Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the rpoB Mutations Causing Rifampin Resistance by Rapid Screening in Mycobacterium Tuberculosis in North-East of Iran

Background and Objectives: The incidence of rifampin-resistant strains of Mycobacterium tuberculosis has attracted more attention than the tuberculosis infection due to laborious treatment and control. Recognizing the Mycobacterium tuberculosis genotypes involving in drug resistance via multiplex PCR, a simple and rapid genotyping method, is an emergency for better treatment and control of tuberculosis. This study was designed to specify the frequency of rifampin-resistant strains of Mycobacterium tuberculosis isolated from patients by multiplex allele-specific Polymerase Chain Reaction assay (MAS-PCR). Methods: In this study, 88 Mycobacterium tuberculosis positive samples were included from Qaem Hospital, Mashhad. MAS-PCR was used to detect the rifampin resistance associated mutations in rpoB gene. Results: Mutations in three codons of rpoB gene causing rifampin resistance were detected in 51 isolates (58.96%). The detected mutations in codons 531, 526, and 516 were 55.68%, 38.63%, and 13.63%, respectively. The simultaneous mutations were detected in 11 isolates (12.50%) in codons 531, 526 and 516, in 21 isolates (23.86%) in codons 531 and 526, and in one isolate (1.13%) in codons 526 and 516. Conclusion: According to the results of this study, the frequency of rifampin-resistant strains of Mycobacterium tuberculosis isolated from Khorasan province patients (North-East of Iran) was high. The developed MAS-PCR assay can be used for rapid detection in clinical diagnostic laboratories in areas with high prevalence of multidrug-resistant Mycobacterium tuberculosis strains. In this respect, MAS-PCR is simple, rapid, and highly sensitive method for drug susceptibility tests for detecting multidrug-resistant Mycobacterium tuberculosis.
Coupling Analysis of Electrocardiosignal and Electroencephalogram Based On Improved Symbolic Transfer Entropy

This paper employs coarse graining extraction based on research of original transfer entropy. Through theoretical and experimental analysis, we find that the results of transfer entropy have different distribution trend under different extraction conditions in the coupling analysis of EEG (electroencephalogram) and ECG (electrocardiogram). We choose the best effect of signal data extraction method and apply it to the later application analysis. Meanwhile, as the traditional symbolic dynamics analysis method may lead to the results from the serious effect of non-stationary time series, this paper proposes improvement on the method of time series symbolization based on research of original symbolic transfer entropy, using dynamic adaptive segmentation method. The experimental results show that the whether waking period or sleeping stage, coupling between EEG and ECG is more significant when using improved symbolic transfer entropy algorithm. It is also better to capture the dynamic information of the signal and the change of complexity of system dynamics, which is more conducive to clinical testing in practical application and has a better effect on the analysis of non-stationary time series.
Clinical outcomes and dosimetric study of hypofractionated Helical TomoTherapy in breast cancer patients

We present a single center’s experience of treatment outcomes and dosimetric parameters for breast cancer patients treated with hypofractionated Helical TomoTherapy (HT). This is a retrospective study of one hundred and thirty-six patients with invasive breast cancer treated between March 2012 and October 2016. Dosimetric parameters and 3-year loco-regional failure free survival (LRFFS) were analyzed. Dose to ipsilateral lung, heart and contralateral breast as well as acute and late toxicities were recorded. The median follow-up time is 45 months (range: 5–83). Two patients had loco-regional failure. The 3-year LRFFS was 99%. Acute grade 1 and 2 skin toxicities occurred in 95% and 1%, respectively. Coverage of the target volumes was achieved with the mean ± standard deviation (SD) of homogeneity and conformity index being 0.1 ± 0.04, and 0.8 ± 0.07, respectively. Dose to ipsilateral lung, contralateral breast, and heart was also within the limited constraints regardless of the complexity of target volumes. Only two percent of patients experienced late grade 2 skin toxicity. No late grade 2 subcutaneous tissue toxicity was found. Nine percent of patients developed late grade 1 lung toxicity. Hypofractionated radiotherapy using Helical TomoTherapy in breast irradiation provides excellent 3-year LRFFS and minimal acute and late toxicities. A careful, longer follow-up of healthy tissue effects to lung, heart, and contralateral breast is warranted.
Dreidimensionale Straßenmodelle für Fahrerassistenzsysteme auf Landstraßen

Ein dreidimensionales Strasenmodell, basierend auf dem Klothoidenmodell, erweitert durch drei Splinefunktionen, ermoglicht die Modellierung des komplexen Strasenverlaufs von Landstrasen. Im Horizontalen konnen enge Krummungswechsel, im Vertikalen der Anstieg und Abfall einer Strase dargestellt werden. Zusatzlich wird die Veranderung der Strasenquerneigung durch eine unabhangige Modellierung des rechten und linken Strasenrandes realisiert. Die Umgebung wird mit einem Stereokamerasystem vermessen.
Railway underpass location affects migration distance in Tibetan antelope (Pantholops hodgsonii)

Wildlife crossings are designed to mitigate barrier effects of transportation infrastructure on wildlife movement. Most efforts in evaluating crossing efficiency focus on counting animal use. However, crossings placed at suboptimal locations may alter animals’ natural movement pattern and decrease population fitness, which cannot be reflected solely by counts of animal use. The long-distance migration of Tibetan antelope (Pantholops hodgsonii) is directly affected by the Qinghai-Tibet Railway (QTR). Using the Wubei wildlife underpass along the QTR, we evaluated how underpass placement affects migration routes and decreases movement efficiency. We calculated the net-squared displacement of each animal to identify migration segments (wintering, calving, and migrating) based on Argos tracking data. We used two corridor modeling methods to identify optimal routes that theoretically require less energy to travel between seasonal habitats. We calculated the distance from actual migration routes recorded by Argos to the modelled optimal routes. We found that antelopes stray farther away from the optimal routes as they approach Wubei, indicating that animals have to deviate from their optimal migration pathway to access the railway underpass. On average, antelopes prolong their migration distance by 86.19 km (SEM = 17.29 km) in order to access the underpass. Our study suggests crossing location can affect animal migrations even if structures facilitate animal crossing. To better conserve long-distance migrations, long-term studies using tracking data which evaluate optimal migration routes are needed. We suggest considering the location and structural characteristics in designing and improving wildlife crossings, which do not only facilitate utilization, but also optimize animal movement processes such as migration.
TEDDI: Tamper Event Detection on Distributed Cyber-Physical Systems

Edge devices , or embedded devices installed along the periphery of a powe r grid SCADA network, pose a significant threat to the grid, as they give at tackers a convenient entry point to access and cause damage to other essential equipment in su bstations and control centers. Grid defenders would like to protect these edge devices from being accessed and tampered with, but they are hindered by the grid defender’s dilemma ; more specifically, the range and nature of tamper events faced by the grid (particularly dist ribu ed events), the prioritization of grid availability, the high costs of improper responses, and the resource constraints of both grid networks and the defenders that run them makes prio r work in the tamper and intrusion protection fields infeasible to apply. In this thesis, we give a detailed description of the grid def ender’s dilemma, and introduce TEDDI (Tamper Event Detection on Distributed Infrastr uc ure), a distributed, sensorbased tamper protection system built to solve this dilemma. TEDDI’s distributed architecture and use of a factor graph fusion algorithm gives grid def enders the power to detect and differentiate between tamper events, and also gives def en rs the flexibility to tailor specific responses for each event. We also propose the TEDDI G eneration Tool, which allows us to capture the defender’s intuition about tamper e v nts, and assists defenders in constructing a custom TEDDI system for their network. To evaluate TEDDI, we collected and constructed twelve diff erent tamper scenarios, and show how TEDDI can detect all of these events and solve the grid defender’s dilemma. In our experiments, TEDDI demonstrated an event detection a ccur cy level of over 99% at both the information and decision point levels, and could pr ocess a 99-node factor graph in under 233μs. We also analyzed the time and resources needed to use TEDDI , and show how it requires less up-front configuration effort than curr ent tamper protection solutions.
Adaptive Array-Gain Spatial Filtering in Magnetoencephalography

This paper analyzes adaptive array-gain spatial filtering as a way of interpreting magnetoencephalography (MEG) data to locate centers of neuronal activity within the brain. When neurons in the brain communicate they generate an electric current which, in turn, generates a magnetic field. MEG can record this magnetic signal when it is generated simultaneously in multiple neurons. The features of MEG include good spatial resolution, very good temporal resolution, and no radiation. Currently, inverse modeling is used in the clinical setting to interpret the MEG signal. Since there is no unique solution to the inverse method, the results rely on the skilled interpretation of a neurologist to evaluate their concordance with the patient’s clinical symptoms. Spatial filtering is a way of locating sources of power within the brain that avoids having to use inverse methods and, therefore, avoids many of the localization errors and artifacts. Spatial filtering accomplishes this by using the relationship between the signals from the individual detectors, in the form of a covariance matrix, along with the known detector sensitivity to magnetic dipoles, known as the lead field. In this study, the head is modeled as a homogonoues conducting sphere and the neuronal activity is modeled as an electrical current dipole. Both patient data and simulated data are analyzed in this study. For the simulated data, dipoles at various positions and orientations are analyzed with and without various levels of Gaussian white noise added in. The results of this study show that adaptive array-gain spatial filtering has the potential to become a standard method in the localization of neuronal activity centers in the brain. With its propensity for reduced error and speed in interpreting the data, spatial filtering may one day help to make MEG a standard procedure in pre-surgical brain mapping, thereby reducing patient morbidity.
Robust Spline Path Following for Redundant Mechanical Systems

Path following controllers make the output of a control system approach and traverse a pre-specified path with no a priori time-parametrization. The first part of the thesis implements a path following controller for a simple class of paths, based on transverse feedback linearization (TFL), which guarantees invariance of the path to be followed. The coordinate and feedback transformation employed allows one to easily design control laws to generate arbitrary desired motions on the path for the closed-loop system. The approach is applied to an uncertain and simplified model of a fully actuated robot manipulator for which none of the dynamic parameters are measured. The controller is made robust to modelling uncertainties using Lyapunov redesign. The experimental results show a substantial improvement when using the robust controller for path following versus standard state feedback. In the second part of the thesis, the class of paths and systems considered are extended. We present a method for path following control design applicable to framed curves generated by spline interpolating waypoints in the workspace of kinematically redundant mechanical systems. The class of admissible paths include self-intersecting curves. Kinematic redundancies of the system are resolved by designing controllers that solve a suitably defined constrained quadratic optimization problem that can be easily tuned by the designer to achieve various desired poses. The class of redundant systems considered include mobile manipulators for a large class of wheeled ground vehicles. The result is a path following controller that simultaneously controls the manipulator and mobile base, without any trajectory planning performed on the mobile base. The approach is experimentally verified using the robust controller developed in the first part of the thesis on a 4-degree-of-freedom (4DOF) redundant manipulator and a mobile manipulator system with a differential drive base.
Network intrusion detection with Naïve Bayes Classification and Self Organizing Maps

In this digital period, internet has turned into an indispensable wellspring of correspondence in just about every calling. With the expanded use of system engineering, its security has developed to be exceptionally discriminating issue as the workstations in distinctive association hold very private data and touchy information. The system used to screen the system security is known as Network detection. Intrusion detection is to get ambushes against a machine structure. It is a discriminating enhancement great to go part and additionally an element extent of examination. In Information Security, Intrusion recognizable proof is the showing of placing exercises that attempt to deal the protection, respectability or availability of a benefit. It accepts an astoundingly key part in waylay area, security check and framework inspect. One of the vital tests to Intrusion Detection is the issue of misjudgement, misdetection and unsuccessful deficiency of steady response to the strike. In the past years, as the second line of boundary after firewall, the Intrusion Detection strategy has got speedy progression. This research work prepares two diverse Machine Learning techniques, both supervised and unsupervised, for Network Intrusion Detection. These techniques are Naive Bayes (supervised learning) and Self Organizing Maps (unsupervised learning). The KDD Cup 99 dataset is utilized for Intrusion Detection Problem. As KDD Cup 99 dataset holds some symbolic attribute and also numeric attributes, two sorts of transformation technique have been utilized for these properties. These are conditional probabilities conversion technique and indicator variables transformation. The two machine learning procedures are prepared on both kind of transformed dataset and afterward their outcomes are looked at with respect to the correctness of intrusion detection.
KINEMATIC AND DYNAMIC 2 D VISUAL SERVOING

In this paper a comparison of kinematic and dynamic image-based (2D) visual servoing is accomplished. This comparison is based on a 2 dof planar robot manipulator constructed at Instituto Superior Técnico, and the simulation results are obtained using a Matlab 6.0 Simulink model under regulator control. For kinematic visual servoing, a control scheme with exponential decay is derived. For dynamic visual servoing, PD and inverse dynamics control were used. Finally, comparison results are presented.
Bag-of-Concepts as a Movie Genome and Representation

Author(s): Zhou, Colin | Advisor(s): Dubnov, Shlomo | Abstract: As online retailers, media providers, and others have learned, the ability to provide quality recommendations for users is a strong profit multiplier: it drives sales, maintains retention, and provides value for users. Video streaming, retail, and video databases particularly have had extensive effort and research devoted to finding effective ways to make representations. One of these is MovieLens.org, a video recommender website that uses a representation called the tag genome to understand movies and make tuned recommendations. The tag genome representation is built from user input on the website, where users can apply tags to movies and rate them in order to provide the information needed to make recommendations. In this work, we draw from research in information retrieval to implement a bag-of-concepts representation for movies and make tuned recommendations in the same manner as MovieLens. Our implementation is fully unsupervised and does not require the user data needed in the implementation of MovieLens while still having similar properties to the tag genome that enable interesting tuned recommendations.
Recognizing and Detecting Errors in Exercises using Kinect Skeleton Data

A novel approach to recognizing and correcting errors in exercise activity based on skeletal joint data obtained from the Kinect 2.0 sensor is presented. Many approaches in the literature for analyzing human motion focus on training a classifier to recognize and/or rank the motions. While effective in some situations, the computational costs of training the models, the unavailability of reference motions and the inability to provide real-time guidance and feedback limit the utility of such approaches for empowering wellness management. A classification technique is used to recognize exercises and a geometric characterization of poses is used to detect errors in the recognized exercises. All the features used are extracted from the time-series data collected from a Microsoft Kinect 2.0 camera. Expert domain knowledge was easily integrated to identify errors in exercise performance. The simplicity and the low computational costs, make this approach useful for providing real-time feedback and guidance to participants, thus improving exercise adherence. Experimental results that demonstrate the viability of the approach are presented. In the future, this approach can be extended to a wider range of exercises and similar techniques can be applied to address related problems in rehabilitation, surveillance and remote user interaction.
Socio-Demographic Factors as a Predictor of the College-Choice Process in International Universities : Some data from the United Arab Emirates

This is an exploratory study that seeks to identify socio-demographic factors that influence students’ choice of college and college majors. The study was based on a survey of freshman students in a premier University based in Dubai, UAE. An extensive review of literature on the college-choice process provided the conceptual framework for the study. The results indicate that family, peer group, the possibility of getting a good job and acquiring job skills are factors that influence the college choice process. The study is among the first of its kind in this region wherein the HE environment is becoming increasingly competitive, and therefore results from this study has practical implications for university and college decision making authorities on the marketing strategies they need to adopt for strategic advantage.
The QoS of MPLS

With the increasement of network traffic and the expansion of network business scope,such as IP Phone,IP Fax,Video Phone and so on,low delay,low jitter and other requirements on data communication are set forth by real time and multimedia service.The Quality of Service(QoS) can hardly be guaranteed and the traditional IP network becomes the bottleneck,which hinders the further development of network.Therefore,IETF has developed two models to achieve the QoS in IP network:Int Serv and Diff Serv,the latter being the tendency of development.At the same time,the MPLS technologies that can well guarantee the QoS come into being.After the basic concepts of the models are introduced,some practical MPLS technologies in securing QoS are discussed.They are the Topology Driven MPLS for routing with traditional router mode,the CR LSP MPLS for creating channel in special service for IP packets,and so on.
Influence of some channeling agents on the release profile of Khaya ivorensis - ibuprofen matrix tablets.

The study was carried out to investigate the effect of channeling agents (sodium chloride or mannitol) on the release profiles of ibuprofen-matrix tablets formulated with a natural gum. Ibuprofen matrix granules were formed by wet granulation technique using khaya gum (15%w/w). Different channeling agents at varying concentrations were incorporated. Magnesium stearate (0.5%w/w) was incorporated into the ibuprofen-matrix granules prior to compression at a compression load of 30 (arbitrary unit on the load scale). Granules were evaluated for micromeritic properties while the formulated tablets were evaluated for tablet crushing strength, friability and invitro dissolution studies. The release data were subject to different release kinetics and their release mechanism was studied. All granules were free flowing with angle of repose ≤310. The densities were not affected by the presence of any of the channeling agents. Generally, the dissolution rate increased with increase in concentration of the channeling agents. The influence was slightly more with sodium chloride compared with mannitol. For instance, the dissolution rate (m∞/t∞) without channeling agents and with channeling agents at same concentration (200mg) were 3.59%h-1 (without channeling agent), 6.04%h-1 (with mannitol as channeling agent) and 7.76%h-1 (with sodium chloride as channeling agent. The mechanism of drug release from these formulations followed anomalous transport, often termed as first-order release since the diffusional release exponent (n) for all the formulations was between 0.45 and 0.89. The presence of channeling agents influenced the release of drugs from the matrix system studied. Therefore; controlled amount as well as appropriate channeling agents can be used to enhance and modulate the release of drugs from such systems studied.
Bayesian Optimization of FEL Performance at LCLS

The LCLS free-electron laser at SLAC is tuned via a huge number of parameters such as energy and magnet settings. Much of this tuning, including quadrupole magnet settings, is typically done by hand by the LCLS operators. In this paper we introduce an automated tuning system using Bayesian optimization, and describe its application to the optimization of noisy objectives such as FEL performance. We demonstrate with preliminary results from our implementation at LCLS that this system can improve both the speed of tuning procedures as well as the quality of the resulting solution.
A System Development Methodology for ERP Systems

An ERP (Enterprise Resource Planning) project is considered highly risky, since it is large, complex, usually unfamiliar to the organization and implemented under a tight timetable. It usually entails process reengineering and many changes. To reduce the risk and improve the probability of project success, an organization can use a structured development approach for such a project, beginning with the selection stage and culminating in the operation stage. There are several conventional structured development approaches. The major ones are the ISDLC model, the prototyping approach and the software package life cycle model. The implementation of an ERP system requires a new methodology that combines components from each of the above approaches. The model suggested in this paper is comprised of four stages: (1) selection; (2) definition; (3) implementation; and (4) operation. Several organizations that have adopted this structured methodology have evidenced a successful ERP implementation