantage of positioning and orientation data calculated from CCD images based on photogrammetry and stereo-vision techniques. The positioning and orientation data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method that can provide accurate position and attitude information for extended period for nonaided GPS information.
Key-Aggregate Cryptosystem based on Elliptic Curve Cryptography for Data Sharing in Cloud Storage with Result and Analysis

It is important to share data securely, efficiently and flexibly in cloud storage. We describe public-key encryption technique based on elliptic-curve theory which is used to create faster, smaller and more efficient cryptographic keys. This public-key cryptosystem produces constant size cipher texts and user can aggregate any set of secret keys and make them as compact as single and can decrypt any set of cipher texts by using that compact aggregate key but, files outside the set remain confidential. In this cryptosystem it is possible to efficiently assign decryption rights for the set of cipher texts to any users. The secret key holder can release a constant-size aggregate key for set of cipher texts and this compact aggregate key conveniently shared with others with very limited secure storage. In this paper, we study how to create a decryption key more powerful so that it can allows decryption of multiple cipher texts, without increasing key size.
Implementation and evaluation of multiple processes control mechanism for regulating program execution speed

If it is possible to regulate program execution speed, we can control the execution speed arbitrarily, regardless of hardware performance, in order to improve the convenience of service. H. Taniguchi (2000) has previously proposed a mechanism to regulate the program execution speed. The proposed mechanism can control only one process, but it can't control multiple processes. However, since in most cases one service consists of multiple processes, it is necessary to establish a multi-process control mechanism for regulating the program execution speed. There are numerous combinations of processes when multiple processes are controlled. Therefore the execution speeds of all the processes need to be regulated smoothly in all combinations. We show a basic mechanism for regulating the program execution speed and describe the problem and treatment for a multi-process control mechanism. This paper reports the result of our evaluation.
Accurate speed improvement techniques for RC line and tree interconnections in CMOS VLSI

A physical delay model for small-geometry CMOS inverters with RC tree interconnection networks is presented. Through extensive comparisons with SPICE simulation results, it is shown that the maximum relative error in delay-time calculations using the model is within 15% for 1.5- mu m CMOS inverters with RC tree interconnection networks. Moreover, the model has a wide applicable range of circuit and device parameters. An experimental sizing program is constructed for speed improvement of interconnection lines and trees. In this program, given the size of the input logic gate and its driving interconnection resistances, capacitances, and structures, users can choose one of four speed improvement techniques and determine the suitable sizes and/or number of drivers/repeaters for a minimum delay. It is found that the required tapering factor in cascaded drivers is not e (the base of the natural logarithm) but a value in the range 4-8. Moreover, adding a small number of drivers/repeaters with large size is more efficient in reducing the interconnection delay. It is also shown that the technique of optimal-size repeaters with cascaded input drivers can lead to the lowest delay. <<ETX>>
Design of a Low Power and Area Efficient Architecture for the Detection of Audio Biological Signals

: In digital audio recording the audio signals are picked by a microphone or other transducer and converted into a stream of discrete numbers, representing the changes over time in air pressure for audio, then recorded to a storage device. Hence these audio recordings are analyzed to detect the audio biological symptoms, such as cough, sneeze, vomiting, wheezing, belching and so on, which are spectrally analyzed using a discrete wavelet transform (DWT). The DWT will help to find out the signal level of variation and also use simple mathematical metrics, such as energy, quasi-average, and coastline parameters. These parameters are used to find out type of symptomatic patterns to be detected. Furthermore a Mel-frequency cepstrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have similar frequency response and hence occur in common wavelet coefficients. The proposed approach is to detect the symptomatic patterns using acoustic non speech human signals which increases the efficiency of mathematical metrics and in particular reduces the area occupied by the architecture. Thus the aim of the proposed work is to design a low power and area efficient mathematical architecture for the calculation of energy parameter, coastline parameter, quasi-average and Mel Cepstrum based analysis for the detection of different symptomatic patterns in audio biological signals. Existing method uses Binary Common Sub-Expression Elimination (BCSE) technique in the design of multiplier which is used in the architecture of DWT and energy parameter calculation. The proposed work employs Multiple Constant Multiplier (MCM) technique in the design of DWT and energy parameter calculation. The design based on MCM outperforms the design using common sub-expression elimination technique with respect to hardware resources and power consumption thereby making the design a low power and area efficient.