Miracle-dz/UniFAR-FTUs huggingface dataset:
A method for PAPR reduction in MSE-OFDM systems

High peak to average power ratio (PAPR) of the transmitted signal is a major drawback of multicarrier transmission such as orthogonal frequency division multiplexing (OFDM). This work considers the problem of PAPR reduction in a multi-symbol encapsulated OFDM (MSE-OFDM) system. This paper employs the discrete cosine transform (DCT) to the data sequence. Not only can this method have the same bandwidth efficiency as that of the CP-reduced MSE-OFDM system, but also reduce the PAPR in the MSE-OFDM. Simulation shows that the proposed scheme can significantly reduce the PAPR in MSE OFDM system without increasing the symbol error rate.
Development of Hematopoietic Stem Cell Based Gene Therapy for HIV-1 Infection: Considerations for Proof of Concept Studies and Translation to Standard Medical Practice

Over the past 15 years we have been investigating an alternative approach to treating HIV-1/AIDS, based on the creation of a disease-resistant immune system through transplantation of autologous, gene-modified (HIV-1-resistant) hematopoietic stem and progenitor cells (GM-HSPC). We propose that the expression of selected RNA-based HIV-1 inhibitors in the CD4+ cells derived from GM-HSPC will protect them from HIV-1 infection and results in a sufficient immune repertoire to control HIV-1 viremia resulting in a functional cure for HIV-1/AIDS. Additionally, it is possible that the subset of protected T cells will also be able to facilitate the immune-based elimination of latently infected cells if they can be activated to express viral antigens. Thus, a single dose of disease resistant GM-HSPC could provide an effective treatment for HIV-1+ patients who require (or desire) an alternative to lifelong antiretroviral chemotherapy. We describe herein the results from several pilot clinical studies in HIV-1 patients and our strategies to develop second generation vectors and clinical strategies for HIV-1+ patients with malignancy who require ablative chemotherapy as part of treatment and others without malignancy. The important issues related to stem cell source, patient selection, conditioning regimen and post-infusion correlative studies become increasingly complex and are discussed herein.
On rate of convergence of Monte Carlo approximations of stochastic programs

In this paper we discuss Monte Carlo simulation based approximations of a stochastic programming problem. We show that if the corresponding random functions are convex piecewise smooth and the distribution is discrete, then (under mild additional assumptions) an optimal solution of the approximating problem provides an exact optimal solution of the true problem for sufficiently large sample size with probability one. Moreover, by using theory of Large Deviations, we show that the probability of such an event approaches one exponentially fast with increase of the sample size. In particular, this happens in the case of two stage stochastic programming with recourse if the corresponding distributions are discrete. This indicates that, in such cases, Monte Carlo simulation algorithms could be very efficient.
Nanomaterials: A challenge for toxicologists

The new and unique applications offered by nanotechnology in diverse areas have made it so popular that it is being applied today in almost all aspects of daily life. Although the small size and subsequent larger surface area of nanoparticles (NPs) endow them with some highly useful and specific properties, it also renders them more active leading to unexpected and unanticipated consequences on interaction with biological systems. The concern over the probable adverse effects of nanomaterials on living systems has given rise to ‘nanotoxicology’. However, nanotoxicology has lagged far behind nanotechnology due to a number of experimental challenges and issues faced in designing studies involving toxicological assessment of nanomaterials. This review, therefore addresses some of the issues pertaining to nanomaterial toxicology with respect to nanomaterial characterization, agglomeration, dose metric and surface coating.
Strategies for safety in human robot interaction

This paper presents a hierarchical path planning and control strategy for ensuring safety during a human-robot interaction. At the planning stage, a two-step process is used where first the danger of the interaction is minimized, followed by a goal seeking optimization. This approach reduces the likelihood of encountering local minima due to conflicts between reducing danger and a demanded interaction task. At the control stage, the human intent signal is evaluated at every step to ensure safe operation of the robot. In initial simulation work, the controller drives the robot away from the planned interaction if danger is identified, and then allows the planned interaction to resume once the danger has passed.
PHENOTYPIC AND MOLECULAR IDENTIFICATION OF YEASTS ISOLATED FROM CULTURED TILAPIA (Oreochromis niloticus) IN KAFRELSHEIKH PROVINCE, EGYPT

The present study aimed to identify yeasts by phenotypic, biochemical and genotypic methods. Three hundred live Oreochromis niloticus samples were collected from different locations at Kafrelsheikh Governorate, Egypt and during different seasons. Yeast isolates were identified according to their phenotypical characterization in combination with polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) method. Two hundred and twenty-six yeast isolates from eighty diseased and two hundred and twenty apparently healthy O. niloticus samples. Isolated yeasts were Rhodotorula spp. (18.14%), Candida albicans (19.47%), Candida parapsilosis (16.37%), Candida guilliermondii (14.6%), Candida inconspicua (8.85%), Trichosporon asahii (17.25%), Geotrichum spp. (2.66%) and unidentified Ascus forming yeasts (2.66%). Yeasts most frequently isolated from gills and skin. Phenotypic methods are valuable in identification of yeasts into genera but these methods take more time. Molecular methods through the amplification of (internal transcribed spacer) ITS1-5.8S-ITS2 regions of fungal rRNA, followed by RFLP-PCR using Msp1 restriction enzyme allowed simple, rapid, cost-effective, sensetive and accurate identification of the phenotypically identified yeasts. Key words : Oreochromis niloticus ; yeast; PCR; Msp1; PCR-RFLP
Partial Data Ear Recognition From One Sample per Person

The relatively stable structure of the human ear makes it suitable for identification. The significance of ear recognition in human authentication has become prominent in recent years. A number of ear recognition systems and methods have achieved good performance under limited conditions in the laboratory. In real-world applications, however, such as passport identification and law enforcement, where usually only one sample per person (OSPP) is registered in the gallery, most of the existing ear recognition methods are paralyzed by partial data (e.g., pose variations and occlusion). To address such problems, we propose a weighted multikeypoint descriptor sparse representation-based classification method to use local features of ear images. By adding adaptive weights to all the keypoints on a query image, the intraclass variations are reduced. Besides, the interclass variations of the gallery samples are enlarged by purifying the multikeypoint dictionary. Experiments are carried out on two benchmark databases, i.e., the Indian Institute of Technology Delhi ear database and the University of Science and Technology Beijing ear image database III, to demonstrate the feasibility and effectiveness of the proposed method in dealing with partial data problems in ear recognition under the premise of OSPP in the gallery. The proposed method has achieved state-of-the-art recognition performance especially when the ear images are affected by pose variations and random occlusion.
Realtime Online Daily Living Activity Recognition Using Head-Mounted Display

Human activity recognition is one of the popular research fields. The results of this study can be applied to many other fields such as the military, commercialism, and health. With the advent of the wearable head mounted display device mainly like google glass raises the possibility of this research. In this study tries to identify everyday activities are often called the ambient activity. Development of the system is done online using a smartphone and a head mounted display. The system produces an accuracy above 90%, which can be concluded that the system was able to recognize the activities with great accuracy.
Second-Look Laparotomy in Stage I Ovarian Cancer Following Comprehensive Surgical Staging

Objective: To evaluate the role of second-look laparotomy in patients with comprehensively staged stage I epithelial ovarian cancer. Methods: We reviewed the medical records and obtained long-term follow-up on 54 stage I patients who had second-look laparotomies following complete surgical staging and chemotherapy. Results: The distribution by stage was as follows: stage IA 18 (33%), stage IB two (4%), and stage IC 34 (63%). Eighteen patients (33%) had endometrioid tumors, 12 (22%) clear cell, 13 (24%) mucinous, eight (15%) serous, and three (6%) undifferentiated. Forty-four patients (82%) had grade 2 or 3 tumors. Thirty-eight (70%) were treated with platinum-based regimens and 16 (30%) received non-platinum regimens. At second-look laparotomy, tumor was identified in three women (5.5%). Stage, cell type, and grade did not predict the second-look laparotomy result, although no patient with a grade 1 tumor had a positive second-look laparotomy. With a mean follow-up of 48 months from second-look laparotomy, 11 women (22%) have had recurrences following negative second looks. Tumor grade was a strong predictor of recurrence following negative secondlook laparotomy (P<.0001), with the risk of recurrence being 0% for grades 1 and 2 and 52% for grade 3. Substage, cell type, and chemotherapy type and duration did not predict recurrence. Conclusions: The likelihood of a positive second-look laparotomy is about 5% in well-staged stage I ovarian cancer. Patients with stage I, grade 3 tumors have a risk of recurrence of approximately 50% following negative second-look laparotomy.
Route travel time estimation using low-frequency floating car data

The paper develops a non-parametric method for route travel time estimation using low-frequency floating car data (FCD). While most previous work has focused on link travel time estimation, the method uses FCD observations directly for estimating the travel time distribution on a defined route. A list of potential biases associated with FCD is presented and discussed. For each source of bias, a correction method for the observations is proposed. The estimation method is implemented using FCD data from taxis in Stockholm, Sweden. Estimates are compared to observed travel times for two routes equipped with automatic number plate recognition (ANPR) cameras. The mean travel time estimates incorporating all bias corrections perform equally well or better than the link-based approach in terms of RMSE, and estimated percentiles show a good match to ANPR.
Prediction of autism spectrum disorder based on imbalanced resting-state fMRI data using clustering oversampling

Recently resting-state functional magnetic resonance imaging (R-fMRI) has been applied as a powerful tool to explore potential biomarkers of autism spectrum disorder (ASD). However, in clinical data, the number of ASD patients is significantly less than that of typical development (TD) subjects, which causes the production of imbalanced data. When the imbalanced data are used to predict ASD, the prediction results are not satisfactory. To improve the ASD prediction performance of imbalanced data, this paper adopts the clustering oversampling method to enhance the representation for minority class (ASD), expecting to obtain the balanced data distribution. For the imbalanced data after feature selection, the clustering algorithm is used to form a few clusters in the ASD group and in the TD group, respectively, and then new samples for each cluster are generated by synthetic minority oversampling technique (SMOTE) to make the imbalanced data convert into the balanced data. Finally, we construct the linear support vector machine (SVM) classification model for ASD prediction. The prediction accuracy of multi-center imbalanced R-fMRI data increased from 59.70% to 66.62% using hierarchical clustering oversampling. The results of experiment show that the clustering oversampling method can effectively improve the prediction performance of imbalanced R-fMRI data.
Factors influencing e-government maturity in transition economies and developing countries: a longitudinal perspective

This study examines the influences of relevant environmental factors on E-government (E-gov) maturity in transition economies and developing countries (TEDC). Countries from Eastern Europe, Sub-Saharan Africa, Latin American and South Asia were selected for the study. Prior research has investigated E-gov growth, development, and diffusion across both the developed and developing worlds. While such a focus is useful for comparative analyses at a global level, it is however argued that more useful information will emerge to enrich insight when research efforts particularly focus attention on issues in emerging parts of the world. Very few researchers have studied the factors influencing E-gov maturity in TEDC and with the approach employed in this present research. Using relevant theoretical frameworks, this research identified and examined the impact of 9 environmental factors of socio, political, economic, and technological dimensions on E-gov maturity in TEDC. A 5-year panel data consisting of 320 observations or data points was used in conjunction with the ordinary least squares (OLS) technique. This research also provided analyses for each of the selected sub-regions to enhance insight. Overall, the results showed that the availability of quality human resource, technological infrastructure, innovative capacity, wealth, rule of law, and transparency levels are important factors that positively impact E-gov maturity in TEDC. The implications of the study's findings for research and policy making are discussed. Future research avenues are also highlighted.
The influence of drug interval on the effect of methotrexate and fluorouracil in the treatment of advanced colorectal cancer.

The importance of the interval between methotrexate (MTX) and fluorouracil (5-FU) was studied in 168 patients with previously untreated, measurable, advanced colorectal cancer. They were randomized to receive MTX 200 mg/m2, followed by 5-FU 600 mg/m2 either 24 hours (arm A) or 1 hour (arm B) after MTX. All patients received leucovorin (LV) 24 hours after MTX, 10 mg/m2 orally every 6 hours for six doses. The regimen was repeated every 2 weeks, with 5-FU escalation as tolerated. Arm A was significantly better than arm B with respect to overall response rate (29% v 14.5%, P = .026), time to progression (TTP; median, 9.9 months v 5.9 months, P = .009), and survival (median, 15.3 months v 11.4 months, P = .003). Significant differences between arms were not found in response rate, median TTP, or median survival for the subgroup of patients with rectal primaries who comprised 20% of the patients in each arm. Significant factors prognostic for survival were performance status and number of metastases, as well as treatment. Age did not influence survival. Toxicity was similar in both arms and was primarily gastrointestinal. More mucositis was seen in arm A. There were four toxic deaths secondary to neutropenia and infection (one from arm A and three from arm B) and three other deaths (two from arm A and one from arm B) that were possibly drug-related. The combination of MTX with LV rescue and 5-FU is an active regimen in advanced colorectal cancer; its efficacy is increased in colon, but not rectal cancer, when the interval between MTX and 5-FU is long (24 hours) rather than short (1 hour).
Coordinated lateral and longitudinal vehicle dynamics control of a scale RC vehicle

This paper addresses the design, implementation and validation of a coordinated lateral and longitudinal dynamics control for a racing four-wheel driven RC vehicle. The coordination is achieved hierarchically: a wheel slip control loop stabilizes the fastest longitudinal dynamics, so that the outer lateral dynamics control can rely on non-saturated tires. Designing a wheel slip controller on a scale model requires the estimation of the wheel slip using a reduced set of sensors. The issue of estimating the wheel slip on such a small vehicle is addressed using a complementary filter. The presence of the inner wheel slip control enables the use of a simple gain-scheduled yaw-rate controller to stabilize the yaw rate dynamics and make the yaw rate gain velocity independent. The combined longitudinal and lateral controller is experimentally tested, using both repeatable tests with precomputed inputs and human-in-the-loop tests. The experimental results indicate that the controller is able to extend the operating envelope of the vehicle yielding an easier to operate vehicle.
Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Early detection of faults in electrical machines, particularly in induction motors, has become necessary and critical in reducing costs by avoiding unexpected and unnecessary maintenance and outages in industrial applications. Additionally, most of these faults are due to problems in bearings. Thus, in this paper, experimental bearing fault detection of a three-phase induction motor is performed by analyzing the squared envelope spectrum of the stator current. Spectral kurtosis-based algorithms, namely, the fast kurtogram and the wavelet kurtogram, are also applied to improve the envelope analysis. Experimental tests are performed, considering outer bearing faults at different stages, and the results are promising.
A shape based approach for biometrical analyses

The performed approach describes the shape of image objects: human palms and cochlea. This approach has the advance to achieve results that are invariant with respect of arbitrary compositions of graphical transformations and point of view change of the images processed. The approach includes an original objects image shape description in the type of multi-sized attribute stored in image data base and a technique to find and determine in the database an image that is equal in shape to the target image -- query.
Host heterogeneity dominates West Nile virus transmission

Heterogeneity in host populations and communities can have large effects on the transmission and control of a pathogen. In extreme cases, a few individuals give rise to the majority of secondary infections, which have been termed super spreading events. Here, we show that transmission of West Nile virus (WNV) is dominated by extreme heterogeneity in the host community, resulting in highly inflated reproductive ratios. A single relatively uncommon avian species, American robin (Turdus migratorius), appeared to be responsible for the majority of WNV-infectious mosquitoes and acted as the species equivalent of a super spreader for this multi-host pathogen. Crows were also highly preferred by mosquitoes at some sites, while house sparrows were significantly avoided. Nonetheless, due to their relative rarity, corvids (crows and jays) were relatively unimportant in WNV amplification. These results challenge current beliefs about the role of certain avian species in WNV amplification and demonstrate the importance of determining contact rates between vectors and host species to understand pathogen transmission dynamics.
Characterizing the Impact of Partially Overlapped Channel on the Performance of Wireless Networks

Recent research results show that the use of partially overlapped channel (POC) can significantly improve network capacity. In this paper we present an analytical model to estimate the capacity improvement added by the use of POC in wireless networks. We mathematically characterize the effect of node density, node distribution, network topology and traffic pattern on the performance of POC based approach. We conclude that POC based approach outperforms traditional non-overlapping multichannel approach in most scenarios except for extremely dense networks where no interference is tolerated at all. Our results indicate that POC based approaches have more advantage in relatively denser network with a heavier traffic load. These results provide us the theoretical incentive to use POCs in crowded wireless environments. We validate the results with simulations and experiments.
Antimicrobial susceptibility and virulence-associated genes in Campylobacter isolates from milk and wastewater in Hatay, Turkey

ABSTRACT: Campylobacter is one of the most common causes of bacterial foodborne diseases throughout the world. This study was conducted to determine the prevalence, antimicrobial resistance and virulence of Campylobacter isolates of raw cow’s milk and cattle slaughterhouse wastewater samples in Hatay, Turkey. A total of 114 raw milk and 78 wastewater samples were analyzed for the identification of C. jejuni, C. coli, and C. lari by multiplex PCR. The overall prevalence of Campylobacter was found to be 7.2%, of these isolates, 85.7% were identified as C. jejuni and 14.2% as C. coli, but C. lari was not detected in the study. The cdtA and cadF genes were present in 66.6% and 41.6% of C. jejuni isolates tested, respectively, but wlaN gene was not found in any of the isolates. Results of antimicrobial resistance analysis showed that 71.4% of the isolates were resistant to erythromycin, 64.2% to tetracycline, and 57.1% to ciprofloxacin. Overall, 8 of 14 Campylobacter isolates (57.1%) showed a multidrug resistance.
Does speech enhancement work with end-to-end ASR objectives?: Experimental analysis of multichannel end-to-end ASR

Recently we proposed a novel multichannel end-to-end speech recognition architecture that integrates the components of multichannel speech enhancement and speech recognition into a single neural-network-based architecture and demonstrated its fundamental utility for automatic speech recognition (ASR). However, the behavior of the proposed integrated system remains insufficiently clarified. An open question is whether the speech enhancement component really gains speech enhancement (noise suppression) ability, because it is optimized based on end-to-end ASR objectives instead of speech enhancement objectives. In this paper, we solve this question by conducting systematic evaluation experiments using the CHiME-4 corpus. We first show that the integrated end-to-end architecture successfully obtains adequate speech enhancement ability that is superior to that of a conventional alternative (a delay-and-sum beamformer) by observing two signal-level measures: the signal-todistortion ratio and the perceptual evaluation of speech quality. Our findings suggest that to further increase the performances of an integrated system, we must boost the power of the latter-stage speech recognition component. However, an insufficient amount of multichannel noisy speech data is available. Based on these situations, we next investigate the effect of using a large amount of single-channel clean speech data, e.g., the WSJ corpus, for additional training of the speech recognition component. We also show that our approach with clean speech significantly improves the total performance of multichannel end-to-end architecture in the multichannel noisy ASR tasks.